Spelling suggestions: "subject:"delaware MPI"" "subject:"chinaware MPI""
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Efektivní komunikace v multi-GPU systémech / Efficient Communication in Multi-GPU SystemsŠpeťko, Matej January 2018 (has links)
After the introduction of CUDA by Nvidia, the GPUs became devices capable of accelerating any general purpose computation. GPUs are designed as parallel processors which posses huge computation power. Modern supercomputers are often equipped with GPU accelerators. Sometimes the performance or the memory capacity of a single GPU is not enough for a scientific application. The application needs to be scaled into multiple GPUs. During the computation there is need for the GPUs to exchange partial results. This communication represents computation overhead. For this reason it is important to research the methods of the effective communication between GPUs. This means less CPU involvement, lower latency, shared system buffers. Inter-node and intra-node communication is examined. The main focus is on GPUDirect technologies from Nvidia and CUDA-Aware MPI. Subsequently k-Wave toolbox for simulating the propagation of acoustic waves is introduced. This application is accelerated by using CUDA-Aware MPI.
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Designing optimized MPI+NCCL hybrid collective communication routines for dense many-GPU clustersSenthil Kumar, Nithin 04 October 2021 (has links)
No description available.
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Efektivní komunikace v multi-GPU systémech / Efficient Communication in Multi-GPU SystemsŠpeťko, Matej January 2018 (has links)
After the introduction of CUDA by Nvidia, the GPUs became devices capable of accelerating any general purpose computation. GPUs are designed as parallel processors which posses huge computation power. Modern supercomputers are often equipped with GPU accelerators. Sometimes single GPU performance is not enough for a scientific application and it needs to scale over multiple GPUs. During the computation, there is a need for the GPUs to exchange partial results. This communication represents computation overhead and it is important to research methods of the effective communication between GPUs. This means less CPU involvement, lower latency and shared system buffers. This thesis is focused on inter-node and intra-node GPU-to-GPU communication using GPUDirect technologies from Nvidia and CUDA-Aware MPI. Subsequently, k-Wave toolbox for simulating the propagation of acoustic waves is introduced. This application is accelerated by using CUDA-Aware MPI. Peer-to-peer transfer support is also integrated to k-Wave using CUDA Inter-process Communication.
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Co-designing Communication Middleware and Deep Learning Frameworks for High-Performance DNN Training on HPC SystemsAwan, Ammar Ahmad 10 September 2020 (has links)
No description available.
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