PCERE: Fine-grained Parallel Benchmark Decomposition for Scalability Prediction - Université de Versailles Saint-Quentin-en-Yvelines Access content directly
Conference Papers Year : 2015

PCERE: Fine-grained Parallel Benchmark Decomposition for Scalability Prediction


Evaluating the strong scalability of OpenMP applications is a costly and time-consuming process. It traditionally requires executing the whole application multiple times with different number of threads. We propose the Parallel Codelet Extractor and REplayer (PCERE), a tool to reduce the cost of scalability evaluation. PCERE decomposes applications into small pieces called codelets: each codelet maps to an OpenMP parallel region and can be replayed as a standalone program. To accelerate scalability prediction, PCERE replays codelets while varying the number of threads. Prediction speedup comes from two key ideas. First, the number of invocations during replay can be significantly reduced. Invocations that have the same performance are grouped together and a single representative is replayed. Second, sequential parts of the programs do not need to be replayed for each different thread configuration. PCERE codelets can be captured once and replayed accurately on multiple architectures, enabling cross-architecture parallel performance prediction. We evaluate PCERE on a C version of the NAS 3.0 Parallel Benchmarks (NPB). We achieve an average speed-up of 25 times on evaluating OpenMP applications scalability with an average error of 4.9\% (median error of 1.7\%).
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hal-01417304 , version 1 (15-12-2016)



Mihail Popov, Chadi Akel, Florent Conti, William Jalby, Pablo de Oliveira Castro. PCERE: Fine-grained Parallel Benchmark Decomposition for Scalability Prediction. 2015 IEEE International Parallel and Distributed Processing Symposium (IPDPS), May 2015, Hyderabad, India. ⟨10.1109/IPDPS.2015.19⟩. ⟨hal-01417304⟩
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