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Abstract:
This talk will introduce two programming models OpenSHMEM and SharP to address the programming challenges of HPC systems with multiple GPUs per node, high-performing network, and huge amount of hierarchical heterogeneous memory. SharP uses distributed data-structure approach to abstract the memory and provide uniform interfaces for data abstractions, locality, sharing and resiliency across these memory systems. OpenSHMEM is a well-established library based PGAS programming model for programming HPC systems. We show how NVSHMEM, an implementation of OpenSHMEM, can enable communication in the CUDA kernels and realize OpenSHMEM model for GPU-based HPC systems. These two complementary programming models provide ability to program emerging architectures for Big-Compute and Big-Data applications. After the introduction, we will present experimental results for a wide-variety of applications including QMCPack, HPGMG, CoMD, and Memcached demonstrating the programming model advantages.
This talk will introduce two programming models OpenSHMEM and SharP to address the programming challenges of HPC systems with multiple GPUs per node, high-performing network, and huge amount of hierarchical heterogeneous memory. SharP uses distributed data-structure approach to abstract the memory and provide uniform interfaces for data abstractions, locality, sharing and resiliency across these memory systems. OpenSHMEM is a well-established library based PGAS programming model for programming HPC systems. We show how NVSHMEM, an implementation of OpenSHMEM, can enable communication in the CUDA kernels and realize OpenSHMEM model for GPU-based HPC systems. These two complementary programming models provide ability to program emerging architectures for Big-Compute and Big-Data applications. After the introduction, we will present experimental results for a wide-variety of applications including QMCPack, HPGMG, CoMD, and Memcached demonstrating the programming model advantages.  Back
 
Topics:
HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8135
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