DK Panda, Ohio State University
In this video from the GPU Technology Conference, DK Panda from Ohio State University presents: Enabling Efficient Use of UPC and OpenSHMEM PGAS Models on GPU Clusters.
Learn about extensions that enable efficient use of Partitioned Global Address Space (PGAS) Models like OpenSHMEM and UPC on supercomputing clusters with NVIDIA GPUs. PGAS models are gaining attention for providing shared memory abstractions that make it easy to develop applications with dynamic and irregular communication patterns. However, the existing UPC and OpenSHMEM standards do not allow communication calls to be made directly on GPU device memory. This talk discusses simple extensions to the OpenSHMEM and UPC models to address this issue. Runtimes to support these extensions, optimize data movement using features like CUDA IPC and GPUDirect RDMA and exploiting overlap are presented. We demonstrate the use of the extensions and performance impact of the runtime designs.”
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