GPU triggered networking for intra-kernel communications
| Autor: | Khaled Hamidouche, Michael LeBeane, Steven K. Reinhardt, Brad Benton, Lizy K. John, Mauricio Breternitz |
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| Rok vydání: | 2017 |
| Předmět: |
010302 applied physics
020203 distributed computing Speedup Computer science 02 engineering and technology Network interface Thread (computing) Parallel computing ComputerSystemsOrganization_PROCESSORARCHITECTURES Software_PROGRAMMINGTECHNIQUES 01 natural sciences Stencil Kernel (image processing) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Central processing unit Implementation Critical path method ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | SC |
| Popis: | GPUs are widespread across clusters of compute nodes due to their attractive performance for data parallel codes. However, communicating between GPUs across the cluster is cumbersome when compared to CPU networking implementations. A number of recent works have enabled GPUs to more naturally access the network, but suffer from performance problems, require hidden CPU helper threads, or restrict communications to kernel boundaries.In this paper, we propose GPU Triggered Networking, a novel, GPU-centric networking approach which leverages the best of CPUs and GPUs. In this model, CPUs create and stage network messages and GPUs trigger the network interface when data is ready to send. GPU Triggered Networking decouples these two operations, thereby removing the CPU from the critical path. We illustrate how this approach can provide up to 25% speedup compared to standard GPU networking across microbenchmarks, a Jacobi stencil, an important MPI collective operation, and machine-learning workloads. |
| Databáze: | OpenAIRE |
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