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9007: The Top Six Advantages of CUDA-Ready Clusters and Clouds

GTC Japan 2014 2014年7月16日 Ian Lumb Evangelist Bright Computing, Inc CUDA-ready clusters enable developers to: 1. Focus on coding, not maintaining infrastructure (drivers, configs) and toolchains (compilers, libraries) 2. Routinely keep pace with innovation - from the latest in GPU hardware to the CUDA toolkit itself 3. Cross-develop with confidence and ease - maintain, and shift between, highly customized CUDA development environments 4. Exercise their preference in programming GPUs - choose CUDA or OpenCL or OpenACC and combine appropriately (with, for example, the Message Passing Interface, MPI) 5. Exploit the convergence of HPC and Big Data Analytics - make simultaneous use HPC and Hadoop services in GPU applications 6. Make use of private and public clouds - create a CUDA-ready cluster in a cloud or extend an on-site CUDA infrastructure into a cloud In this presentation, participants will learn how Bright Cluster Manager provisions, monitors and manages CUDA-ready clusters for developer advantage. Case studies will be used to briefly illustrate all six advantages for Bright developers. Specific attention will be given to: • Cross-developing under CUDA 5.5 and CUDA 6.0 with the NVIDIA Tesla K40 GPU accelerator • The challenges and opportunities for making use of private (using OpenStack) and public (using Amazon Web Services) clouds in GPU applications

Иконка канала  Монитор: Основы
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2 года назад
12+
16 просмотров
2 года назад

GTC Japan 2014 2014年7月16日 Ian Lumb Evangelist Bright Computing, Inc CUDA-ready clusters enable developers to: 1. Focus on coding, not maintaining infrastructure (drivers, configs) and toolchains (compilers, libraries) 2. Routinely keep pace with innovation - from the latest in GPU hardware to the CUDA toolkit itself 3. Cross-develop with confidence and ease - maintain, and shift between, highly customized CUDA development environments 4. Exercise their preference in programming GPUs - choose CUDA or OpenCL or OpenACC and combine appropriately (with, for example, the Message Passing Interface, MPI) 5. Exploit the convergence of HPC and Big Data Analytics - make simultaneous use HPC and Hadoop services in GPU applications 6. Make use of private and public clouds - create a CUDA-ready cluster in a cloud or extend an on-site CUDA infrastructure into a cloud In this presentation, participants will learn how Bright Cluster Manager provisions, monitors and manages CUDA-ready clusters for developer advantage. Case studies will be used to briefly illustrate all six advantages for Bright developers. Specific attention will be given to: • Cross-developing under CUDA 5.5 and CUDA 6.0 with the NVIDIA Tesla K40 GPU accelerator • The challenges and opportunities for making use of private (using OpenStack) and public (using Amazon Web Services) clouds in GPU applications

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