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GTC ON-DEMAND

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Abstract:
Learn about advanced features in MVAPICH2 that accelerate HPC and AI on modern dense GPU systems. We'll talk about how MVAPICH2 supports MPI communication from GPU memory and improves it using the CUDA toolkit for optimized performance on different GPU configurations. We'll examine recent advances in MVAPICH2 that support large message collective operations and heterogeneous clusters with GPU and non-GPU nodes. We'll explain how we use the popular OSU micro-benchmark suite, and we'll provide examples from HPC and AI to demonstrate how developers can take advantage of MVAPICH2 in applications using MPI and CUDA/OpenACC. We'll also provide guidance on issues like processor affinity to GPUs and networks that can significantly affect the performance of MPI applications using MVAPICH2.
Learn about advanced features in MVAPICH2 that accelerate HPC and AI on modern dense GPU systems. We'll talk about how MVAPICH2 supports MPI communication from GPU memory and improves it using the CUDA toolkit for optimized performance on different GPU configurations. We'll examine recent advances in MVAPICH2 that support large message collective operations and heterogeneous clusters with GPU and non-GPU nodes. We'll explain how we use the popular OSU micro-benchmark suite, and we'll provide examples from HPC and AI to demonstrate how developers can take advantage of MVAPICH2 in applications using MPI and CUDA/OpenACC. We'll also provide guidance on issues like processor affinity to GPUs and networks that can significantly affect the performance of MPI applications using MVAPICH2.  Back
 
Topics:
HPC and AI, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9476
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Abstract:

Learn about the current wave of advances in AI and HPC technologies to improve performance of DNN training on NVIDIA GPUs. We'll discuss exciting opportunities for HPC and AI researchers and give an overview of interesting trends in DL frameworks from an architectural/performance standpoint. Several modern DL frameworks offer ease of use and flexibility to describe, train, and deploy various types of DNN architectures. These typically use a single GPU to accelerate DNN training and inference. We're exploring approaches to parallelize training. We'll highlight challenges for message passing interface runtimes to efficiently support DNN training and discuss how efficient communication primitives in MVAPICH2 can support scalable DNN training. We'll also talk about how co-design of the OSU-Caffe framework and MVAPICH2 runtime enables scale-out of DNN training to 160 GPUs.

Learn about the current wave of advances in AI and HPC technologies to improve performance of DNN training on NVIDIA GPUs. We'll discuss exciting opportunities for HPC and AI researchers and give an overview of interesting trends in DL frameworks from an architectural/performance standpoint. Several modern DL frameworks offer ease of use and flexibility to describe, train, and deploy various types of DNN architectures. These typically use a single GPU to accelerate DNN training and inference. We're exploring approaches to parallelize training. We'll highlight challenges for message passing interface runtimes to efficiently support DNN training and discuss how efficient communication primitives in MVAPICH2 can support scalable DNN training. We'll also talk about how co-design of the OSU-Caffe framework and MVAPICH2 runtime enables scale-out of DNN training to 160 GPUs.

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Topics:
HPC and AI, Deep Learning & AI Frameworks
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9501
Streaming:
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