Merge branch 'branch-23.12' into cuda-120-arm

python/benchmarks/zarr-io.py

@@ -0,0 +1,324 @@
+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+# See file LICENSE for terms.
+
+import argparse
+import contextlib
+import os
+import os.path
+import pathlib
+import shutil
+import statistics
+import subprocess
+import tempfile
+from time import perf_counter as clock
+from typing import ContextManager, Union
+
+import cupy
+import numcodecs.blosc
+import numpy
+import zarr
+from dask.utils import format_bytes, parse_bytes
+
+import kvikio
+import kvikio.defaults
+import kvikio.zarr
+
+if not kvikio.zarr.supported:
+    raise RuntimeError(f"requires Zarr >={kvikio.zarr.MINIMUM_ZARR_VERSION}")
+
+compressors = {
+    "none": (None, None),
+    "lz4": (numcodecs.blosc.Blosc(cname="lz4"), kvikio.zarr.LZ4()),
+}
+
+
+def drop_vm_cache(args):
+    if args.drop_vm_cache:
+        subprocess.check_output(["sudo /sbin/sysctl vm.drop_caches=3"], shell=True)
+
+
+def create_src_data(args):
+    return cupy.random.random(args.nelem, dtype=args.dtype)
+
+
+def run_kvikio(args):
+    dir_path = args.dir / "kvikio"
+    shutil.rmtree(str(dir_path), ignore_errors=True)
+
+    # Get the GPU compressor
+    compressor = compressors[args.compressor][1]
+
+    src = create_src_data(args)
+
+    # Write
+    drop_vm_cache(args)
+    t0 = clock()
+    z = zarr.create(
+        shape=(args.nelem,),
+        chunks=(args.chunksize,),
+        dtype=args.dtype,
+        compressor=compressor,
+        store=kvikio.zarr.GDSStore(dir_path),
+        meta_array=cupy.empty(()),
+    )
+    z[:] = src
+    os.sync()
+    write_time = clock() - t0
+
+    # Read
+    drop_vm_cache(args)
+    t0 = clock()
+    res = z[:]
+    read_time = clock() - t0
+    assert res.nbytes == args.nbytes
+
+    return read_time, write_time
+
+
+def run_posix(args):
+    dir_path = args.dir / "posix"
+    shutil.rmtree(str(dir_path), ignore_errors=True)
+
+    # Get the CPU compressor
+    compressor = compressors[args.compressor][0]
+
+    src = create_src_data(args)
+
+    # Write
+    drop_vm_cache(args)
+    t0 = clock()
+    z = zarr.create(
+        shape=(args.nelem,),
+        chunks=(args.chunksize,),
+        dtype=args.dtype,
+        compressor=compressor,
+        store=zarr.DirectoryStore(dir_path),
+        meta_array=numpy.empty(()),
+    )
+    z[:] = src.get()
+    os.sync()
+    write_time = clock() - t0
+
+    # Read
+    drop_vm_cache(args)
+    t0 = clock()
+    res = cupy.asarray(z[:])
+    read_time = clock() - t0
+    assert res.nbytes == args.nbytes
+
+    return read_time, write_time
+
+
+API = {
+    "kvikio": run_kvikio,
+    "posix": run_posix,
+}
+
+
+def main(args):
+    cupy.cuda.set_allocator(None)  # Disable CuPy's default memory pool
+    cupy.arange(10)  # Make sure CUDA is initialized
+
+    kvikio.defaults.num_threads_reset(args.nthreads)
+    props = kvikio.DriverProperties()
+    try:
+        import pynvml.smi
+
+        nvsmi = pynvml.smi.nvidia_smi.getInstance()
+    except ImportError:
+        gpu_name = "Unknown (install pynvml)"
+        mem_total = gpu_name
+        bar1_total = gpu_name
+    else:
+        info = nvsmi.DeviceQuery()["gpu"][0]
+        gpu_name = f"{info['product_name']} (dev #0)"
+        mem_total = format_bytes(
+            parse_bytes(
+                str(info["fb_memory_usage"]["total"]) + info["fb_memory_usage"]["unit"]
+            )
+        )
+        bar1_total = format_bytes(
+            parse_bytes(
+                str(info["bar1_memory_usage"]["total"])
+                + info["bar1_memory_usage"]["unit"]
+            )
+        )
+    gds_version = "N/A (Compatibility Mode)"
+    if props.is_gds_available:
+        gds_version = f"v{props.major_version}.{props.minor_version}"
+    gds_config_json_path = os.path.realpath(
+        os.getenv("CUFILE_ENV_PATH_JSON", "/etc/cufile.json")
+    )
+    drop_vm_cache_msg = str(args.drop_vm_cache)
+    if not args.drop_vm_cache:
+        drop_vm_cache_msg += " (use --drop-vm-cache for better accuracy!)"
+    chunksize = args.chunksize * args.dtype.itemsize
+
+    print("Roundtrip benchmark")
+    print("----------------------------------")
+    if kvikio.defaults.compat_mode():
+        print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+        print("   WARNING - KvikIO compat mode   ")
+        print("      libcufile.so not used       ")
+        print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+    elif not props.is_gds_available:
+        print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+        print("   WARNING - cuFile compat mode   ")
+        print("         GDS not enabled          ")
+        print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+    print(f"GPU               | {gpu_name}")
+    print(f"GPU Memory Total  | {mem_total}")
+    print(f"BAR1 Memory Total | {bar1_total}")
+    print(f"GDS driver        | {gds_version}")
+    print(f"GDS config.json   | {gds_config_json_path}")
+    print("----------------------------------")
+    print(f"nbytes            | {args.nbytes} bytes ({format_bytes(args.nbytes)})")
+    print(f"chunksize         | {chunksize} bytes ({format_bytes(chunksize)})")
+    print(f"4K aligned        | {args.nbytes % 4096 == 0}")
+    print(f"drop-vm-cache     | {drop_vm_cache_msg}")
+    print(f"directory         | {args.dir}")
+    print(f"nthreads          | {args.nthreads}")
+    print(f"nruns             | {args.nruns}")
+    print(f"compressor        | {args.compressor}")
+    print("==================================")
+
+    # Run each benchmark using the requested APIs
+    for api in args.api:
+        rs = []
+        ws = []
+        for _ in range(args.n_warmup_runs):
+            read, write = API[api](args)
+        for _ in range(args.nruns):
+            read, write = API[api](args)
+            rs.append(args.nbytes / read)
+            ws.append(args.nbytes / write)
+
+        def pprint_api_res(name, samples):
+            mean = statistics.mean(samples) if len(samples) > 1 else samples[0]
+            ret = f"{api} {name}".ljust(18)
+            ret += f"| {format_bytes(mean).rjust(10)}/s".ljust(14)
+            if len(samples) > 1:
+                stdev = statistics.stdev(samples) / mean * 100
+                ret += " ± %5.2f %%" % stdev
+                ret += " ("
+                for sample in samples:
+                    ret += f"{format_bytes(sample)}/s, "
+                ret = ret[:-2] + ")"  # Replace trailing comma
+            return ret
+
+        print(pprint_api_res("read", rs))
+        print(pprint_api_res("write", ws))
+
+
+if __name__ == "__main__":
+
+    def parse_directory(x):
+        if x is None:
+            return x
+        else:
+            p = pathlib.Path(x)
+            if not p.is_dir():
+                raise argparse.ArgumentTypeError("Must be a directory")
+            return p
+
+    parser = argparse.ArgumentParser(description="Roundtrip benchmark")
+    parser.add_argument(
+        "-n",
+        "--nbytes",
+        metavar="BYTES",
+        default="10 MiB",
+        type=parse_bytes,
+        help="Message size, which must be a multiple of 8 (default: %(default)s).",
+    )
+    parser.add_argument(
+        "--chunksize",
+        metavar="BYTES",
+        default="10 MiB",
+        type=parse_bytes,
+        help="Chunk size (default: %(default)s).",
+    )
+    parser.add_argument(
+        "--dtype",
+        default="float32",
+        type=numpy.dtype,
+        help="NumPy datatype to use (default: '%(default)s')",
+    )
+    parser.add_argument(
+        "-d",
+        "--dir",
+        metavar="PATH",
+        default=None,
+        type=parse_directory,
+        help="Path to the directory to r/w from (default: tempfile.TemporaryDirectory)",
+    )
+    parser.add_argument(
+        "--nruns",
+        metavar="RUNS",
+        default=1,
+        type=int,
+        help="Number of runs per API (default: %(default)s).",
+    )
+    parser.add_argument(
+        "--n-warmup-runs",
+        default=0,
+        type=int,
+        help="Number of warmup runs (default: %(default)s).",
+    )
+    parser.add_argument(
+        "-t",
+        "--nthreads",
+        metavar="THREADS",
+        default=1,
+        type=int,
+        help="Number of threads to use (default: %(default)s).",
+    )
+    parser.add_argument(
+        "--api",
+        metavar="API",
+        default=("kvikio", "posix"),
+        nargs="+",
+        choices=tuple(API.keys()) + ("all",),
+        help="List of APIs to use {%(choices)s}",
+    )
+    parser.add_argument(
+        "--compressor",
+        metavar="COMPRESSOR",
+        default="none",
+        choices=tuple(compressors.keys()),
+        help=(
+            "Set a nvCOMP compressor to use with Zarr "
+            "{%(choices)s} (default: %(default)s)"
+        ),
+    )
+    parser.add_argument(
+        "--drop-vm-cache",
+        action="store_true",
+        default=False,
+        help=(
+            "Drop the VM cache between writes and reads, "
+            "requires sudo access to /sbin/sysctl"
+        ),
+    )
+
+    args = parser.parse_args()
+    if "all" in args.api:
+        args.api = tuple(API.keys())
+
+    # Check if size is divisible by size of datatype
+    assert args.nbytes % args.dtype.itemsize == 0
+    assert args.chunksize % args.dtype.itemsize == 0
+
+    # Compute/convert to number of elements
+    args.nelem = args.nbytes // args.dtype.itemsize
+    args.chunksize = args.chunksize // args.dtype.itemsize
+
+    # Create a temporary directory if user didn't specify a directory
+    temp_dir: Union[tempfile.TemporaryDirectory, ContextManager]
+    if args.dir is None:
+        temp_dir = tempfile.TemporaryDirectory()
+        args.dir = pathlib.Path(temp_dir.name)
+    else:
+        temp_dir = contextlib.nullcontext()
+
+    with temp_dir:
+        main(args)