Intel® Open Volume Kernel Library

This is release v2.0.1 of Intel® Open VKL. For changes and new features see the changelog. Visit http://www.openvkl.org for more information.

Overview

Intel® Open Volume Kernel Library (Intel® Open VKL) is a collection of high-performance volume computation kernels, developed at Intel. The target users of Open VKL are graphics application engineers who want to improve the performance of their volume rendering applications by leveraging Open VKL’s performance-optimized kernels, which include volume traversal and sampling functionality for a variety of volumetric data formats. Open VKL supports x86 CPUs under Linux, macOS, and Windows; ARM CPUs on macOS; as well as Intel® GPUs under Linux and Windows (currently in beta).

Open VKL contains kernels optimized for the latest x86 processors with support for SSE, AVX, AVX2, and AVX-512 instructions, and for ARM processors with support for NEON instructions. Open VKL supports Intel GPUs based on the Xe HPG microarchitecture (Intel® Arc™ GPU) under Linux and Windows and Xe HPC microarchitecture (Intel® Data Center GPU Flex Series and Intel® Data Center GPU Max Series) under Linux. Intel GPU support leverages the SYCL open standard programming language; SYCL allows one to write C++ code that can be run on various devices, such as CPUs and GPUs. Open VKL is part of the Intel® oneAPI Rendering Toolkit and is released under the permissive Apache 2.0 license.

Open VKL provides a C-based API on CPU and GPU, and also supports applications written with the Intel® Implicit SPMD Program Compiler (Intel® ISPC) for CPU by also providing an ISPC interface to the core volume algorithms. This makes it possible to write a renderer in ISPC that automatically vectorizes and leverages SSE, AVX, AVX2, AVX-512, and NEON instructions. ISPC also supports runtime code selection, thus ISPC will select the best code path for your application.

In addition to the volume kernels, Open VKL provides tutorials and example renderers to demonstrate how to best use the Open VKL API.

Version History

Open VKL 2.0.1

Removed ISPC runtime dependency and level zero loader requirement
Add DEPENDENTLOADFLAG linker parameter for Windows binaries, restricting DLL loading behavior
Superbuild updates to latest versions of dependencies

Open VKL 2.0.0

This Open VKL release adds support for Intel® Arc™ GPUs, Intel® Data Center GPU Flex Series and Intel® Data Center GPU Max Series through SYCL.
- The SYCL support of Open VKL is in beta phase. Current functionality, quality, and GPU performance may not reflect that of the final product.
- Open VKL CPU support in this release remains at Gold level, incorporating the same quality and performance as previous releases.
API changes:
- Handle types are now passed by pointer in the following APIs:
  - vklComputeSample*()
  - vklComputeGradient*()
  - vklGet*IteratorSize*()
  - vklInit*Iterator*()
  - vklIterate*()
- vklLoadModule() has been removed; compile-time linkage to an Open VKL device implementation (cpu or gpu) is now required
- Added vklInit() API, which must be called to initialize the library
- VKL_FILTER_[TRILINEAR,TRICUBIC] are renamed to VKL_FILTER_[LINEAR,CUBIC]
- VKLAMRMethod enum is now uint32_t
- structuredSpherical volumes: the gridSpacing default now results in the volume occupying a full sphere
Added new examples demonstrating GPU usage: vklExamplesGPU and vklTutorialGPU
Superbuild updates to latest versions of dependencies

Open VKL 1.3.2

Move to and require latest versions of RenderKit dependencies: Embree v4.0.0 and rkcommon v1.11.0
ARM support: expose ISPC neon-i32x8 target via OPENVKL_ISA_NEON2X CMake option
Superbuild updates to latest versions of dependencies

Open VKL 1.3.1

Superbuild updates to latest versions of dependencies
Note that the update to zlib v1.2.13 remedies CVE-2022-37434

Open VKL 1.3.0

Added AVX512 8-wide CPU device mode, enabled via the OPENVKL_ISA_AVX512SKX_8_WIDE CMake option
VDB volumes: added support for packed / contiguous data layouts for temporally constant volumes, which can provide improved performance (nodesPackedDense, nodesPackedTile parameters)
VDB utility library: added repackNodes flag to toggle usage of packed data layouts
Particle volumes: general memory efficiency and performance improvements
Superbuild updates to latest versions of dependencies
Minimum ISPC version is now v1.18.0

Open VKL 1.2.0

Added vklSetParam() API function which can set parameters of any supported type
Structured regular volumes:
- Added support for cell-centered data via the cellCentered parameter; vertex-centered remains the default
- Added support for more general transformations via the indexToObject parameter
- Added indexOrigin parameter which applies an index-space vec3i translation
VDB volumes:
- Added indexClippingBounds parameter, which can restrict the active voxel bounding box
- The indexToObject parameter can now be provided as a VKL_AFFINE3F
- Corrected bounding box computations in InnerNode observer
Particle volumes:
- Now ignoring particles with zero radius
VDB utility library: added commit flag (default true) to volume creation methods, allowing apps to set additional parameters before first commit
Examples:
- Added new set of minimal examples, which step through creation of basic volume and isosurface renderers
- Exposing intervalResolutionHint parameter in vklExamples application
Superbuild updates to latest versions of dependencies

Open VKL 1.1.0

vklExamples improvements: asynchronous rendering, multiple viewports, docking, and more
Fixed bug in openvkl_utility_vdb which could lead to crashes when creating VDB volumes with temporally constant tiles
Superbuild updates to latest versions of dependencies
Minimum rkcommon version is now 1.8.0

Open VKL 1.0.1

Fixed issue in structuredRegular and vdb interval iterators that could lead to erroneous initial intervals for certain ray inputs
Fixed handling of intervalResolutionHint interval iterator context parameter for amr, particle, and unstructured volumes with small numbers of cells / primitives

Open VKL 1.0.0

The version 1.0 release marks long term API stability (until v2.0)
Open VKL can now be built for ARM CPUs that support Neon
Iterator API updates:
- Introducing interval and hit iterator contexts, which hold iterator-specific configuration (eliminates value selector objects)
- Interval and hit iteration is now supported on any volume attribute
- Interval iterators now include a time parameter
- Interval iterators now support the intervalResolutionHint parameter, replacing maxIteratorDepth and elementaryCellIteration
Supporting configurable background values; default is now VKL_BACKGROUND_UNDEFINED (NaN) for all volume types
vklGetValueRange() now supports all volume attributes
Added ISPC-side API bindings for vklGetNumAttributes() and vklGetValueRange()
Structured regular volumes:
- Added support for tricubic filtering
- More accurate gradient computations respecting filter mode
- Hit iteration robustness improvements
VDB volumes:
- Interval and hit iteration robustness improvements
- Corrected interval iterator nominalDeltaT computation for non-normalized ray directions and non-uniform object-space grid spacings
- Fixed bug which could cause incorrect value range computations for temporally varying volumes
vklExamples additions demonstrating:
- Multi-attribute interval / hit iteration
- Configurable background values
- Temporally varying volumes
Superbuild updates to latest versions of dependencies
Now requiring minimum versions:
- Embree 3.13.1
- rkcommon 1.7.0
- ISPC 1.16.0

Open VKL 0.13.0

Driver (now device) API changes:
- Renamed VKLDriver to VKLDevice and updated associated device setup APIs
- Use of multiple concurrent devices is now supported; therefore vklNewVolume() and vklNewData() now require a device handle
- Renamed the ispc_device module and ispc device to cpu_device and cpu, respectively
- The OPENVKL_CPU_DEVICE_DEFAULT_WIDTH environment variable can now be used to change the cpu device’s default SIMD width at run time
Added new VKLTemporalFormat enum used for temporally varying volume parameterization
VDB volumes:
- Support for temporally structured and temporally unstructured (TUV) attribute data, which can be used for motion blurred rendering
- Supporting tricubic filtering via VKL_FILTER_TRICUBIC filter type
- Added support for half precision float-point (FP16) attribute data via VKL_HALF data type
- Added a new InnerNode observer and associated utility functions which allows applications to introspect inner nodes of the internal tree structure, including bounding boxes and value ranges
- Renamed VKL_FORMAT_CONSTANT_ZYX to VKL_FORMAT_DENSE_ZYX
Structured regular and spherical volumes:
- Added support for half precision float-point (FP16) attribute data via VKL_HALF data type
Unstructured volumes:
- Added support for elementary cell iteration via the elementaryCellIteration parameter
- Robustness improvements for hit iteration
AMR volumes:
- Improved interval iterator implementation, resolving issues with returned interval nominalDeltaT values
- Interval iterators now support maxIteratorDepth parameter
Interval and hit iteration performance improvements when multiple values ranges / values are selected
Added new temporal compression utilities which applications can use for processing temporally unstructured attribute data
vklExamples additions demonstrating:
- Motion blurred rendering on temporally structured and temporally unstructured vdb volumes
- Tricubic filtering on vdb volumes
- Half-precision floating-point (FP16) support for structuredRegular, structuredSpherical, and vdb volumes
- Elementary cell interval iteration on unstructured volumes
- Use of the InnerNode observer on vdb volumes
Superbuild updates to:
- Embree 3.13.0
- rkcommon 1.6.1
Minimum rkcommon version is now 1.6.1

Open VKL 0.12.1

Fixed bug in VDB volume interval iterator implementation which could lead to missed intervals or incorrect value ranges in returned intervals

Open VKL 0.12.0

Added support for temporally varying volumes with associated API changes for sampling, gradients, and hit iteration. This feature can be used to enable motion blurred rendering
Structured regular volumes:
- Support for temporally structured and temporally unstructured (TUV) input data
- Improved nominalDeltaT for interval iteration
- Interval iterator robustness improvements for axis-aligned rays
- Sampling performance improvements
VDB volumes:
- Multi-attribute support (including three-component float grids)
- Interval iterator robustness improvements for axis-aligned rays
- Performance improvements for scalar sampling
- Now restricting volumes to exactly four levels
- Allowing leaf nodes on the lowest level only
Unstructured volumes:
- Improved nominalDeltaT for interval iteration
vdb_util updates:
- Support for loading multi-attribute .vdb files (float and Vec3s grids)
- Fix order of rotation matrix coefficients loaded from .vdb files
vklExamples additions demonstrating:
- Motion blurred rendering on temporally structured and temporally unstructured volumes (structuredRegular only)
- Support for vdb multi-attribute volumes
- Hit iterator time support
Superbuild updates to:
- Embree 3.12.2
- rkcommon 1.6.0
- ISPC 1.15.0
- OpenVDB 8.0.0
Minimum rkcommon version is now 1.6.0

Open VKL 0.11.0

Introduced API support for multi-attribute volumes, including APIs for sampling multiple attributes simultaneously
- Initially only structuredRegular and structuredSpherical volume types support multi-attribute data
Iterator APIs now work on sampler objects rather than volumes, supporting finer-grained configurability
Observers can now be created for both volume and sampler objects
- LeafNodeAccess observers must now be created on sampler objects
Log and error callbacks now support a user pointer
vdb volume interval iterators:
- Added support for elementary cell iteration when maxIteratorDepth is set to VKL_VDB_NUM_LEVELS-1
- Up to 2x faster iteration
unstructured and particle volume interval iterators:
- Improved interior empty space skipping behavior
- Added support for configurable iterator depth via the maxIteratorDepth parameter
Added support for filter modes in structuredRegular and structuredSpherical volumes
amr volumes now support method parameter on sampler objects
Added new interval_iterator_debug renderer in vklExamples to visualize interval iteration behavior
Hit iterator accuracy improvements for unstructured volumes
Fixed bugs in amr and vdb volume bounding box computations
Fixed bug in unstructured volume gradient computations near empty regions
Minimum ISPC version is now v1.14.1

Open VKL 0.10.0 (alpha)

Added new particle volume type supporting Gaussian radial basis functions
Introduced VKLSampler objects allowing configuration of sampling and gradient behavior
Added stream-wide sampling and gradient APIs
Introduced a new way to allocate iterators, giving the user more freedom in choosing allocation schemes and reducing iterator size
Added support for strided data arrays
Added gradient implementations for amr and vdb volumes
Hit iterator accuracy improvements for amr, structuredSpherical, unstructured, and vdb volumes
Up to 4x performance improvement for structuredRegular and structuredSpherical sampling for volumes in the 1-2GB range
Up to 2x performance improvement for structuredRegular interval iteration
Improved commit speed for unstructured volumes
Improved value range computation in vdb volumes
Improved isosurface shading in vklExamples
Improved parameter validation across all volume types
Aligned VKLHit[4,8,16] and VKLInterval[4,8,16] structs
Added hit epsilon to VKLHit[4,8,16]
Updated parameter names for vdb volumes
Renamed VKLVdbLeafFormat to VKLFormat
Fixed incorrect use of system-installed CMake in superbuild while building dependencies
Fixed various memory leaks
Fixed crashes which could occur in VdbVolume::cleanup() and vklShutdown()
Moved from ospcommon to rkcommon v1.4.1

Open VKL 0.9.0 (alpha)

Added support for VDB sparse structured volumes ("vdb" volume type)
Added vdb_util library to simplify instantiation of VDB volumes, and support loading of .vdb files using OpenVDB
Added VKLObserver and associated APIs, which may used by volume types to pass information back to the application
- A LeafNodeAccess observer is provided for VDB volumes to support on-demand loading of leaf nodes
Structured regular volumes:
- Up to 6x performance improvement for scalar iterator initialization
- Up to 2x performance improvement for scalar iterator iteration
General improvements to the CMake Superbuild for building Open VKL and all associated dependencies
Allowing instantiation of ISPC driver for any supported SIMD width (in addition to the default automatically selected width)
Volume type names are now camelCase (legacy snake_case type names are deprecated), impacting structuredRegular and structuredSpherical volumes
Enabling flushDenormals driver mode by default
Aligning public vkl_vvec3f[4,8,16] and vkl_vrange1f[4,8,16] types
Added VKL_LOG_NONE log level
Fixed bug in vklExamples which could lead to improper rendering on macOS Catalina
Fixed bug in unstructured volume interval iterator which could lead to errors with some combinations of lane masks
Now providing binary releases for Linux, macOS, and Windows

Open VKL 0.8.0 (alpha)

Added support for structured volumes on spherical grids ("structured_spherical" volume type)
Structured regular volumes:
- Up to 8x performance improvement for scalar (single-wide) sampling
- Fixed hit iterator bug which could lead to isosurfacing artifacts
- Renamed voxelData parameter to data
Unstructured volumes:
- Up to 4x performance improvement for scalar (single-wide) sampling
- Improved interval iterator implementation for more efficient space skipping and tighter value bounds on returned intervals
- Now using Embree for BVH builds for faster build times / volume commits
- Renamed vertex.value and cell.value parameters to vertex.data and cell.data, respectively
AMR volumes:
- renamed block.cellWidth parameter to cellWidth, and clarified API documentation
Added vklGetValueRange() API for querying volume value ranges
Added new driver parameters, APIs, and environment variables allowing user control of log levels, log / error output redirection, number of threads, and other options
vklIterateHit[4,8,16]() and vklIterateInterval[4,8,16]() calls now only populate hit / interval data for active lanes
Changed VKLDataType enum values for better forward compatibility
ISPC-side hit and interval iterator objects must now be declared varying
More flexible ISA build configuration through OPENVKL_MAX_ISA and OPENVKL_ISA_* CMake build options
Minimum ospcommon version is now 1.1.0

Open VKL 0.7.0 (alpha)

Initial public alpha release, with support for structured, unstructured, and AMR volumes.

Support and Contact

Open VKL is under active development, and though we do our best to guarantee stable release versions a certain number of bugs, as-yet-missing features, inconsistencies, or any other issues are still possible. Should you find any such issues please report them immediately via Open VKL’s GitHub Issue Tracker (or, if you should happen to have a fix for it, you can also send us a pull request); you may also contact us via email at [email protected].

Join our mailing list to receive release announcements and major news regarding Open VKL.

Open VKL API

The Open VKL API is provided in two parts: a host-side API which is responsible for object creation and configuration (e.g. instantiating new volumes and providing data from the application), and a device-side API which provides access to low-level kernels such as volume sampling and iteration. The host-side API is identical for all Open VKL device implementations, while the device-side API varies slightly between device implementations.

To access the Open VKL host-side API you first need to include the Open VKL header. For C99 or C++:

#include <openvkl/openvkl.h>

Additionally, the device-side APIs are provided through a device-specific header provided by the currently linked-to device:

#include <openvkl/device/openvkl.h>

CPU applications using the Intel® Implicit SPMD Program Compiler (Intel® ISPC) can include the host- and device-side APIs similarly via:

#include <openvkl/openvkl.isph>
#include <openvkl/device/openvkl.isph>

This documentation will discuss the C99/C++ API. The ISPC version has the same functionality and flavor. Looking at the headers, the vklTutorialISPC example, and this documentation should be enough to figure it out.

Device initialization and shutdown

To use the API, one of the implemented backends must be linked at compile time. Currently both a CPU and GPU device are available. To link one of these devices within CMake, use for example:

target_link_libraries(myApp PRIVATE openvkl::openvkl openvkl::openvkl_module_cpu_device)

or

target_link_libraries(myApp PRIVATE openvkl::openvkl openvkl::openvkl_module_gpu_device)

The application code must then first initialize Open VKL:

vklInit();

A device then needs to be instantiated, either via:

VKLDevice device = vklNewDevice("cpu");

or

VKLDevice device = vklNewDevice("gpu");

By default, the CPU device selects the maximum supported SIMD width (and associated ISA) for the system. Optionally, a specific width may be requested using the cpu_4, cpu_8, or cpu_16 device names. Note that the system must support the given width (SSE4.1 for 4-wide, AVX for 8-wide, and AVX512 for 16-wide).

Once a device is created, you can call

void vklDeviceSetInt(VKLDevice, const char *name, int val);
void vklDeviceSetString(VKLDevice, const char *name, const char *val);

to set parameters on the device. The following parameters are understood by all devices:

Type	Name	Description
int	logLevel	logging level; valid values are `VKL_LOG_DEBUG`, `VKL_LOG_INFO`, `VKL_LOG_WARNING`, `VKL_LOG_ERROR` and `VKL_LOG_NONE`
string	logOutput	convenience for setting where log messages go; valid values are `cout`, `cerr` and `none`
string	errorOutput	convenience for setting where error messages go; valid values are `cout`, `cerr` and `none`
int	numThreads	number of threads which Open VKL can use
int	flushDenormals	sets the `Flush to Zero` and `Denormals are Zero` mode of the MXCSR control and status register (default: 1); see Performance Recommendations section for details

Parameters shared by all devices.

Additionally, the following parameters are understood by the gpu device:

Type	Name	Description
void *	syclContext	REQUIRED: pointer to a valid SYCL context

Parameters understood by the gpu device

Once parameters are set, the device must be committed with

vklCommitDevice(device);

The newly committed device is then ready to use. Users may change parameters on a device after initialization. In this case the device would need to be re-committed.

All Open VKL objects are associated with a device. A device handle must be explicitly provided when creating volume and data objects, via vklNewVolume() and vklNewData() respectively. Other object types are automatically associated with a device via transitive dependency on a volume.

On CPU, Open VKL provides vector-wide versions for several APIs. To determine the native vector width for a given device, call:

int width = vklGetNativeSIMDWidth(VKLDevice device);

When the application is finished with an Open VKL device or shutting down, release the device via:

vklReleaseDevice(VKLDevice device);

Environment variables

The generic device parameters can be overridden via environment variables for easy changes to Open VKL’s behavior without needing to change the application (variables are prefixed by convention with “OPENVKL_”):

Variable	Description
OPENVKL_LOG_LEVEL	logging level; valid values are `debug`, `info`, `warning`, `error` and `none`
OPENVKL_LOG_OUTPUT	convenience for setting where log messages go; valid values are `cout`, `cerr` and `none`
OPENVKL_ERROR_OUTPUT	convenience for setting where error messages go; valid values are `cout`, `cerr` and `none`
OPENVKL_THREADS	number of threads which Open VKL can use
OPENVKL_FLUSH_DENORMALS	sets the `Flush to Zero` and `Denormals are Zero` mode of the MXCSR control and status register (default: 1); see Performance Recommendations section for details

Environment variables understood by all devices.

Note that these environment variables take precedence over values set through the vklDeviceSet*() functions.

Additionally, the CPU device’s default SIMD width can be overriden at run time with the OPENVKL_CPU_DEVICE_DEFAULT_WIDTH environment variable. Legal values are 4, 8, or 16. This setting is only applicable when the generic cpu device is instantiated; if a specific width is requested via the cpu_[4,8,16] device names then the environment variable is ignored.

Error handling and log messages

The following errors are currently used by Open VKL:

Name	Description
VKL_NO_ERROR	no error occurred
VKL_UNKNOWN_ERROR	an unknown error occurred
VKL_INVALID_ARGUMENT	an invalid argument was specified
VKL_INVALID_OPERATION	the operation is not allowed for the specified object
VKL_OUT_OF_MEMORY	there is not enough memory to execute the command
VKL_UNSUPPORTED_CPU	the CPU is not supported (minimum ISA is SSE4.1)

Possible error codes, i.e., valid named constants of type VKLError.

These error codes are either directly returned by some API functions, or are recorded to be later queried by the application via

VKLError vklDeviceGetLastErrorCode(VKLDevice);

A more descriptive error message can be queried by calling

const char* vklDeviceGetLastErrorMsg(VKLDevice);

Alternatively, the application can also register a callback function of type

typedef void (*VKLErrorCallback)(void *, VKLError, const char* message);

via

void vklDeviceSetErrorCallback(VKLDevice, VKLErrorFunc, void *);

to get notified when errors occur. Applications may be interested in messages which Open VKL emits, whether for debugging or logging events. Applications can register a callback function of type

typedef void (*VKLLogCallback)(void *, const char* message);

via

void vklDeviceSetLogCallback(VKLDevice, VKLLogCallback, void *);

which Open VKL will use to emit log messages. Applications can clear either callback by passing nullptr instead of an actual function pointer. By default, Open VKL uses cout and cerr to emit log and error messages, respectively. The last parameter to vklDeviceSetErrorCallback and vklDeviceSetLogCallback is a user data pointer. Open VKL passes this pointer to the callback functions as the first parameter. Note that in addition to setting the above callbacks, this behavior can be changed via the device parameters and environment variables described previously.

Basic data types

Open VKL defines 3-component vectors of integer and float types:

typedef struct
{
  int x, y, z;
} vkl_vec3i;

typedef struct
{
  float x, y, z;
} vkl_vec3f;

Vector versions of these are also defined in structure-of-array format for 4, 8, and 16 wide types.

typedef struct
{
  float x[WIDTH];
  float y[WIDTH];
  float z[WIDTH];
} vkl_vvec3f##WIDTH;

typedef struct
{
  float lower[WIDTH], upper[WIDTH];
} vkl_vrange1f##WIDTH;

1-D range and 3-D ranges are defined as ranges and boxes, with no vector versions:

typedef struct
{
  float lower, upper;
} vkl_range1f;

typedef struct
{
  vkl_vec3f lower, upper;
} vkl_box3f;

Object model

Objects in Open VKL are exposed to the APIs as handles with internal reference counting for lifetime determination. Objects are created with each particular type’s vklNew... API entry point. For example, vklNewData and vklNewVolume.

In general, modifiable parameters to objects are modified using vklSet... functions based on the type of the parameter being set. The parameter name is passed as a string. Below are variants of vklSet....

void vklSetBool(VKLObject object, const char *name, int b);
void vklSetFloat(VKLObject object, const char *name, float x);
void vklSetVec3f(VKLObject object, const char *name, float x, float y, float z);
void vklSetInt(VKLObject object, const char *name, int x);
void vklSetVec3i(VKLObject object, const char *name, int x, int y, int z);
void vklSetData(VKLObject object, const char *name, VKLData data);
void vklSetString(VKLObject object, const char *name, const char *s);
void vklSetVoidPtr(VKLObject object, const char *name, void *v);

A more generic parameter setter is also available, which allows setting parameters beyond the explicit types above:

void vklSetParam(VKLObject object,
                 const char *name,
                 VKLDataType dataType,
                 const void *mem);

Note that mem must always be a pointer to the object, otherwise accidental type casting can occur. This is especially true for pointer types (VKL_VOID_PTR and VKLObject handles), as they will implicitly cast to void\ *, but be incorrectly interpreted.

After parameters have been set, vklCommit must be called on the object to make them take effect.

Open VKL uses reference counting to manage the lifetime of all objects. Therefore one cannot explicitly “delete” any object. Instead, one can indicate the application does not need or will not access the given object anymore by calling

void vklRelease(VKLObject);

This decreases the object’s reference count. If the count reaches 0 the object will automatically be deleted.

Managed data

Large data is passed to Open VKL via a VKLData handle created with vklNewData:

VKLData vklNewData(VKLDevice device,
                   size_t numItems,
                   VKLDataType dataType,
                   const void *source,
                   VKLDataCreationFlags dataCreationFlags,
                   size_t byteStride);

Data objects can be created as Open VKL owned (dataCreationFlags = VKL_DATA_DEFAULT), in which the library will make a copy of the data for its use, or shared (dataCreationFlags = VKL_DATA_SHARED_BUFFER), which will try to use the passed pointer for usage. The library is allowed to copy data when a volume is committed. Note that for the gpu device, shared data buffers only support source data from USM shared allocations.

The distance between consecutive elements in source is given in bytes with byteStride. If the provided byteStride is zero, then it will be determined automatically as sizeof(type). Open VKL owned data will be compacted into a naturally-strided array on copy, regardless of the original byteStride.

As with other object types, when data objects are no longer needed they should be released via vklRelease.

The enum type VKLDataType describes the different element types that can be represented in Open VKL. The types accepted vary per volume; see the volume section for specifics. Valid constants are listed in the table below.

Type/Name	Description
VKL_DEVICE	API device object reference
VKL_DATA	data reference
VKL_OBJECT	generic object reference
VKL_VOLUME	volume object reference
VKL_STRING	C-style zero-terminated character string
VKL_CHAR, VKL_VEC[234]C	8 bit signed character scalar and [234]-element vector
VKL_UCHAR, VKL_VEC[234]UC	8 bit unsigned character scalar and [234]-element vector
VKL_SHORT, VKL_VEC[234]S	16 bit unsigned integer scalar and [234]-element vector
VKL_USHORT, VKL_VEC[234]US	16 bit unsigned integer scalar and [234]-element vector
VKL_INT, VKL_VEC[234]I	32 bit signed integer scalar and [234]-element vector
VKL_UINT, VKL_VEC[234]UI	32 bit unsigned integer scalar and [234]-element vector
VKL_LONG, VKL_VEC[234]L	64 bit signed integer scalar and [234]-element vector
VKL_ULONG, VKL_VEC[234]UL	64 bit unsigned integer scalar and [234]-element vector
VKL_HALF, VKL_VEC[234]H	16 bit half precision floating-point scalar and [234]-element vector (IEEE 754 `binary16`)
VKL_FLOAT, VKL_VEC[234]F	32 bit single precision floating-point scalar and [234]-element vector
VKL_DOUBLE, VKL_VEC[234]D	64 bit double precision floating-point scalar and [234]-element vector
VKL_BOX[1234]I	32 bit integer box (lower + upper bounds)
VKL_BOX[1234]F	32 bit single precision floating-point box (lower + upper bounds)
VKL_LINEAR[23]F	32 bit single precision floating-point linear transform ([23] vectors)
VKL_AFFINE[23]F	32 bit single precision floating-point affine transform (linear transform plus translation)
VKL_VOID_PTR	raw memory address

Valid named constants for VKLDataType.

Volume types

Open VKL currently supports structured volumes on regular and spherical grids; unstructured volumes with tetrahedral, wedge, pyramid, and hexahedral primitive types; adaptive mesh refinement (AMR) volumes; sparse VDB volumes; and particle volumes. Volumes are created with vklNewVolume with a device and appropriate type string:

VKLVolume vklNewVolume(VKLDevice device, const char *type);

In addition to the usual vklSet...() and vklCommit() APIs, the volume bounding box can be queried:

vkl_box3f vklGetBoundingBox(VKLVolume volume);

The number of attributes in a volume can also be queried:

unsigned int vklGetNumAttributes(VKLVolume volume);

Finally, the value range of the volume for a given attribute can be queried:

vkl_range1f vklGetValueRange(VKLVolume volume, unsigned int attributeIndex);

Structured Volumes

Structured volumes only need to store the values of the samples, because their addresses in memory can be easily computed from a 3D position. Data can be provided either per cell or per vertex (the default), selectable via the cellCentered parameter. This parameter also affects the interpretation of the volume’s dimensions, which will be in units of cells or vertices, respectively. A volume with $(x, y, z)$ vertices will have $(x-1, y-1, z-1)$ cells.

Structured Regular Volumes

A common type of structured volumes are regular grids, which are created by passing a type string of "structuredRegular" to vklNewVolume. The parameters understood by structured regular volumes are summarized in the table below.

Type	Name	Default	Description
vec3i	dimensions		number of values in each dimension $(x, y, z)$
VKLData VKLData[]	data		VKLData object(s) of volume data, supported types are:
			`VKL_UCHAR`
			`VKL_SHORT`
			`VKL_USHORT`
			`VKL_HALF`
			`VKL_FLOAT`
			`VKL_DOUBLE`
			Multiple attributes are supported through passing an array of VKLData objects.
bool	cellCentered	false	indicates if data is provided per cell (true) or per vertex (false)
vec3f	gridOrigin	$(0, 0, 0)$	origin of the grid in object space
vec3f	gridSpacing	$(1, 1, 1)$	size of the grid cells in object space
affine3f	indexToObject	1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0	Defines the transformation from index space to object space. In index space, the grid is an axis-aligned regular grid, and grid cells have size (1,1,1). This parameter takes precedence over `gridOrigin` and `gridSpacing`, if provided.
vec3i	indexOrigin	$(0, 0, 0)$	Defines the index space origin of the volume. This translation is applied before any (`gridOrigin`, `gridSpacing`) or `indexToObject` transformation.
uint32	temporalFormat	`VKL_TEMPORAL_FORMAT_CONSTANT`	The temporal format for this volume. Use `VKLTemporalFormat` for named constants. Structured regular volumes support `VKL_TEMPORAL_FORMAT_CONSTANT`, `VKL_TEMPORAL_FORMAT_STRUCTURED`, and `VKL_TEMPORAL_FORMAT_UNSTRUCTURED`.
int	temporallyStructuredNumTimesteps		For temporally structured variation, number of timesteps per voxel. Only valid if `temporalFormat` is `VKL_TEMPORAL_FORMAT_STRUCTURED`.
uint32[] uint64[]	temporallyUnstructuredIndices		For temporally unstructured variation, indices to `data` time series beginning per voxel. Only valid if `temporalFormat` is `VKL_TEMPORAL_FORMAT_UNSTRUCTURED`.
float[]	temporallyUnstructuredTimes		For temporally unstructured variation, time values corresponding to values in `data`. Only valid if `temporalFormat` is `VKL_TEMPORAL_FORMAT_UNSTRUCTURED`.
float[]	background	`VKL_BACKGROUND_UNDEFINED`	For each attribute, the value that is returned when sampling an undefined region outside the volume domain.

Configuration parameters for structured regular ("structuredRegular") volumes.

Structured regular volumes support temporally structured and temporally unstructured temporal variation. See section ‘Temporal Variation’ for more detail.

The following additional parameters can be set both on "structuredRegular" volumes and their sampler objects. Sampler object parameters default to volume parameters.

Type	Name	Default	Description
int	filter	`VKL_FILTER_LINEAR`	The filter used for reconstructing the field. Use `VKLFilter` for named constants.
int	gradientFilter	`filter`	The filter used for reconstructing the field during gradient computations. Use `VKLFilter` for named constants.

Configuration parameters for structured regular ("structuredRegular") volumes and their sampler objects.

Reconstruction filters

Structured regular volumes support the filter types VKL_FILTER_NEAREST, VKL_FILTER_LINEAR, and VKL_FILTER_CUBIC for both filter and gradientFilter.

Note that when gradientFilter is set to VKL_FILTER_NEAREST, gradients are always $(0, 0, 0)$.

Structured Spherical Volumes

Structured spherical volumes are also supported, which are created by passing a type string of "structuredSpherical" to vklNewVolume. The grid dimensions and parameters are defined in terms of radial distance ($r$), inclination angle ($\theta$), and azimuthal angle ($\phi$), conforming with the ISO convention for spherical coordinate systems. Structured spherical volumes currently only support vertex-centered data. The coordinate system and parameters understood by structured spherical volumes are summarized below.

$Structured spherical volume coordinate system: radial distance ($r$), inclination angle ($\theta$), and azimuthal angle ($\phi$).$

Type	Name	Default	Description
vec3i	dimensions		number of voxels in each dimension $(r, \theta, \phi)$
VKLData VKLData[]	data		VKLData object(s) of voxel data, supported types are:
			`VKL_UCHAR`
			`VKL_SHORT`
			`VKL_USHORT`
			`VKL_HALF`
			`VKL_FLOAT`
			`VKL_DOUBLE`
			Multiple attributes are supported through passing an array of VKLData objects.
vec3f	gridOrigin	$(0, 0, 0)$	origin of the grid in units of $(r, \theta, \phi)$; angles in degrees
vec3f	gridSpacing	$(1, \theta_0, \phi_0)$	size of the grid cells in units of $(r, \theta, \phi)$; angles in degrees. The defaults _0 and _0 are such that the volume occupies a full sphere.
float[]	background	`VKL_BACKGROUND_UNDEFINED`	For each attribute, the value that is returned when sampling an undefined region outside the volume domain.

Configuration parameters for structured spherical ("structuredSpherical") volumes.

These grid parameters support flexible specification of spheres, hemispheres, spherical shells, spherical wedges, and so forth. The grid extents (computed as $[gridOrigin, gridOrigin + (dimensions - 1) * gridSpacing]$) however must be constrained such that:

$r \geq 0$
$0 \leq \theta \leq 180$
$0 \leq \phi \leq 360$

The following additional parameters can be set both on "structuredSpherical" volumes and their sampler objects. Sampler object parameters default to volume parameters.

Type	Name	Default	Description
int	filter	`VKL_FILTER_LINEAR`	The filter used for reconstructing the field. Use `VKLFilter` for named constants.
int	gradientFilter	`filter`	The filter used for reconstructing the field during gradient computations. Use `VKLFilter` for named constants.

Configuration parameters for structured spherical ("structuredSpherical") volumes and their sampler objects.

Adaptive Mesh Refinement (AMR) Volumes

Open VKL currently supports block-structured (Berger-Colella) AMR volumes. Volumes are specified as a list of blocks, which exist at levels of refinement in potentially overlapping regions. Blocks exist in a tree structure, with coarser refinement level blocks containing finer blocks. The cell width is equal for all blocks at the same refinement level, though blocks at a coarser level have a larger cell width than finer levels.

There can be any number of refinement levels and any number of blocks at any level of refinement.

Blocks are defined by three parameters: their bounds, the refinement level in which they reside, and the scalar data contained within each block.

Note that cell widths are defined per refinement level, not per block.

AMR volumes are created by passing the type string "amr" to vklNewVolume, and have the following parameters:

Type	Name	Default	Description
float[]	cellWidth		[data] array of each level’s cell width
box3i[]	block.bounds		[data] array of each block’s bounds (in voxels)
int[]	block.level		[data] array of each block’s refinement level
VKLData[]	block.data		[data] array of each block’s VKLData object containing the actual scalar voxel data. Currently only `VKL_FLOAT` data is supported.
vec3f	gridOrigin	$(0, 0, 0)$	origin of the grid in object space
vec3f	gridSpacing	$(1, 1, 1)$	size of the grid cells in object space
float	background	`VKL_BACKGROUND_UNDEFINED`	The value that is returned when sampling an undefined region outside the volume domain.

Configuration parameters for AMR ("amr") volumes.

Note that the gridOrigin and gridSpacing parameters act just like the structured volume equivalent, but they only modify the root (coarsest level) of refinement.

The following additional parameters can be set both on "amr" volumes and their sampler objects. Sampler object parameters default to volume parameters.

Type	Name	Default	Description
`VKLAMRMethod`	method	`VKL_AMR_CURRENT`	`VKLAMRMethod` sampling method. Supported methods are:
			`VKL_AMR_CURRENT`
			`VKL_AMR_FINEST`
			`VKL_AMR_OCTANT`

Configuration parameters for AMR ("AMR") volumes and their sampler objects.

Open VKL’s AMR implementation was designed to cover Berger-Colella [1] and Chombo [2] AMR data. The method parameter above determines the interpolation method used when sampling the volume.

VKL_AMR_CURRENT finds the finest refinement level at that cell and interpolates through this “current” level
VKL_AMR_FINEST will interpolate at the closest existing cell in the volume-wide finest refinement level regardless of the sample cell’s level
VKL_AMR_OCTANT interpolates through all available refinement levels at that cell. This method avoids discontinuities at refinement level boundaries at the cost of performance

Gradients are computed using finite differences, using the method defined on the sampler.

Details and more information can be found in the publication for the implementation [3].

M. J. Berger, and P. Colella. “Local adaptive mesh refinement for shock hydrodynamics.” Journal of Computational Physics 82.1 (1989): 64-84. DOI: 10.1016/0021-9991(89)90035-1
M. Adams, P. Colella, D. T. Graves, J.N. Johnson, N.D. Keen, T. J. Ligocki. D. F. Martin. P.W. McCorquodale, D. Modiano. P.O. Schwartz, T.D. Sternberg and B. Van Straalen, Chombo Software Package for AMR Applications - Design Document, Lawrence Berkeley National Laboratory Technical Report LBNL-6616E.
I. Wald, C. Brownlee, W. Usher, and A. Knoll. CPU volume rendering of adaptive mesh refinement data. SIGGRAPH Asia 2017 Symposium on Visualization on - SA ’17, 18(8), 1–8. DOI: 10.1145/3139295.3139305

Unstructured Volumes

Unstructured volumes can have their topology and geometry freely defined. Geometry can be composed of tetrahedral, hexahedral, wedge or pyramid cell types. The data format used is compatible with VTK and consists of multiple arrays: vertex positions and values, vertex indices, cell start indices, cell types, and cell values.

Sampled cell values can be specified either per-vertex (vertex.data) or per-cell (cell.data). If both arrays are set, cell.data takes precedence.

Similar to a mesh, each cell is formed by a group of indices into the vertices. For each vertex, the corresponding (by array index) data value will be used for sampling when rendering, if specified. The index order for a tetrahedron is the same as VTK_TETRA: bottom triangle counterclockwise, then the top vertex.

For hexahedral cells, each hexahedron is formed by a group of eight indices into the vertices and data values. Vertex ordering is the same as VTK_HEXAHEDRON: four bottom vertices counterclockwise, then top four counterclockwise.

For wedge cells, each wedge is formed by a group of six indices into the vertices and data values. Vertex ordering is the same as VTK_WEDGE: three bottom vertices counterclockwise, then top three counterclockwise.

For pyramid cells, each cell is formed by a group of five indices into the vertices and data values. Vertex ordering is the same as VTK_PYRAMID: four bottom vertices counterclockwise, then the top vertex.

To maintain VTK data compatibility, the index array may be specified with cell sizes interleaved with vertex indices in the following format: $n, id_1, ..., id_n, m, id_1, ..., id_m$. This alternative index array layout can be enabled through the indexPrefixed flag (in which case, the cell.type parameter should be omitted).

Gradients are computed using finite differences.

Unstructured volumes are created by passing the type string "unstructured" to vklNewVolume, and have the following parameters:

Type	Name	Default	Description
vec3f[]	vertex.position		[data] array of vertex positions
float[]	vertex.data		[data] array of vertex data values to be sampled
uint32[] / uint64[]	index		[data] array of indices (into the vertex array(s)) that form cells
bool	indexPrefixed	false	indicates that the `index` array is provided in a VTK-compatible format, where the indices of each cell are prefixed with the number of vertices
uint32[] / uint64[]	cell.index		[data] array of locations (into the index array), specifying the first index of each cell
float[]	cell.data		[data] array of cell data values to be sampled
uint8[]	cell.type		[data] array of cell types (VTK compatible). Supported types are:
			`VKL_TETRAHEDRON`
			`VKL_HEXAHEDRON`
			`VKL_WEDGE`
			`VKL_PYRAMID`
bool	hexIterative	false	hexahedron interpolation method, defaults to fast non-iterative version which could have rendering inaccuracies may appear if hex is not parallelepiped
bool	precomputedNormals	false	whether to accelerate by precomputing, at a cost of 12 bytes/face
float	background	`VKL_BACKGROUND_UNDEFINED`	The value that is returned when sampling an undefined region outside the volume domain.

Configuration parameters for unstructured ("unstructured") volumes.

VDB Volumes

VDB volumes implement a data structure that is very similar to the data structure outlined in Museth [1].

The data structure is a hierarchical regular grid at its core: Nodes are regular grids, and each grid cell may either store a constant value (this is called a tile), or child pointers.

Nodes in VDB trees are wide: Nodes on the first level have a resolution of 32^3 voxels by default, on the next level 16^3, and on the leaf level 8^3 voxels. All nodes on a given level have the same resolution. This makes it easy to find the node containing a coordinate using shift operations (cp. [1]).

VDB leaf nodes are implicit in Open VKL: they are stored as pointers to user-provided data.

VDB volumes interpret input data as constant cells (which are then potentially filtered). This is in contrast to structuredRegular volumes, which can have either a vertex-centered or cell-centered interpretation.

The VDB implementation in Open VKL follows the following goals:

Efficient data structure traversal on vector architectures.
Enable the use of industry-standard .vdb files created through the OpenVDB library.
Compatibility with OpenVDB on a leaf data level, so that .vdb files may be loaded with minimal overhead.

VDB volumes are created by passing the type string "vdb" to vklNewVolume, and have the following parameters:

Type	Name	Default	Description
affine3f float[]	indexToObject	1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0	Defines the transformation from index space to object space. In index space, the grid is an axis-aligned regular grid, and leaf voxels have size (1,1,1). A `vkl_affine3f` can be provided; alternatively an array of 12 values of type `float` can be used, where the first 9 values are interpreted as a row-major linear transformation matrix, and the last 3 values are the translation of the grid origin.
uint32[]	node.format		For each input node, the data format. Currently supported are `VKL_FORMAT_TILE` for tiles, and `VKL_FORMAT_DENSE_ZYX` for nodes that are dense regular grids.
uint32[]	node.level		For each input node, the level on which this node exists. Tiles may exist on levels [1, `VKL_VDB_NUM_LEVELS-1`], all other nodes may only exist on level `VKL_VDB_NUM_LEVELS-1`.
vec3i[]	node.origin		For each input node, the node origin index.
VKLData[]	node.data		For each input node, the attribute data. Single-attribute volumes may have one array provided per node, while multi-attribute volumes require an array per attribute for each node. Nodes with format `VKL_FORMAT_TILE` are expected to have single-entry arrays per attribute. Nodes with format `VKL_FORMAT_DENSE_ZYX` are expected to have arrays with `vklVdbLevelNumVoxels(level[i])` entries per attribute. `VKL_HALF` and `VKL_FLOAT` data is currently supported; all nodes for a given attribute must be the same data type.
uint32[]	node.temporalFormat	`VKL_TEMPORAL_FORMAT_CONSTANT`	The temporal format for this volume. Use `VKLTemporalFormat` for named constants. VDB volumes support `VKL_TEMPORAL_FORMAT_CONSTANT`, `VKL_TEMPORAL_FORMAT_STRUCTURED`, and `VKL_TEMPORAL_FORMAT_UNSTRUCTURED`.
int[]	node.temporallyStructuredNumTimesteps		For temporally structured variation, number of timesteps per voxel. Only valid if `temporalFormat` is `VKL_TEMPORAL_FORMAT_STRUCTURED`.
VKLData[]	node.temporallyUnstructuredIndices		For temporally unstructured variation, beginning per voxel. Supported data types for each node are `VKL_UINT` and `VKL_ULONG`. Only valid if `temporalFormat` is `VKL_TEMPORAL_FORMAT_UNSTRUCTURED`.
VKLData[]	node.temporallyUnstructuredTimes		For temporally unstructured variation, time values corresponding to values in `node.data`. For each node, the data must be of type `VKL_FLOAT`. Only valid if `temporalFormat` is `VKL_TEMPORAL_FORMAT_UNSTRUCTURED`.
VKLData[]	nodesPackedDense		Optionally provided instead of `node.data`, for each attribute a single array of all dense node data (`VKL_FORMAT_DENSE_ZYX` only) in a contiguous layout, provided in the same order as the corresponding `node.*` parameters. This packed layout may be more performant. Supported for temporally constant data only.
VKLData[]	nodesPackedTile		Optionally provided instead of `node.data`, for each attribute a single array of all tile node data (`VKL_FORMAT_TILE` only) in a contiguous layout, provided in the same order as the corresponding `node.*` parameters. This packed layout may be more performant. Supported for temporally constant data only.
float[]	background	`VKL_BACKGROUND_UNDEFINED`	For each attribute, the value that is returned when sampling an undefined region outside the volume domain.
box3i	indexClippingBounds		Clips the volume to the specified index-space bounding box. This is useful for volumes with dimensions that are not even multiples of the leaf node dimensions, or .vdb files with restrictive active voxel bounding boxes.

Configuration parameters for VDB ("vdb") volumes.

The level, origin, format, and data parameters must have the same size, and there must be at least one valid node or commit() will fail. The nodesPackedDense and nodesPackedTile parameters may be provided instead of node.data; this packed data layout may provide better performance.

VDB volumes support temporally structured and temporally unstructured temporal variation. See section ‘Temporal Variation’ for more detail.

The following additional parameters can be set both on vdb volumes and their sampler objects (sampler object parameters default to volume parameters).

Type	Name	Default	Description
int	filter	`VKL_FILTER_LINEAR`	The filter used for reconstructing the field. Use `VKLFilter` for named constants.
int	gradientFilter	`filter`	The filter used for reconstructing the field during gradient computations. Use `VKLFilter` for named constants.
int	maxSamplingDepth	`VKL_VDB_NUM_LEVELS`-1	Do not descend further than to this depth during sampling.

Configuration parameters for VDB ("vdb") volumes and their sampler objects.

VDB volume objects support the following observers:

Name	Buffer Type	Description
InnerNode	float[]	Return an array of bounding boxes, along with value ranges, of inner nodes in the data structure. The bounding box is given in object space. For a volume with M attributes, the entries in this array are (6+2*M)-tuples `(minX, minY, minZ, maxX, maxY, maxZ, lower_0, upper_0, lower_1, upper_1, ...)`. This is in effect a low resolution representation of the volume. The InnerNode observer can be parametrized using `int maxDepth` to control the depth at which inner nodes are returned. Note that the observer will also return leaf nodes or tiles at lower levels if they exist.

Observers supported by VDB ("vdb") volumes.

VDB sampler objects support the following observers:

Name	Buffer Type	Description
LeafNodeAccess	uint32[]	This observer returns an array with as many entries as input nodes were passed. If the input node i was accessed during traversal, then the ith entry in this array has a nonzero value. This can be used for on-demand loading of leaf nodes.

Observers supported by sampler objects created on VDB ("vdb") volumes.

Reconstruction filters

VDB volumes support the filter types VKL_FILTER_NEAREST, VKL_FILTER_LINEAR, and VKL_FILTER_CUBIC for both filter and gradientFilter.

Note that when gradientFilter is set to VKL_FILTER_NEAREST, gradients are always $(0, 0, 0)$.

Major differences to OpenVDB

Open VKL implements sampling in ISPC, and can exploit wide SIMD architectures.
VDB volumes in Open VKL are read-only once committed, and designed for rendering only. Authoring or manipulating datasets is not in the scope of this implementation.
The only supported field types are VKL_HALF and VKL_FLOAT at this point. Other field types may be supported in the future. Note that multi-attribute volumes may be used to represent multi-component (e.g. vector) fields.
The root level in Open VKL has a single node with resolution 64^3 (cp. [1]. OpenVDB uses a hash map, instead).
Open VKL supports four-level vdb volumes. The resolution of each level can be configured at compile time using CMake variables.
- VKL_VDB_LOG_RESOLUTION_0 sets the base 2 logarithm of the root level resolution. This variable defaults to 6, which means that the root level has a resolution of $(2^6)^3 = 64^3$.
- VKL_VDB_LOG_RESOLUTION_1 and VKL_VDB_LOG_RESOLUTION_2 default to 5 and 4, respectively. This matches the default Open VDB resolution for inner levels.
- VKL_VDB_LOG_RESOLUTION_3 set the base 2 logarithm of the leaf level resolution, and defaults to 3. Therefore, leaf nodes have a resolution of $8^3$ voxels. Again, this matches the Open VDB default. The default settings lead to a domain resolution of $2^18^3=262144^3$ voxels.

Loading OpenVDB .vdb files

Files generated with OpenVDB can be loaded easily since Open VKL vdb volumes implement the same leaf data layout. This means that OpenVDB leaf data pointers can be passed to Open VKL using shared data buffers, avoiding copy operations.

An example of this can be found in utility/vdb/include/openvkl/utility/vdb/OpenVdbGrid.h, where the class OpenVdbFloatGrid encapsulates the necessary operations. This class is also accessible through the vklExamples application using the -file and -field command line arguments.

To use this example feature, compile Open VKL with OpenVDB_ROOT pointing to the OpenVDB prefix.

Museth, K. VDB: High-Resolution Sparse Volumes with Dynamic Topology. ACM Transactions on Graphics 32(3), 2013. DOI: 10.1145/2487228.2487235

Particle Volumes

Particle volumes consist of a set of points in space. Each point has a position, a radius, and a weight typically associated with an attribute. A radial basis function defines the contribution of that particle. Currently, we use the Gaussian radial basis function,

phi(P) = w * exp( -0.5 * ((P - p) / r)^2 )

where P is the particle position, p is the sample position, r is the radius and w is the weight.

At each sample, the scalar field value is then computed as the sum of each radial basis function phi, for each particle that overlaps it. Gradients are similarly computed, based on the summed analytical contributions of each contributing particle.

Particles with a radius less than or equal to zero are ignored. At least one valid particle (radius greater than zero) must be provided.

The Open VKL implementation is similar to direct evaluation of samples in Reda et al.[2]. It uses an Embree-built BVH with a custom traversal, similar to the method in [1].

Particle volumes are created by passing the type string "particle" to vklNewVolume, and have the following parameters:

Type	Name	Default	Description
vec3f[]	particle.position		[data] array of particle positions
float[]	particle.radius		[data] array of particle radii
float[]	particle.weight	null	[data] (optional) array of particle weights, specifying the height of the kernel.
float	radiusSupportFactor	3.0	The multipler of the particle radius required for support. Larger radii ensure smooth results at the cost of performance. In the Gaussian kernel, the the radius is one standard deviation (sigma), so a `radiusSupportFactor` of 3 corresponds to 3*sigma.
float	clampMaxCumulativeValue	0	The maximum cumulative value possible, set by user. All cumulative values will be clamped to this, and further traversal (RBF summation) of particle contributions will halt when this value is reached. A value of zero or less turns this off.
bool	estimateValueRanges	true	Enable heuristic estimation of value ranges which are used in internal acceleration structures for interval and hit iterators, as well as for determining the volume’s overall value range. When set to `false`, the user must specify `clampMaxCumulativeValue`, and all value ranges will be assumed [0, `clampMaxCumulativeValue`]. Disabling this may improve volume commit time, but will make interval and hit iteration less efficient.

Configuration parameters for particle ("particle") volumes.

Knoll, A., Wald, I., Navratil, P., Bowen, A., Reda, K., Papka, M.E. and Gaither, K. (2014), RBF Volume Ray Casting on Multicore and Manycore CPUs. Computer Graphics Forum, 33: 71-80. doi:10.1111/cgf.12363
K. Reda, A. Knoll, K. Nomura, M. E. Papka, A. E. Johnson and J. Leigh, “Visualizing large-scale atomistic simulations in ultra-resolution immersive environments,” 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), Atlanta, GA, 2013, pp. 59-65.

Temporal Variation

Open VKL supports two types of temporal variation: temporally structured and temporally unstructured. When one of these modes is enabled, the volume can be sampled at different times. In both modes, time is assumed to vary between zero and one. This can be useful for implementing renderers with motion blur, for example.

Temporal variation is generally configured through a parameter temporalFormat, which accepts constants from the VKLTemporalFormat enum, though not all modes may be supported by all volumes. On volumes that expect multiple input nodes, the parameter is an array node.temporalFormat, and must provide one value per node. Multiple attributes in a voxel share the same temporal configuration. Please refer to the individual volume sections above to find out supported for each volume type.

temporalFormat defaults to VKL_TEMPORAL_FORMAT_CONSTANT for all volume types. This means that no temporal variation is present in the data.

Temporally structured variation is configured by setting temporalFormat to VKL_TEMPORAL_FORMAT_STRUCTURED. In this mode, the volume expects an additional parameter [node.]temporallyStructuredNumTimesteps, which specifies how many time steps are provided for all voxels, and must be at least 2. A volume, or node, with $N$ voxels expects $N * temporallyStructuredNumTimesteps$ values for each attribute. The values are assumed evenly spaced over times $[0, 1]$: ${0, 1/(N-1), ..., 1}$

Temporally unstructured variation supports differing time step counts and sample times per voxel. For $N$ input voxels, temporallyUnstructuredIndices is an array of $N+1$ indices. Voxel $i$ has $N_i = [temporallyUnstructuredIndices[i+1]-temporallyUnstructuredIndices[i])$ temporal samples starting at index $temporallyUnstructuredIndices[i]$. temporallyUnstructuredTimes specifies the times corresponding to the sample values; the time values for each voxel must be between zero and one and strictly increasing: $t0 < t1 < ... < tN$. To return a value at sample time t, $t0 <= t <= tN$, Open VKL will interpolate linearly from the two nearest time steps. Time values outside this range are clamped to $[t0, tN]$.

Sampler Objects

Computing the value of a volume at an object space coordinate is done using the sampling API, and sampler objects. Sampler objects can be created using

VKLSampler vklNewSampler(VKLVolume volume);

Sampler objects may then be parametrized with traversal parameters. Available parameters are defined by volumes, and are a subset of the volume parameters. As an example, filter can be set on both vdb volumes and their sampler objects. The volume parameter is used as the default for sampler objects. The sampler object parameter provides an override per ray. More detail on parameters can be found in the sections on volumes. Use vklCommit() to commit parameters to the sampler object.

Sampling

The scalar API takes a volume and coordinate, and returns a float value. The volume’s background value (by default VKL_BACKGROUND_UNDEFINED) is returned for probe points outside the volume. The attribute index selects the scalar attribute of interest; not all volumes support multiple attributes. The time value, which must be between 0 and 1, specifies the sampling time. For temporally constant volumes, this value has no effect.

For the cpu device, the scalar sampling API is:

float vklComputeSample(const VKLSampler *sampler,
                       const vkl_vec3f *objectCoordinates,
                       unsigned int attributeIndex,
                       float time);

while on the gpu device, it is:

float vklComputeSample(const VKLSampler *sampler,
                       const vkl_vec3f *objectCoordinates,
                       unsigned int attributeIndex,
                       float time,
                       const VKLFeatureFlags featureFlags);

Note that the gpu sampling API introduces an additional featureFlags argument. These provided “feature flags” allow Open VKL to prune unnecessary code during just-in-time (JIT) compilation on GPU, providing potentially significant performance gains. See section ‘Feature flag usage on GPU’ for details.

Vector-wide and Stream-wide Sampling (CPU device only)

On the cpu device, vector-wide and stream-wide sampling APIs are also provided.

Vector versions allow sampling at 4, 8, or 16 positions at once. Depending on the machine type and Open VKL device implementation, these can give greater performance. An active lane mask valid is passed in as an array of integers; set 0 for lanes to be ignored, -1 for active lanes. An array of time values corresponding to each object coordinate may be provided; a NULL value indicates all times are zero.

void vklComputeSample4(const int *valid,
                       const VKLSampler *sampler,
                       const vkl_vvec3f4 *objectCoordinates,
                       float *samples,
                       unsigned int attributeIndex,
                       const float *times);

void vklComputeSample8(const int *valid,
                       const VKLSampler *sampler,
                       const vkl_vvec3f8 *objectCoordinates,
                       float *samples,
                       unsigned int attributeIndex,
                       const float *times);

void vklComputeSample16(const int *valid,
                        const VKLSampler *sampler,
                        const vkl_vvec3f16 *objectCoordinates,
                        float *samples,
                        unsigned int attributeIndex,
                        const float *times);

A stream version allows sampling an arbitrary number of positions at once. While the vector version requires coordinates to be provided in a structure-of-arrays layout, the stream version allows coordinates to be provided in an array-of-structures layout. Thus, the stream API can be used to avoid reformatting of data by the application. As with the vector versions, the stream API can give greater performance than the scalar API.

  void vklComputeSampleN(const VKLSampler *sampler,
                         unsigned int N,
                         const vkl_vec3f *objectCoordinates,
                         float *samples,
                         unsigned int attributeIndex,
                         const float *times);

All of the above sampling APIs can be used, regardless of the device’s native SIMD width.

Sampling Multiple Attributes

Open VKL provides additional APIs for sampling multiple scalar attributes in a single call through the vklComputeSampleM*() interfaces. Beyond convenience, these can give improved performance relative to the single attribute sampling APIs. As with the single attribute APIs, sampling time values may be specified; note that these are provided per object coordinate only (rather than separately per attribute).

A scalar API supports sampling M attributes specified by attributeIndices on a single object space coordinate:

For the cpu device, the scalar sampling API is:

void vklComputeSampleM(const VKLSampler *sampler,
                       const vkl_vec3f *objectCoordinates,
                       float *samples,
                       unsigned int M,
                       const unsigned int *attributeIndices,
                       float time);

while on the gpu device, it is:

void vklComputeSampleM(const VKLSampler *sampler,
                       const vkl_vec3f *objectCoordinates,
                       float *samples,
                       unsigned int M,
                       const unsigned int *attributeIndices,
                       float time,
                       const VKLFeatureFlags featureFlags);

Again, see section ‘Feature flag usage on GPU’ for details on feature flags.

Vector-wide and Stream-wide Multi-Attribute Sampling (CPU device only)

On the cpu device, vector-wide and stream-wide sampling APIs are also provided.

Vector versions allow sampling at 4, 8, or 16 positions at once across the M attributes:

void vklComputeSampleM4(const int *valid,
                        const VKLSampler *sampler,
                        const vkl_vvec3f4 *objectCoordinates,
                        float *samples,
                        unsigned int M,
                        const unsigned int *attributeIndices,
                        const float *times);

void vklComputeSampleM8(const int *valid,
                        const VKLSampler *sampler,
                        const vkl_vvec3f8 *objectCoordinates,
                        float *samples,
                        unsigned int M,
                        const unsigned int *attributeIndices,
                        const float *times);

void vklComputeSampleM16(const int *valid,
                         const VKLSampler *sampler,
                         const vkl_vvec3f16 *objectCoordinates,
                         float *samples,
                         unsigned int M,
                         const unsigned int *attributeIndices,
                         const float *times);

The [4, 8, 16] * M sampled values are populated in the samples array in a structure-of-arrays layout, with all values for each attribute provided in sequence. That is, sample values s_m,n for the mth attribute and nth object coordinate will be populated as

samples = [s_0,0,   s_0,1,   ..., s_0,N-1,
           s_1,0,   s_1,1,   ..., s_1,N-1,
           ...,
           s_M-1,0, s_M-1,1, ..., s_M-1,N-1]

A stream version allows sampling an arbitrary number of positions at once across the M attributes. As with single attribute stream sampling, the N coordinates are provided in an array-of-structures layout.

void vklComputeSampleMN(const VKLSampler *sampler,
                        unsigned int N,
                        const vkl_vec3f *objectCoordinates,
                        float *samples,
                        unsigned int M,
                        const unsigned int *attributeIndices,
                        const float *times);

The M * N sampled values are populated in the samples array in an array-of-structures layout, with all attribute values for each coordinate provided in sequence as

samples = [s_0,0,   s_1,0,   ..., s_M-1,0,
           s_0,1,   s_1,1,   ..., s_M-1,1,
           ...,
           s_0,N-1, s_1,N-1, ..., s_M-1,N-1]

All of the above sampling APIs can be used, regardless of the device’s native SIMD width.

Gradients

In a very similar API to vklComputeSample, vklComputeGradient queries the value gradient at an object space coordinate. Again, a scalar API, now returning a vec3f instead of a float. NaN values are returned for points outside the volume. The time value, which must be between 0 and 1, specifies the sampling time. For temporally constant volumes, this value has no effect.

For the cpu device, the scalar sampling API is:

vkl_vec3f vklComputeGradient(const VKLSampler *sampler,
                             const vkl_vec3f *objectCoordinates,
                             unsigned int attributeIndex,
                             float time);

while on the gpu device, it is:

vkl_vec3f vklComputeGradient(const VKLSampler *sampler,
                             const vkl_vec3f *objectCoordinates,
                             unsigned int attributeIndex,
                             float time,
                             const VKLFeatureFlags featureFlags);

Vector-wide and Stream-wide Gradients (CPU device only)

As with the sampling APIs, on the cpu device vector-wide and stream-wide gradient APIs are also provided.

The vector versions are:

void vklComputeGradient4(const int *valid,
                         const VKLSampler *sampler,
                         const vkl_vvec3f4 *objectCoordinates,
                         vkl_vvec3f4 *gradients,
                         unsigned int attributeIndex,
                         const float *times);

void vklComputeGradient8(const int *valid,
                         const VKLSampler *sampler,
                         const vkl_vvec3f8 *objectCoordinates,
                         vkl_vvec3f8 *gradients,
                         unsigned int attributeIndex,
                         const float *times);

void vklComputeGradient16(const int *valid,
                          const VKLSampler *sampler,
                          const vkl_vvec3f16 *objectCoordinates,
                          vkl_vvec3f16 *gradients,
                          unsigned int attributeIndex,
                          const float *times);

Finally, a stream version is provided:

void vklComputeGradientN(const VKLSampler *sampler,
                         unsigned int N,
                         const vkl_vec3f *objectCoordinates,
                         vkl_vec3f *gradients,
                         unsigned int attributeIndex,
                         const float *times);

All of the above gradient APIs can be used, regardless of the device’s native SIMD width.

Iterators

Open VKL has APIs to search for particular volume values along a ray. Queries can be for ranges of volume values (vklIterateInterval) or for particular values (vklIterateHit).

Interval iterators require a context object to define the sampler and parameters related to iteration behavior. An interval iterator context is created via

VKLIntervalIteratorContext vklNewIntervalIteratorContext(VKLSampler sampler);

The parameters understood by interval iterator contexts are defined in the table below.

Type	Name	Default	Description
int	attributeIndex	0	Defines the volume attribute of interest.
vkl_range1f[]	valueRanges	[-inf, inf]	Defines the value ranges of interest. Intervals not containing any of these values ranges may be skipped during iteration.
float	intervalResolutionHint	0.5	A value in the range [0, 1] affecting the resolution (size) of returned intervals. A value of 0 yields the lowest resolution (largest) intervals while 1 gives the highest resolution (smallest) intervals. This value is only a hint; it may not impact behavior for all volume types.

Configuration parameters for interval iterator contexts.

Most volume types support the intervalResolutionHint parameter that can impact the size of intervals returned duration iteration. These include amr, particle, structuredRegular, unstructured, and vdb volumes. In all cases a value of 1.0 yields the highest resolution (smallest) intervals possible, while a value of 0.0 gives the lowest resolution (largest) intervals. In general, smaller intervals will have tighter bounds on value ranges, and more efficient space skipping behavior than larger intervals, which can be beneficial for some rendering methods.

For structuredRegular, unstructured, and vdb volumes, a value of 1.0 will enable elementary cell iteration, such that each interval spans an individual voxel / cell intersection. Note that interval iteration can be significantly slower in this case.

As with other objects, the interval iterator context must be committed before being used.

To query an interval, a VKLIntervalIterator of scalar or vector width must be initialized with vklInitIntervalIterator. Time value(s) may be provided to specify the sampling time. These values must be between 0 and 1; for the vector versions, a NULL value indicates all times are zero. For temporally constant volumes, the time values have no effect.

On a gpu device, interval iterators may be initialized via:

VKLIntervalIterator vklInitIntervalIterator(const VKLIntervalIteratorContext *context,
                                            const vkl_vec3f *origin,
                                            const vkl_vec3f *direction,
                                            const vkl_range1f *tRange,
                                            float time,
                                            void *buffer,
                                            const VKLFeatureFlags featureFlags);

Note again the featureFlags argument; see section ’Feature flag usage on GPU` for details.

On a cpu device, interval iterators can be initialized via:

VKLIntervalIterator vklInitIntervalIterator(const VKLIntervalIteratorContext *context,
                                            const vkl_vec3f *origin,
                                            const vkl_vec3f *direction,
                                            const vkl_range1f *tRange,
                                            float time,
                                            void *buffer);

VKLIntervalIterator4 vklInitIntervalIterator4(const int *valid,
                                              const VKLIntervalIteratorContext *context,
                                              const vkl_vvec3f4 *origin,
                                              const vkl_vvec3f4 *direction,
                                              const vkl_vrange1f4 *tRange,
                                              const float *times,
                                              void *buffer);

VKLIntervalIterator8 vklInitIntervalIterator8(const int *valid,
                                              const VKLIntervalIteratorContext *context,
                                              const vkl_vvec3f8 *origin,
                                              const vkl_vvec3f8 *direction,
                                              const vkl_vrange1f8 *tRange,
                                              const float *times,
                                              void *buffer);

VKLIntervalIterator16 vklInitIntervalIterator16(const int *valid,
                                                const VKLIntervalIteratorContext *context,
                                                const vkl_vvec3f16 *origin,
                                                const vkl_vvec3f16 *direction,
                                                const vkl_vrange1f16 *tRange,
                                                const float *times,
                                                void *buffer);

Open VKL places the iterator struct into a user-provided buffer, and the returned handle is essentially a pointer into this buffer. This means that the iterator handle must not be used after the buffer ceases to exist. Copying iterator buffers is currently not supported.

The required size, in bytes, of the buffer can be queried with

size_t vklGetIntervalIteratorSize(const VKLIntervalIteratorContext *context);

size_t vklGetIntervalIteratorSize4(const VKLIntervalIteratorContext *context);

size_t vklGetIntervalIteratorSize8(const VKLIntervalIteratorContext *context);

size_t vklGetIntervalIteratorSize16(const VKLIntervalIteratorContext *context);

The values these functions return may change depending on the parameters set on sampler.

Open VKL also provides a conservative maximum size over all volume types as a preprocessor definition (VKL_MAX_INTERVAL_ITERATOR_SIZE). For ISPC use cases, Open VKL will attempt to detect the native vector width using TARGET_WIDTH, which is defined in recent versions of ISPC, to provide a less conservative size.

Intervals can then be processed by calling vklIterateInterval as long as the returned lane masks indicates that the iterator is still within the volume.

On a gpu device this is done via:

int vklIterateInterval(VKLIntervalIterator iterator,
                       VKLInterval *interval,
                       const VKLFeatureFlags featureFlags);

while on a cpu device, iteration is via:

int vklIterateInterval(VKLIntervalIterator iterator,
                       VKLInterval *interval);

void vklIterateInterval4(const int *valid,
                         VKLIntervalIterator4 iterator,
                         VKLInterval4 *interval,
                         int *result);

void vklIterateInterval8(const int *valid,
                         VKLIntervalIterator8 iterator,
                         VKLInterval8 *interval,
                         int *result);

void vklIterateInterval16(const int *valid,
                          VKLIntervalIterator16 iterator,
                          VKLInterval16 *interval,
                          int *result);

The intervals returned have a t-value range, a value range, and a nominalDeltaT which is approximately the step size (in units of ray direction) that should be used to walk through the interval, if desired. The number and length of intervals returned is volume type implementation dependent. There is currently no way of requesting a particular splitting.

typedef struct
{
  vkl_range1f tRange;
  vkl_range1f valueRange;
  float nominalDeltaT;
} VKLInterval;

typedef struct
{
  vkl_vrange1f4 tRange;
  vkl_vrange1f4 valueRange;
  float nominalDeltaT[4];
} VKLInterval4;

typedef struct
{
  vkl_vrange1f8 tRange;
  vkl_vrange1f8 valueRange;
  float nominalDeltaT[8];
} VKLInterval8;

typedef struct
{
  vkl_vrange1f16 tRange;
  vkl_vrange1f16 valueRange;
  float nominalDeltaT[16];
} VKLInterval16;

Querying for particular values is done using a VKLHitIterator in much the same fashion. This API could be used, for example, to find isosurfaces. As with interval iterators, time value(s) may be provided to specify the sampling time. These values must be between 0 and 1; for the vector versions, a NULL value indicates all times are zero. For temporally constant volumes, the time values have no effect.

Hit iterators similarly require a context object to define the sampler and other iteration parameters. A hit iterator context is created via

VKLHitIteratorContext vklNewHitIteratorContext(VKLSampler sampler);

The parameters understood by hit iterator contexts are defined in the table below.

Type	Name	Default	Description
int	attributeIndex	0	Defines the volume attribute of interest.
float[]	values		Defines the value(s) of interest.

Configuration parameters for hit iterator contexts.

The hit iterator context must be committed before being used.

Again, a user allocated buffer must be provided, and a VKLHitIterator of the desired width must be initialized.

On a gpu device this is done via:

VKLHitIterator vklInitHitIterator(VKLHitIteratorContext context,
                                  const vkl_vec3f *origin,
                                  const vkl_vec3f *direction,
                                  const vkl_range1f *tRange,
                                  float time,
                                  void *buffer,
                                  const VKLFeatureFlags featureFlags);

while on a cpu device initialization is via:

VKLHitIterator vklInitHitIterator(VKLHitIteratorContext context,
                                  const vkl_vec3f *origin,
                                  const vkl_vec3f *direction,
                                  const vkl_range1f *tRange,
                                  float time,
                                  void *buffer);

VKLHitIterator4 vklInitHitIterator4(const int *valid,
                         VKLHitIteratorContext context,
                         const vkl_vvec3f4 *origin,
                         const vkl_vvec3f4 *direction,
                         const vkl_vrange1f4 *tRange,
                         const float *times,
                         void *buffer);

VKLHitIterator8 vklInitHitIterator8(const int *valid,
                         VKLHitIteratorContext context,
                         const vkl_vvec3f8 *origin,
                         const vkl_vvec3f8 *direction,
                         const vkl_vrange1f8 *tRange,
                         const float *times,
                         void *buffer);

VKLHitIterator16 vklInitHitIterator16(const int *valid,
                          VKLHitIteratorContext context,
                          const vkl_vvec3f16 *origin,
                          const vkl_vvec3f16 *direction,
                          const vkl_vrange1f16 *tRange,
                          const float *times,
                          void *buffer);

Buffer size can be queried with

size_t vklGetHitIteratorSize(VKLHitIteratorContext context);

size_t vklGetHitIteratorSize4(VKLHitIteratorContext context);

size_t vklGetHitIteratorSize8(VKLHitIteratorContext context);

size_t vklGetHitIteratorSize16(VKLHitIteratorContext context);

Open VKL also provides the macro VKL_MAX_HIT_ITERATOR_SIZE as a conservative estimate.

Hits are then queried by looping a call to vklIterateHit as long as the returned lane mask indicates that the iterator is still within the volume.

On a gpu device, this is done via:

int vklIterateHit(VKLHitIterator iterator,
                  VKLHit *hit,
                  const VKLFeatureFlags featureFlags);

while on a cpu device, the APIs are:

int vklIterateHit(VKLHitIterator iterator, VKLHit *hit);

void vklIterateHit4(const int *valid,
                    VKLHitIterator4 iterator,
                    VKLHit4 *hit,
                    int *result);

void vklIterateHit8(const int *valid,
                    VKLHitIterator8 iterator,
                    VKLHit8 *hit,
                    int *result);

void vklIterateHit16(const int *valid,
                     VKLHitIterator16 iterator,
                     VKLHit16 *hit,
                     int *result);

Returned hits consist of a t-value, a volume value (equal to one of the requested values specified in the context), and an (object space) epsilon value estimating the error of the intersection:

typedef struct
{
  float t;
  float sample;
  float epsilon;
} VKLHit;

typedef struct
{
  float t[4];
  float sample[4];
  float epsilon[4];
} VKLHit4;

typedef struct
{
  float t[8];
  float sample[8];
  float epsilon[8];
} VKLHit8;

typedef struct
{
  float t[16];
  float sample[16];
  float epsilon[16];
} VKLHit16;

For both interval and hit iterators, only the vector-wide API for the native SIMD width (determined via vklGetNativeSIMDWidth can be called. The scalar versions are always valid. This restriction will likely be lifted in the future.

Observers

Volumes and samplers in Open VKL may provide observers to communicate data back to the application. Please note that observers are currently only allowed for the cpu device. Observers may be created with

VKLObserver vklNewSamplerObserver(VKLSampler sampler,
                                  const char *type);

VKLObserver vklNewVolumeObserver(VKLVolume volume,
                                 const char *type);

The object passed to vklNew*Observer must already be committed. Valid observer type strings are defined by volume implementations (see section ‘Volume types’ below).

vklNew*Observer returns NULL on failure.

To access the underlying data, an observer must first be mapped using

const void * vklMapObserver(VKLObserver observer);

If this fails, the function returns NULL. vklMapObserver may fail on observers that are already mapped. On success, the application may query the underlying type, element size in bytes, and the number of elements in the buffer using

VKLDataType vklGetObserverElementType(VKLObserver observer);
size_t vklGetObserverElementSize(VKLObserver observer);
size_t vklGetObserverNumElements(VKLObserver observer);

On failure, these functions return VKL_UNKNOWN and 0, respectively. Possible data types are defined by the volume that provides the observer , as are the semantics of the observation. See section ‘Volume types’ for details.

The pointer returned by vklMapObserver may be cast to the type corresponding to the value returned by vklGetObserverElementType to access the observation. For example, if vklGetObserverElementType returns VKL_FLOAT, then the pointer returned by vklMapObserver may be cast to const float * to access up to vklGetObserverNumElements consecutive values of type float.

Once the application has finished processing the observation, it should unmap the observer using

void vklUnmapObserver(VKLObserver observer);

so that the observer may be mapped again.

When an observer is no longer needed, it should be released using vklRelease.

The observer API is not thread safe, and these functions should not be called concurrently on the same object.

Performance Recommendations

Feature flag usage on GPU

Feature flags are used extensively throughout the device-side APIs defined by the gpu device. These flags identify the required feature set for a given volume and sampler, and are used internally by Open VKL to prune unnecessary code during just-in-time (JIT) compilation on the GPU. Thus, using feature flags can provide a significant performance gain on GPU, both for the one-time JIT compilation time and of course for kernel execution times.

Feature flags must be populated separately for each VKLSampler used in an application, via:

VKLFeatureFlags vklGetFeatureFlags(VKLSampler sampler);

The resulting VKLFeatureFlags can be passed to sampling, gradient, interval iterator, and hit iterator APIs.

MXCSR control and status register

It is strongly recommended to have the Flush to Zero and Denormals are Zero mode of the MXCSR control and status register enabled for each thread before calling the sampling, gradient, or interval API functions. Otherwise, under some circumstances special handling of denormalized floating point numbers can significantly reduce application and Open VKL performance. The device parameter flushDenormals or environment variable OPENVKL_FLUSH_DENORMALS can be used to toggle this mode; by default it is enabled. Alternatively, when using Open VKL together with the Intel® Threading Building Blocks, it is sufficient to execute the following code at the beginning of the application main thread (before the creation of the tbb::task_scheduler_init object):

#include <xmmintrin.h>
#include <pmmintrin.h>
...
_MM_SET_FLUSH_ZERO_MODE(_MM_FLUSH_ZERO_ON);
_MM_SET_DENORMALS_ZERO_MODE(_MM_DENORMALS_ZERO_ON);

If using a different tasking system, make sure each thread calling into Open VKL has the proper mode set.

Iterator Allocation

vklInitIntervalIterator and vklInitHitIterator expect a user allocated buffer. While this buffer can be allocated by any means, we expect iterators to be used in inner loops and advise against heap allocation in that case. Applications may provide high performance memory pools, but as a preferred alternative we recommend stack allocated buffers.

In C99, variable length arrays provide an easy way to achieve this:

const size_t bufferSize = vklGetIntervalIteratorSize(context);
char buffer[bufferSize];

Note that the call to vklGetIntervalIteratorSize or vklGetHitIteratorSize should not appear in an inner loop as it is relatively costly. The return value depends on the volume type, target architecture, and parameters to context.

In C++, variable length arrays are not part of the standard. Here, users may rely on alloca and similar functions:

#include <alloca.h>
const size_t bufferSize = vklGetIntervalIteratorSize(context);
void *buffer = alloca(bufferSize);

Similarly for ISPC, variable length arrays are not supported, but alloca may be used:

const uniform size_t bufferSize = vklGetIntervalIteratorSizeV(context);
void *uniform buffer = alloca(bufferSize);

Users should understand the implications of alloca. In particular, alloca does check available stack space and may result in stack overflow. buffer also becomes invalid at the end of the scope. As one consequence, it cannot be returned from a function. On Windows, _malloca is a safer option that performs additional error checking, but requires the use of _freea.

Applications may instead rely on the VKL_MAX_INTERVAL_ITERATOR_SIZE and VKL_MAX_HIT_ITERATOR_SIZE macros. For example, in ISPC:

uniform unsigned int8 buffer[VKL_MAX_INTERVAL_ITERATOR_SIZE];

These values are majorants over all devices and volume types. Note that Open VKL attempts to detect the target SIMD width using TARGET_WIDTH, returning smaller buffer sizes for narrow architectures. However, Open VKL may fall back to the largest buffer size over all targets.

Multi-attribute Volume Data Layout

Open VKL provides flexible managed data APIs that allow applications to specify input data in various formats and layouts. When shared buffers are used (dataCreationFlags = VKL_DATA_SHARED_BUFFER), Open VKL will use the application-owned memory directly, respecting the input data layout. Shared buffers therefore allow applications to strategically select the best layout for multi-attribute volume data and expected sampling behavior.

For volume attributes that are sampled individually (e.g. using vklComputeSample[4,8,16,N]()), it is recommended to use a structure-of-arrays layout. That is, each attribute’s data should be compact in contiguous memory. This can be accomplished by simply using Open VKL owned data objects (dataCreationFlags = VKL_DATA_DEFAULT), or by using a natural byteStride for shared buffers.

For volume attributes that are sampled simultaneously (e.g. using vklComputeSampleM[4,8,16,N]()), it is recommended to use an array-of-structures layout. That is, data for these attributes should be provided per voxel in a contiguous layout. This is accomplished using shared buffers for each attribute with appropriate byte strides. For example, for a three attribute structured volume representing a velocity field, the data can be provided as:

// used in Open VKL shared buffers, so must not be freed by application
std::vector<vkl_vec3f> velocities(numVoxels);

for (auto &v : velocities) {
  v.x = ...;
  v.y = ...;
  v.z = ...;
}

std::vector<VKLData> attributes;

attributes.push_back(vklNewData(device,
                                velocities.size(),
                                VKL_FLOAT,
                                &velocities[0].x,
                                VKL_DATA_SHARED_BUFFER,
                                sizeof(vkl_vec3f)));

attributes.push_back(vklNewData(device,
                                velocities.size(),
                                VKL_FLOAT,
                                &velocities[0].y,
                                VKL_DATA_SHARED_BUFFER,
                                sizeof(vkl_vec3f)));

attributes.push_back(vklNewData(device,
                                velocities.size(),
                                VKL_FLOAT,
                                &velocities[0].z,
                                VKL_DATA_SHARED_BUFFER,
                                sizeof(vkl_vec3f)));

VKLData attributesData =
    vklNewData(device, attributes.size(), VKL_DATA, attributes.data());

for (auto &attribute : attributes)
  vklRelease(attribute);

VKLVolume volume = vklNewVolume(device, "structuredRegular");

vklSetData(volume, "data", attributesData);
vklRelease(attributesData);

// set other volume parameters...

vklCommit(volume);

These are general recommendations for common scenarios; it is still recommended to evaluate performance of different volume data layouts for your application’s particular use case.

Examples

Open VKL ships with simple tutorial applications demonstrating the basic usage of the API, as well as full renderers showing recommended usage.

Tutorials

Simple tutorials can be found in the examples/ directory. These are:

vklTutorialCPU.c: usage of the C API with a CPU device
vklTutorialISPC.cpp and vklTutorialISPC.ispc : combined usage of the C and ISPC APIs with a CPU device
vklTutorialGPU.cpp: usage of the SYCL API with a GPU device

Interactive examples

Open VKL also ships with interactive example applications, vklExamples[CPU,GPU]. The interactive viewer demonstrates multiple example renderers including a path tracer, isosurface renderer (using hit iterators), and ray marcher. The viewer UI supports switching between renderers interactively.

For CPU, each renderer has both a C++ and ISPC implementation showing recommended API usage. These implementations are available in the examples/interactive/renderer/ directory. On GPU, the example renderers are written in SYCL.

vklTutorial source

For quick reference, the contents of vklTutorialCPU.c are shown below.

#include <openvkl/openvkl.h>
#include <openvkl/device/openvkl.h>
#include <stdio.h>

#if defined(_MSC_VER)
#include <malloc.h>   // _malloca
#include <windows.h>  // Sleep
#endif

void demoScalarAPI(VKLDevice device, VKLVolume volume)
{
  printf("demo of 1-wide API\n");

  VKLSampler sampler = vklNewSampler(volume);
  vklCommit(sampler);

  // bounding box
  vkl_box3f bbox = vklGetBoundingBox(volume);
  printf("\tbounding box\n");
  printf("\t\tlower = %f %f %f\n", bbox.lower.x, bbox.lower.y, bbox.lower.z);
  printf("\t\tupper = %f %f %f\n\n", bbox.upper.x, bbox.upper.y, bbox.upper.z);

  // number of attributes
  unsigned int numAttributes = vklGetNumAttributes(volume);
  printf("\tnum attributes = %d\n\n", numAttributes);

  // value range for all attributes
  for (unsigned int i = 0; i < numAttributes; i++) {
    vkl_range1f valueRange = vklGetValueRange(volume, i);
    printf("\tvalue range (attribute %u) = (%f %f)\n",
           i,
           valueRange.lower,
           valueRange.upper);
  }

  // coordinate for sampling / gradients
  vkl_vec3f coord = {1.f, 2.f, 3.f};
  printf("\n\tcoord = %f %f %f\n\n", coord.x, coord.y, coord.z);

  // sample, gradient (first attribute)
  unsigned int attributeIndex = 0;
  float time                  = 0.f;
  float sample   = vklComputeSample(&sampler, &coord, attributeIndex, time);
  vkl_vec3f grad = vklComputeGradient(&sampler, &coord, attributeIndex, time);
  printf("\tsampling and gradient computation (first attribute)\n");
  printf("\t\tsample = %f\n", sample);
  printf("\t\tgrad   = %f %f %f\n\n", grad.x, grad.y, grad.z);

  // sample (multiple attributes)
  unsigned int M                  = 3;
  unsigned int attributeIndices[] = {0, 1, 2};
  float samples[3];
  vklComputeSampleM(&sampler, &coord, samples, M, attributeIndices, time);
  printf("\tsampling (multiple attributes)\n");
  printf("\t\tsamples = %f %f %f\n\n", samples[0], samples[1], samples[2]);

  // interval iterator context setup
  vkl_range1f ranges[2] = {{10, 20}, {50, 75}};
  int num_ranges        = 2;
  VKLData rangesData =
      vklNewData(device, num_ranges, VKL_BOX1F, ranges, VKL_DATA_DEFAULT, 0);

  VKLIntervalIteratorContext intervalContext =
      vklNewIntervalIteratorContext(sampler);

  vklSetInt(intervalContext, "attributeIndex", attributeIndex);

  vklSetData(intervalContext, "valueRanges", rangesData);
  vklRelease(rangesData);

  vklCommit(intervalContext);

  // hit iterator context setup
  float values[2] = {32, 96};
  int num_values  = 2;
  VKLData valuesData =
      vklNewData(device, num_values, VKL_FLOAT, values, VKL_DATA_DEFAULT, 0);

  VKLHitIteratorContext hitContext = vklNewHitIteratorContext(sampler);

  vklSetInt(hitContext, "attributeIndex", attributeIndex);

  vklSetData(hitContext, "values", valuesData);
  vklRelease(valuesData);

  vklCommit(hitContext);

  // ray definition for iterators
  vkl_vec3f rayOrigin    = {0, 1, 1};
  vkl_vec3f rayDirection = {1, 0, 0};
  vkl_range1f rayTRange  = {0, 200};
  printf("\trayOrigin = %f %f %f\n", rayOrigin.x, rayOrigin.y, rayOrigin.z);
  printf("\trayDirection = %f %f %f\n",
         rayDirection.x,
         rayDirection.y,
         rayDirection.z);
  printf("\trayTRange = %f %f\n", rayTRange.lower, rayTRange.upper);

  // interval iteration. This is scoped
  {
    // Note: buffer will cease to exist at the end of this scope.
#if defined(_MSC_VER)
    // MSVC does not support variable length arrays, but provides a
    // safer version of alloca.
    char *buffer = _malloca(vklGetIntervalIteratorSize(&intervalContext));
#else
    char buffer[vklGetIntervalIteratorSize(&intervalContext)];
#endif
    VKLIntervalIterator intervalIterator = vklInitIntervalIterator(
        &intervalContext, &rayOrigin, &rayDirection, &rayTRange, time, buffer);

    printf("\n\tinterval iterator for value ranges {%f %f} {%f %f}\n",
           ranges[0].lower,
           ranges[0].upper,
           ranges[1].lower,
           ranges[1].upper);

    for (;;) {
      VKLInterval interval;
      int result = vklIterateInterval(intervalIterator, &interval);
      if (!result)
        break;
      printf(
          "\t\ttRange (%f %f)\n\t\tvalueRange (%f %f)\n\t\tnominalDeltaT "
          "%f\n\n",
          interval.tRange.lower,
          interval.tRange.upper,
          interval.valueRange.lower,
          interval.valueRange.upper,
          interval.nominalDeltaT);
    }
#if defined(_MSC_VER)
    _freea(buffer);
#endif
  }

  // hit iteration
  {
#if defined(_MSC_VER)
    // MSVC does not support variable length arrays, but provides a
    // safer version of alloca.
    char *buffer = _malloca(vklGetHitIteratorSize(&hitContext));
#else
    char buffer[vklGetHitIteratorSize(&hitContext)];
#endif
    VKLHitIterator hitIterator = vklInitHitIterator(
        &hitContext, &rayOrigin, &rayDirection, &rayTRange, time, buffer);

    printf("\thit iterator for values %f %f\n", values[0], values[1]);

    for (;;) {
      VKLHit hit;
      int result = vklIterateHit(hitIterator, &hit);
      if (!result)
        break;
      printf("\t\tt %f\n\t\tsample %f\n\t\tepsilon %f\n\n",
             hit.t,
             hit.sample,
             hit.epsilon);
    }
#if defined(_MSC_VER)
    _freea(buffer);
#endif
  }

  vklRelease(hitContext);
  vklRelease(intervalContext);
  vklRelease(sampler);
}

void demoVectorAPI(VKLVolume volume)
{
  printf("demo of 4-wide API (8- and 16- follow the same pattern)\n");

  VKLSampler sampler = vklNewSampler(volume);
  vklCommit(sampler);

  // structure-of-array layout
  vkl_vvec3f4 coord4;
  int valid[4];
  for (int i = 0; i < 4; i++) {
    coord4.x[i] = i * 3 + 0;
    coord4.y[i] = i * 3 + 1;
    coord4.z[i] = i * 3 + 2;
    valid[i]    = -1;  // valid mask: 0 = not valid, -1 = valid
  }

  for (int i = 0; i < 4; i++) {
    printf(
        "\tcoord[%d] = %f %f %f\n", i, coord4.x[i], coord4.y[i], coord4.z[i]);
  }

  // sample, gradient (first attribute)
  unsigned int attributeIndex = 0;
  float time4[4]              = {0.f};
  float sample4[4];
  vkl_vvec3f4 grad4;
  vklComputeSample4(valid, &sampler, &coord4, sample4, attributeIndex, time4);
  vklComputeGradient4(valid, &sampler, &coord4, &grad4, attributeIndex, time4);

  printf("\n\tsampling and gradient computation (first attribute)\n");

  for (int i = 0; i < 4; i++) {
    printf("\t\tsample[%d] = %f\n", i, sample4[i]);
    printf(
        "\t\tgrad[%d]   = %f %f %f\n", i, grad4.x[i], grad4.y[i], grad4.z[i]);
  }

  // sample (multiple attributes)
  unsigned int M                  = 3;
  unsigned int attributeIndices[] = {0, 1, 2};
  float samples[3 * 4];
  vklComputeSampleM4(
      valid, &sampler, &coord4, samples, M, attributeIndices, time4);

  printf("\n\tsampling (multiple attributes)\n");

  printf("\t\tsamples = ");

  for (unsigned int j = 0; j < M; j++) {
    printf("%f %f %f %f\n",
           samples[j * 4 + 0],
           samples[j * 4 + 1],
           samples[j * 4 + 2],
           samples[j * 4 + 3]);
    printf("\t\t          ");
  }

  printf("\n");

  vklRelease(sampler);
}

void demoStreamAPI(VKLVolume volume)
{
  printf("demo of stream API\n");

  VKLSampler sampler = vklNewSampler(volume);
  vklCommit(sampler);

  // array-of-structure layout; arbitrary stream lengths are supported
  vkl_vec3f coord[5];

  for (int i = 0; i < 5; i++) {
    coord[i].x = i * 3 + 0;
    coord[i].y = i * 3 + 1;
    coord[i].z = i * 3 + 2;
  }

  for (int i = 0; i < 5; i++) {
    printf("\tcoord[%d] = %f %f %f\n", i, coord[i].x, coord[i].y, coord[i].z);
  }

  // sample, gradient (first attribute)
  printf("\n\tsampling and gradient computation (first attribute)\n");
  unsigned int attributeIndex = 0;
  float time[5]               = {0.f};
  float sample[5];
  vkl_vec3f grad[5];
  vklComputeSampleN(&sampler, 5, coord, sample, attributeIndex, time);
  vklComputeGradientN(&sampler, 5, coord, grad, attributeIndex, time);

  for (int i = 0; i < 5; i++) {
    printf("\t\tsample[%d] = %f\n", i, sample[i]);
    printf("\t\tgrad[%d]   = %f %f %f\n", i, grad[i].x, grad[i].y, grad[i].z);
  }

  // sample (multiple attributes)
  unsigned int M                  = 3;
  unsigned int attributeIndices[] = {0, 1, 2};
  float samples[3 * 5];
  vklComputeSampleMN(&sampler, 5, coord, samples, M, attributeIndices, time);

  printf("\n\tsampling (multiple attributes)\n");

  printf("\t\tsamples = ");

  for (int i = 0; i < 5; i++) {
    for (unsigned int j = 0; j < M; j++) {
      printf("%f ", samples[i * M + j]);
    }
    printf("\n\t\t          ");
  }

  printf("\n");

  vklRelease(sampler);
}

int main()
{
  vklInit();

  VKLDevice device = vklNewDevice("cpu");
  vklCommitDevice(device);

  const int dimensions[] = {128, 128, 128};

  const int numVoxels = dimensions[0] * dimensions[1] * dimensions[2];

  const int numAttributes = 3;

  VKLVolume volume = vklNewVolume(device, "structuredRegular");
  vklSetVec3i(
      volume, "dimensions", dimensions[0], dimensions[1], dimensions[2]);
  vklSetVec3f(volume, "gridOrigin", 0, 0, 0);
  vklSetVec3f(volume, "gridSpacing", 1, 1, 1);

  float *voxels = malloc(numVoxels * sizeof(float));

  if (!voxels) {
    printf("failed to allocate voxel memory!\n");
    return 1;
  }

  // volume attribute 0: x-grad
  for (int k = 0; k < dimensions[2]; k++)
    for (int j = 0; j < dimensions[1]; j++)
      for (int i = 0; i < dimensions[0]; i++)
        voxels[k * dimensions[0] * dimensions[1] + j * dimensions[2] + i] =
            (float)i;

  VKLData data0 =
      vklNewData(device, numVoxels, VKL_FLOAT, voxels, VKL_DATA_DEFAULT, 0);

  // volume attribute 1: y-grad
  for (int k = 0; k < dimensions[2]; k++)
    for (int j = 0; j < dimensions[1]; j++)
      for (int i = 0; i < dimensions[0]; i++)
        voxels[k * dimensions[0] * dimensions[1] + j * dimensions[2] + i] =
            (float)j;

  VKLData data1 =
      vklNewData(device, numVoxels, VKL_FLOAT, voxels, VKL_DATA_DEFAULT, 0);

  // volume attribute 2: z-grad
  for (int k = 0; k < dimensions[2]; k++)
    for (int j = 0; j < dimensions[1]; j++)
      for (int i = 0; i < dimensions[0]; i++)
        voxels[k * dimensions[0] * dimensions[1] + j * dimensions[2] + i] =
            (float)k;

  VKLData data2 =
      vklNewData(device, numVoxels, VKL_FLOAT, voxels, VKL_DATA_DEFAULT, 0);

  VKLData attributes[] = {data0, data1, data2};

  VKLData attributesData = vklNewData(
      device, numAttributes, VKL_DATA, attributes, VKL_DATA_DEFAULT, 0);

  vklRelease(data0);
  vklRelease(data1);
  vklRelease(data2);

  vklSetData(volume, "data", attributesData);
  vklRelease(attributesData);

  vklCommit(volume);

  demoScalarAPI(device, volume);
  demoVectorAPI(volume);
  demoStreamAPI(volume);

  vklRelease(volume);

  vklReleaseDevice(device);

  free(voxels);

  printf("complete.\n");

#if defined(_MSC_VER)
  // On Windows, sleep for a few seconds so the terminal window doesn't close
  // immediately.
  Sleep(3000);
#endif

  return 0;
}

Packages

Precompiled Open VKL packages for Linux, macOS, and Windows are available via Open VKL GitHub releases. Packages with “sycl” in the name include support for both x86 CPUs and Intel® GPUs, while the other packages only include x86 CPU support. Open VKL can be compiled from source (needed for ARM platforms) following the compilation instructions below.

GPU Runtime Requirements

To run Open VKL on Intel® GPUs you will need to first have drivers installed on your system.

GPU drivers on Linux

Install the latest GPGPU drivers for your Intel® GPU from: https://dgpu-docs.intel.com/. Follow the driver installation instructions for your graphics card.

GPU drivers on Windows

Install the latest GPGPU drivers for your Intel® GPU from: https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html. Follow the driver installation instructions for your graphics card.

Building Open VKL from source

The latest Open VKL sources are always available at the Open VKL GitHub repository. The default master branch should always point to the latest tested bugfix release.

Prerequisites

Open VKL currently supports Linux, Mac OS X, and Windows on CPU; and Linux and Windows on Intel® GPUs. Before you can build Open VKL you need the following prerequisites:

You can clone the latest Open VKL sources via:

git clone https://github.com/openvkl/openvkl.git

To build Open VKL you need CMake, any form of C++11 compiler (we recommend using GCC, but also support Clang and MSVC), and standard Linux development tools. To build the examples, you should also have some version of OpenGL.
Additionally you require a copy of the Intel® Implicit SPMD Program Compiler (Intel® ISPC), version 1.18.0 or later. Please obtain a release of ISPC from the ISPC downloads page.
Open VKL depends on the Intel RenderKit common library, rkcommon. rkcommon is available at the rkcommon GitHub repository.
Open VKL depends on Embree, which is available at the Embree GitHub repository.

Depending on your Linux distribution you can install these dependencies using yum or apt-get. Some of these packages might already be installed or might have slightly different names.

GPU-specific Prerequisites

In addition, if you would like to build Open VKL for Intel® GPUs on Linux or Windows, you need the following additional prerequisites:

CMake version 3.25.3 or higher
Download the oneAPI DPC++ Compiler 2023-10-26; please note this specific version has been validated and used in our releases.
- On Linux, the compiler can be simply extracted, then set up using the following commands in bash (where path_to_dpcpp_compiler should point to the root directory of unpacked package):
```
export SYCL_BUNDLE_ROOT=path_to_dpcpp_compiler
export PATH=$SYCL_BUNDLE_ROOT/bin:$PATH
export CPATH=$SYCL_BUNDLE_ROOT/include:$CPATH
export LIBRARY_PATH=$SYCL_BUNDLE_ROOT/lib:$LIBRARY_PATH
export LD_LIBRARY_PATH=$SYCL_BUNDLE_ROOT/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=$SYCL_BUNDLE_ROOT/linux/lib/x64:$LD_LIBRARY_PATH
```
- On Windows, you will also need an installed version of Visual Studio that supports the C++17 standard, e.g. Visual Studio 2019. Then, download and unpack the DPC++ compiler package and open the “x64 Native Tools Command Prompt” of Visual Studio. Execute the following lines to properly configure the environment to use the oneAPI DPC++ compiler (where path_to_dpcpp_compiler should point to the root directory of unpacked package):
```
set "DPCPP_DIR=path_to_dpcpp_compiler"
set "PATH=%DPCPP_DIR%\bin;%PATH%"
set "PATH=%DPCPP_DIR%\lib;%PATH%"
set "CPATH=%DPCPP_DIR%\include;%CPATH%"
set "INCLUDE=%DPCPP_DIR%\include;%INCLUDE%"
set "LIB=%DPCPP_DIR%\lib;%LIB%"
```

CMake Superbuild

For convenience, Open VKL provides a CMake Superbuild script which will pull down Open VKL’s dependencies and build Open VKL itself. The result is an install directory, with each dependency in its own directory.

Run with:

mkdir build
cd build
cmake [<VKL_ROOT>/superbuild]
cmake --build .

If you wish to enable GPU support, additional flags must be passed to the superbuild. On Linux:

```
export CC=clang
export CXX=clang++

cmake -D OPENVKL_EXTRA_OPTIONS="-DOPENVKL_ENABLE_DEVICE_GPU=ON" \
  [<VKL_ROOT>/superbuild]
```

And on Windows:

```
cmake -L -G Ninja \
  -D CMAKE_CXX_COMPILER=clang-cl -D CMAKE_C_COMPILER=clang-cl \
  -D OPENVKL_EXTRA_OPTIONS="-DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++  -DOPENVKL_ENABLE_DEVICE_GPU=ON" \
   [<VKL_ROOT>/superbuild]
```

The resulting install directory (or the one set with CMAKE_INSTALL_PREFIX) will have everything in it, with one subdirectory per dependency.

CMake options to note (all have sensible defaults):

CMAKE_INSTALL_PREFIX will be the root directory where everything gets installed.
BUILD_JOBS sets the number given to make -j for parallel builds.
INSTALL_IN_SEPARATE_DIRECTORIES toggles installation of all libraries in separate or the same directory.
BUILD_TBB_FROM_SOURCE specifies whether TBB should be built from source or the releases on Gitub should be used. This must be ON when compiling for ARM.
OPENVKL_ENABLE_DEVICE_GPU specifies if GPU support should be enabled. Note this defaults to OFF.
For the full set of options, run ccmake [<VKL_ROOT>/superbuild].

Standard CMake build

Assuming the above prerequisites are all fulfilled, building Open VKL through CMake is easy:

Create a build directory, and go into it
```
mkdir openvkl/build
cd openvkl/build
```
(We do recommend having separate build directories for different configurations such as release, debug, etc.).
The compiler CMake will use will default to whatever the CC and CXX environment variables point to. Should you want to specify a different compiler, run cmake manually while specifying the desired compiler. The default compiler on most linux machines is gcc, but it can be pointed to clang instead by executing the following:
```
cmake -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_C_COMPILER=clang ..
```
CMake will now use Clang instead of GCC. If you are ok with using the default compiler on your system, then simply skip this step. Note that the compiler variables cannot be changed after the first cmake or ccmake run.
Open the CMake configuration dialog
```
ccmake ..
```
Make sure to properly set build mode and enable the components you need, etc.; then type ’c’onfigure and ’g’enerate. When back on the command prompt, build it using
```
make
```
You should now have libopenvkl.so as well as the tutorial / example applications.

Projects that make use of Open VKL

This page gives a brief (and incomplete) list of other projects that make use of Open VKL, as well as a set of related links to other projects and related information.

If you have a project that makes use of Open VKL and would like this to be listed here, please let us know.

Intel® OSPRay, a ray tracing based rendering engine for high-fidelity visualization

Projects that are closely related to Open VKL

The Intel® oneAPI Rendering Toolkit
The Intel® Embree Ray Tracing Kernel Framework

Name		Name	Last commit message	Last commit date
Latest commit History 1,573 Commits
.github		.github
cmake		cmake
doc		doc
examples		examples
gitlab		gitlab
openvkl		openvkl
superbuild		superbuild
testing		testing
utility		utility
.clang-format		.clang-format
.gitattributes		.gitattributes
.gitignore		.gitignore
.gitmodules		.gitmodules
CHANGELOG.md		CHANGELOG.md
CMakeLists.txt		CMakeLists.txt
LICENSE.txt		LICENSE.txt
README.md		README.md
SECURITY.md		SECURITY.md
third-party-programs-Embree.txt		third-party-programs-Embree.txt
third-party-programs-OSPRay.txt		third-party-programs-OSPRay.txt
third-party-programs-TBB.txt		third-party-programs-TBB.txt
third-party-programs.txt		third-party-programs.txt

License

RenderKit/openvkl

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Intel® Open Volume Kernel Library

Overview

Version History

Open VKL 2.0.1

Open VKL 2.0.0

Open VKL 1.3.2

Open VKL 1.3.1

Open VKL 1.3.0

Open VKL 1.2.0

Open VKL 1.1.0

Open VKL 1.0.1

Open VKL 1.0.0

Open VKL 0.13.0

Open VKL 0.12.1

Open VKL 0.12.0

Open VKL 0.11.0

Open VKL 0.10.0 (alpha)

Open VKL 0.9.0 (alpha)

Open VKL 0.8.0 (alpha)

Open VKL 0.7.0 (alpha)

Support and Contact

Open VKL API

Device initialization and shutdown

Environment variables

Error handling and log messages

Basic data types

Object model

Managed data

Volume types

Structured Volumes

Structured Regular Volumes

Reconstruction filters

Structured Spherical Volumes

Adaptive Mesh Refinement (AMR) Volumes

Unstructured Volumes

VDB Volumes

Reconstruction filters

Major differences to OpenVDB

Loading OpenVDB .vdb files

Particle Volumes

Temporal Variation

Sampler Objects

Sampling

Vector-wide and Stream-wide Sampling (CPU device only)

Sampling Multiple Attributes

Vector-wide and Stream-wide Multi-Attribute Sampling (CPU device only)

Gradients

Vector-wide and Stream-wide Gradients (CPU device only)

Iterators

Observers

Performance Recommendations

Feature flag usage on GPU

MXCSR control and status register

Iterator Allocation

Multi-attribute Volume Data Layout

Examples

Tutorials

Interactive examples

vklTutorial source

Packages

GPU Runtime Requirements

GPU drivers on Linux

GPU drivers on Windows

Building Open VKL from source

Prerequisites

GPU-specific Prerequisites

CMake Superbuild

Standard CMake build

Projects that make use of Open VKL

Projects that are closely related to Open VKL

About

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