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GTC ON-DEMAND

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Abstract:
Learn about the latest developments in MVAPICH2-GDR library that helps MPI developers to exploit maximum performance and scalability on HPC clusters with NVIDIA GPUs. Multiple designs focusing on GPUDirect RDMA(GDR)_Async, non-blocking collectives, support for unified memory and datatype processing will be highlighted to boost the performance of HPC applications. Furthermore, targeting emerging deep learning frameworks, we'll present novel designs and enhancements to the MVAPICH2-GDR library to accommodate the large message and dense GPU computing requirements of the DL frameworks. Using a co-designed scheme between MVAPICH2-GDR and the Caffe workflow, we'll present OSU-Caffe, which supports an MPI-based distributed and scalable DL framework. Performance and scalability numbers of OSU-Caffe for various system configurations and datasets will also be presented.
Learn about the latest developments in MVAPICH2-GDR library that helps MPI developers to exploit maximum performance and scalability on HPC clusters with NVIDIA GPUs. Multiple designs focusing on GPUDirect RDMA(GDR)_Async, non-blocking collectives, support for unified memory and datatype processing will be highlighted to boost the performance of HPC applications. Furthermore, targeting emerging deep learning frameworks, we'll present novel designs and enhancements to the MVAPICH2-GDR library to accommodate the large message and dense GPU computing requirements of the DL frameworks. Using a co-designed scheme between MVAPICH2-GDR and the Caffe workflow, we'll present OSU-Caffe, which supports an MPI-based distributed and scalable DL framework. Performance and scalability numbers of OSU-Caffe for various system configurations and datasets will also be presented.  Back
 
Topics:
HPC and Supercomputing, Artificial Intelligence and Deep Learning, Tools & Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7356
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Abstract:

Learn about the latest developments in MVAPICH2 library that simplifies the task of porting Message Passing Interface (MPI) applications to supercomputing clusters with NVIDIA GPUs. MVAPICH2 supports MPI communication directly from GPU device memory and optimizes it using various features offered by the CUDA toolkit. Various optimizations are integrated transparently under standard MPI API, for better programmability. Recent advances in MVAPICH2 include designs for MPI-3 RMA using GPUDirect RDMA, usage of fast GDRCOPY library, framework for MPI Datatype processing using CUDA kernels, and more. Performance results with micro-benchmarks and applications will be presented using MPI and CUDA/OpenACC. Impact of processor affinity to GPU and network affecting the performance will be presented.

Learn about the latest developments in MVAPICH2 library that simplifies the task of porting Message Passing Interface (MPI) applications to supercomputing clusters with NVIDIA GPUs. MVAPICH2 supports MPI communication directly from GPU device memory and optimizes it using various features offered by the CUDA toolkit. Various optimizations are integrated transparently under standard MPI API, for better programmability. Recent advances in MVAPICH2 include designs for MPI-3 RMA using GPUDirect RDMA, usage of fast GDRCOPY library, framework for MPI Datatype processing using CUDA kernels, and more. Performance results with micro-benchmarks and applications will be presented using MPI and CUDA/OpenACC. Impact of processor affinity to GPU and network affecting the performance will be presented.

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Topics:
HPC and Supercomputing, Tools & Libraries, Data Center & Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5461
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