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- W1968391520 abstract "A GPU's computing power lies in its abundant memory bandwidth and massive parallelism. However, its hardware thread schedulers, despite being able to quickly distribute computation to processors, often fail to capitalize on program characteristics effectively, achieving only a fraction of the GPU's full potential. Moreover, current GPUs do not allow programmers or compilers to control this thread scheduling, forfeiting important optimization opportunities at the program level. This paper presents a transformation centered on Streaming Multiprocessors (SM); this software approach to circumventing the limitations of the hardware scheduler allows flexible program-level control of scheduling. By permitting precise control of job locality on SMs, the transformation overcomes inherent limitations in prior methods." @default.
- W1968391520 created "2016-06-24" @default.
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- W1968391520 date "2015-06-08" @default.
- W1968391520 modified "2023-09-26" @default.
- W1968391520 title "Enabling and Exploiting Flexible Task Assignment on GPU through SM-Centric Program Transformations" @default.
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- W1968391520 doi "https://doi.org/10.1145/2751205.2751213" @default.
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