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We present Lerna, an end-to-end tool that automatically and transparently detects and extracts parallelism from data-dependent sequential loops. Lerna uses speculation combined with a set of techniques including code profiling, dependency analysis, instrumentation, and adaptive execution. Speculation is needed to avoid conservative actions and detect actual conflicts. Lerna targets applications that are hard-to-parallelize due to data dependency. Our experimental study involves the parallelization of 13 applications with data dependencies. Results on a 24-core machine show an average of 2.7 speedup for micro-benchmarks and 2.5 for the macro-benchmarks.
ACM Transactions on Storage (TOS) – Association for Computing Machinery
Published: Mar 22, 2019
Keywords: Code parallelization
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