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Data Triggered Threads -- Eliminating Redundant Computation

Event Date: April 7, 2015
Speaker: Professor Dean Tullsen
Speaker Affiliation: University of California, San Diego
Time: 10:30am
Location: EE 317
Contact Name: T.N. Vijaykumar
Contact Phone: 765-49-40592
Contact Email:

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. This talk will focus primarily on the latter. 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, a software-only implementation, and compiler-generated DTTs with no input from the programmer.

Dean Tullsen is a professor in the computer science and engineering department at UCSD. He received his PhD from the University of Washington in 1996, where he worked on simultaneous multithreading (hyper-threading). He has continued to work in the area of computer architecture and back-end compilation, where with various co-authors 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.  He has twice won the Influential ISCA Paper Award.