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Data Triggered Threads-Eliminating Redundant Computation
2013-09-11 09:22:36 | 【 【打印】【關閉】

報告時間: 2013年8月16日(周五)下午15:00-16:30
報告地點: 計算所440會議室(4樓研究生教室)

摘要:
This talk will introduce a new programming/architectural execution model for parallel threads. Unlike threads in conventional programming models, data-triggered threads (DTT) are initiated on a change to a memory location. This enables increased parallelism and the elimination of redundant, unnecessary computation. We‘ll show that 78% of all loads fetch redundant data, leading to a high incidence of redundant computation. By expressing computation through data-triggered threads, that computation is executed once when the data changes, and is skipped whenever the data does not change. The set of C SPEC benchmarks show performance speedup of up to 5.9X, and averaging 46%; other benchmarks even higher. We’ll examine hardware-supported DTT, and software-only implementation, and compiler-generated DTTs.

主講人簡介:
Dean Tullsen is a professor in the CSE at UCSD. He received his PhD from the University of Washington in 1996, where he introduced the concept of simultaneous multithreading (SMT, hyper-threading). He has continued to work in the area of computer architecture and back-end compilation, where he has introduced many new ideas to the research community, including threaded multipath execution, symbiotic job scheduling for multithreaded processors, dynamic critical path prediction, speculative precomputation, heterogeneous multi-core architectures, conjoined core architectures, event-driven simultaneous code optimization, and data triggered threads. He is a Fellow of the ACM and the IEEE.

 
 
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