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|Abstract:||GPUs are flexible parallel processors capable of accelerating real applications. To exploit them, programmers must ensure a consistent program state between the CPU and GPU memories by managing data. Manually managing data is tedious and error-prone. In prior work on automatic CPU-GPU data management, alias analysis quality limits performance, and type-inference quality limits applicability. This paper presents Dynamically Managed Data (DyManD), the first automatic system to manage complex and recursive data-structures without static analyses. By replacing static analyses with a dynamic run-time system, DyManD overcomes the performance limitations of alias analysis and enables management for complex and recursive data-structures. DyManD-enabled GPU parallelization matches the performance of prior work equipped with perfectly precise alias analysis for 27 programs and demonstrates improved applicability on programs not previously managed automatically.|
|Citation:||Jablin, Thomas B., James A. Jablin, Prakash Prabhu, Feng Liu, and David I. August. "Dynamically managed data for CPU-GPU architectures." Proceedings of the Tenth International Symposium on Code Generation and Optimization (2012): pp. 165-174. doi:10.1145/2259016.2259038|
|Pages:||165 - 174|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the Tenth International Symposium on Code Generation and Optimization|
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