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Permanent link (DOI): https://doi.org/10.7939/R39W09512

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Evaluation of Thread Level Speculation in BlueGene/Q Open Access

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Author or creator
Bhattacharyya, Arnamoy
Amaral, Jose Nelson
Finkel, Hal
Additional contributors
Subject/Keyword
Parallelization
Thread-Level Speculation
Benchmarking
High-Performance Computing
Software Systems
Data Dependencies
Type of item
Computing Science Technical Report
Computing science technical report ID
TR14-02
Language
English
Place
Time
Description
Thread Level Speculation (TLS) is a hardware/software technique that guarantees correct parallel execution of loops even in the presence of dependence and has potential to lead to performance gains through the parallelization of loops that cannot be proven to be free of dependencies at compile time. However, given the overhead of TLS execution, the selection of loops to be speculated is important to avoid performance degradation. Data-dependence profiling is often used to find out if the may dependencies reported by the static analysis of a compiler materialize at runtime. A cost analysis may conclude that some loops with a lower probability of dependence should be speculatively parallelized. This report addressed the question as to whether a loops' dependence behaviour changes when the input to the program changes --- a study of 57 different benchmarks indicates that it usually does not change. Then the report describes SpecEval, a new automatic speculative parallelization framework that uses single-input data-dependence profiles to find speculation candidates in the SPEC2006 and PolyBench/C benchmarks. This report also presents the first performance evaluation of TLS implementation in IBM's BlueGene/Q supercomputer and shows that the performance of TLS is affected by several factors, including: number and coverage of speculated loops, miss-speculation overhead due to function calls in a speculated loop, L1 cache miss rate and dynamic instruction path length affects.
Date created
2014
DOI
doi:10.7939/R39W09512
License information
Creative Commons Attribution 3.0 Unported
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