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- W2936699137 abstract "While GPU Computing is pervasive in various areas, including scientific-technical computing and machine learning, single GPUs are often insufficient to meet application demand. Furthermore, multi-GPU processing is a promising option to achieve a continuing performance scaling, given that CMOS technology is expected to hit fundamental scaling limits. We observe that a large amount of work has been done regarding characterizing single-GPU applications. However, characterizing GPGPU applications regarding the NUMA effects resulting from distributed execution has been largely overlooked.In this work, we introduce a framework that allows analyzing the internal communication behavior of GPGPU applications, consisting of 1) an open-source memory tracing plugin for Clang/LLVM, 2) a simple communication model based on summaries of a kernel's memory accesses, 3) communication and locality analyses from memory traces. Besides characterizing locality of kernels, it allows reasoning about virtual bandwidth-limited communication paths between NUMA nodes using different partitioning strategies. We then apply this framework for a large variety of applications, report initial results from our analysis, and finally discuss benefits and limitations of this concept." @default.
- W2936699137 created "2019-04-25" @default.
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- W2936699137 date "2019-04-13" @default.
- W2936699137 modified "2023-09-24" @default.
- W2936699137 title "Quantifying the NUMA Behavior of Partitioned GPGPU Applications" @default.
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- W2936699137 doi "https://doi.org/10.1145/3300053.3319420" @default.
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