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GTC ON-DEMAND

HPC and AI
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NVSHMEM: A Partitioned Global Address Space Library for NVIDIA GPU Clusters
Abstract:
Addressing the apparent Amdahl's fraction of synchronizing with the CPU for communication is critical for strong scaling of applications on GPU clusters. GPUs are designed to maximize throughput and have enough state and parallelism to hide long latencies to global memory. It's important to take advantage of these inherent capabilities of the GPU and the CUDA programming model when tackling communications between GPUs. NVSHMEM provides a Partitioned Global Address Space (PGAS) that spans memory across GPUs and provides an API for fine-grained GPU-GPU data movement and synchronization from within a CUDA kernel. NVSHMEM also provides CPU-side API for GPU-GPU data movement that provides a progression for applications to move to NVSHMEM. CPU-side communication can be issued in stream order, similar to CUDA operations. It implements the OpenSHMEM programming model that is of great interest to government agencies and national labs. We'll give an overview of capabilities, API, and semantics of NVSHMEM. We'll use examples from a varied set of applications (HPGMG, Multi-GPU Transpose, Graph500, etc.) to demonstrate the use and benefits of NVSHMEM.
 
Topics:
HPC and AI, Tools & Libraries, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8595
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