GPUs Are What Drive High Performance Computing Today
Through GPU sharing, IT teams can expect more efficient GPU utilization. GPU sharing ultimately enables multiple users or applications to share the computational resources of a single GPU, thus increasing efficiency. GPU sharing is commonly used in cloud computing environments, where multiple users can access the same physical GPU through a remote connection, as well as in high-performance computing systems, where multiple applications can share the resources of a single GPU to achieve faster computation times.
You can unlock more of your GPUs’ power with GigaIO’s rack-scale composable infrastructure which frees GPUs from servers, more than doubling utilization through precise, dynamic CPU/GPU composition. FabreX Enables breakthrough configurations, with lower acquisition, operating, and lifecycle costs and drives cloud-like agility without the cloud cost.
GPU Sharing allows for:
- A significant cost savings
- Resource Pooling
- Reduced Maintenance
- Increased Collaboration
GPUs are often Trapped Inside Servers, Limiting Utilization and Flexibility
Underutilized GPUs needlessly waste energy and drive up operating costs.
The following chart from towardsdatascience.com depicts the average GPU utilization by user. Which shows a decrease in GPU utilization across a majority of those users.
“Nearly a third of our users are averaging less than 15% utilization. Average GPU memory usage is quite similar. Our users tend to be experienced deep learning practitioners and GPUs are an expensive resource so I was surprised to see such low average usage.” – via towardsdatascience.com
Enter GigaIO’s FabreX environment: FabreX Memory Fabric breaks the through the server chassis barrier and disaggregates rack components into pools of resources, allowing for an increase in GPU utilization.