Abstract
GPU-to-CPU translation may extend Graphics Processing Units (GPU) programs executions to multi-/many-core CPUs, and hence enable cross-device task migration and promote whole-system synergy. This paper describes some of our findings in treatment to GPU synchronizations during the translation process. We show that careful dependence analysis may allow a fine-grained treatment to synchronizations and reveal redundant computation at the instruction-instance level. Based on thread-level dependence graphs, we present a method to enable such fine-grained treatment automatically. Experiments demonstrate that compared to existing translations, the new approach can yield speedup of a factor of integers.
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References
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Guo, Z., Shen, X. (2013). Fine-Grained Treatment to Synchronizations in GPU-to-CPU Translation. In: Rajopadhye, S., Mills Strout, M. (eds) Languages and Compilers for Parallel Computing. LCPC 2011. Lecture Notes in Computer Science, vol 7146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36036-7_12
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DOI: https://doi.org/10.1007/978-3-642-36036-7_12
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