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GTC ON-DEMAND

Programming Languages
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Enabling Efficient Use of UPC and OpenSHMEM PGAS Models on GPU Clusters
Abstract:
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 communication patterns. However, the existing UPC and OpenSHMEM standards do not allow communication calls to be made directly on GPU device memory. Data has to be moved to the CPU before PGAS models can be used for communication. This talk discusses simple extensions to the OpenSHMEM and UPC models that address this issue. They allow direct communication from GPU memory and enable runtimes to optimize data movement using features like CUDA IPC and GPUDirect RDMA, in a way that is transparent to the application developer. We present designs which focus on performance and truly one-sided communication. We use application kernels to demonstrate the use of the extensions and performance impact of the runtime designs, on clusters with GPUs.
 
Topics:
Programming Languages, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2014
Session ID:
S4528
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