This topic describes the benefits of cGPU Professional Edition by comparing it with cGPU Basic Edition.
Benefits of cGPU Professional Edition
|Supports graphics processing unit (GPU) sharing and memory isolation.||
|Supports flexible GPU sharing and memory isolation policies.||
|Supports comprehensive monitoring on GPU resources.||Supports monitoring on both exclusive GPUs and shared GPUs.|
Comparison between cGPU Basic Edition and cGPU Professional Edition
|cGPU Basic Edition||cGPU Professional Edition|
|Supports GPU sharing on one GPU.||Supports GPU sharing on one GPU and multiple GPUs.|
|Supports memory isolation on one GPU.||Supports memory isolation on one GPU and multiple GPUs.|
|Supports monitoring and auto scaling for exclusive GPUs and shared GPUs.||Supports monitoring and auto scaling for exclusive GPUs and shared GPUs.|
|By default, GPUs are scheduled by using the binpack algorithm.||Supports GPU scheduling by using the binpack and spread algorithms. You can choose binpack or spread based on your business requirements.|
|Allows you to create different GPU scheduling policies for a node pool. You can enable GPU sharing with or without memory isolation for a node pool.||Allows you to create different GPU scheduling policies for a node pool. You can enable GPU sharing with or without memory isolation for a node pool. In addition, you can use the binpack or spread algorithm to schedule GPUs.|
The key of GPU sharing among multiple pods is to isolate the GPU memory and computing power that are allocated to each pod. When you run multiple containers on one GPU, GPU resources are allocated to each container as required. However, if one container occupies excessive GPU resources, the performance of other containers may be affected. To solve this issue, many solutions have been developed in the computing industry. Technologies such as NVIDIA virtual GPU (vGPU), NVIDIA Multi-process Service (MPS), rCUDA, and vCUDA all contribute to the use of fine-grained GPU resource allocation.
- High compatibility: cGPU is compatible with standard open source solutions, such as Kubernetes and NVIDIA Docker.
- Ease-of-use: cGPU adopts a user-friendly design. To replace a Compute Unified Device Architecture (CUDA) library of an artificial intelligence (AI) application, you do not need to re-compile the application or create a new container image.
- Stability: cGPU provides stable underlying operations on NVIDIA GPUs. API operations on CUDA libraries and some private API operations on cuDNN are difficult to call.
- Resource isolation: cGPU ensures that the allocated GPU memory and computing capacity do not affect each other.
Based on cGPU, ACK enables GPU sharing and the scheduling of multiple tasks to one GPU. This enables GPU sharing and memory isolation for scheduled Kubernetes resources and the container runtime. This provides low-cost, reliable, and user-friendly GPU sharing and memory isolation for large scale business.