Fix links to operator-framework intro and add arch labels to csv

Author: shivamerla

Diff for: CONTRIBUTING.md

@@ -8,7 +8,7 @@ However, managing special hardware resources in Kubernetes is difficult and pron
 
 Moreover, NVIDIA software components such as drivers have been traditionally deployed as part of the base operating system image. This meant that there was a different image for CPU vs. GPU nodes that infrastructure teams would have to manage as part of the software lifecycle. This in turn requires sophisticated automation as part of the provisioning phase for GPU nodes in Kubernetes.
 
-The NVIDIA GPU Operator was primarily built to address these challenges. It leverages the standard [Operator Framework](https://coreos.com/blog/introducing-operator-framework) within Kubernetes to automate the management of all NVIDIA software components needed to provision GPUs within Kubernetes.
+The NVIDIA GPU Operator was primarily built to address these challenges. It leverages the standard [Operator Framework](https://cloud.redhat.com/blog/introducing-the-operator-framework) within Kubernetes to automate the management of all NVIDIA software components needed to provision GPUs within Kubernetes.
 
 The NVIDIA GPU Operator is an open-source product built and maintained by NVIDIA. It is currently validated on a set of platforms (including specific NVIDIA GPUs, operating systems and deployment configurations). The purpose of this document is to briefly describe the architecture of the GPU Operator, so that partners can extend the GPU Operator to support other platforms.
 

Diff for: README.md

@@ -7,7 +7,7 @@
 ![nvidia-gpu-operator](https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/egx/[email protected])
 
 Kubernetes provides access to special hardware resources such as NVIDIA GPUs, NICs, Infiniband adapters and other devices through the [device plugin framework](https://kubernetes.io/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/). However, configuring and managing nodes with these hardware resources requires configuration of multiple software components such as drivers, container runtimes or other libraries which  are difficult and prone to errors.
-The NVIDIA GPU Operator uses the [operator framework](https://coreos.com/blog/introducing-operator-framework) within Kubernetes to automate the management of all NVIDIA software components needed to provision GPU. These components include the NVIDIA drivers (to enable CUDA), Kubernetes device plugin for GPUs, the NVIDIA Container Runtime, automatic node labelling, [DCGM](https://developer.nvidia.com/dcgm) based monitoring and others.
+The NVIDIA GPU Operator uses the [operator framework](https://cloud.redhat.com/blog/introducing-the-operator-framework) within Kubernetes to automate the management of all NVIDIA software components needed to provision GPU. These components include the NVIDIA drivers (to enable CUDA), Kubernetes device plugin for GPUs, the NVIDIA Container Runtime, automatic node labelling, [DCGM](https://developer.nvidia.com/dcgm) based monitoring and others.
 
 ## Audience and Use-Cases
 The GPU Operator allows administrators of Kubernetes clusters to manage GPU nodes just like CPU nodes in the cluster. Instead of provisioning a special OS image for GPU nodes, administrators can rely on a standard OS image for both CPU and GPU nodes and then rely on the GPU Operator to provision the required software components for GPUs.

Diff for: bundle/manifests/gpu-operator.clusterserviceversion.yaml

@@ -1,6 +1,9 @@
 apiVersion: operators.coreos.com/v1alpha1
 kind: ClusterServiceVersion
 metadata:
+  labels:
+    operatorframework.io/arch.arm64: supported
+    operatorframework.io/arch.amd64: supported
   annotations:
     operators.openshift.io/infrastructure-features: '["Disconnected"]'
     olm.skipRange: '>=v1.9.0 <v1.10.1'
@@ -344,7 +347,7 @@ spec:
     errors.
 
     The NVIDIA GPU Operator uses the [operator
-    framework](https://coreos.com/blog/introducing-operator-framework) within
+    framework](https://cloud.redhat.com/blog/introducing-the-operator-framework) within
     Kubernetes to automate the management of all NVIDIA software components
     needed to provision and monitor GPUs.
     These components include the NVIDIA drivers (to enable CUDA), Kubernetes