$\mathrm{PPT}$-Multicore: performance prediction of Open$\mathrm{MP}$ applications using reuse profiles and analytical modeling

Barai, Atanu; Arafa, Yehia; Badawy, Abdel-Hameed; Chennupati, Gopinath; Santhi, Nandakishore; Eidenbenz, Stephan

doi:10.1007/s11227-021-03949-4

$$\mathrm{PPT}$$-Multicore: performance prediction of Open$$\mathrm{MP}$$ applications using reuse profiles and analytical modeling

Journal Article · Mon Jun 28 00:00:00 EDT 2021 · Journal of Supercomputing

DOI:https://doi.org/10.1007/s11227-021-03949-4· OSTI ID:1922761

^[1]; Arafa, Yehia ^[1]; Badawy, Abdel-Hameed ^[1]; Chennupati, Gopinath ^[2]; ^[2]; ^[2]

New Mexico State University, Las Cruces, NM (United States)
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

In this report we present PPT-Multicore, an analytical model embedded in the Performance Prediction Toolkit (PPT) to predict parallel applications’ performance running on a multicore processor. PPT-Multicore builds upon our previous work towards a multicore cache model. We extract LLVM basic block labeled memory trace using an architecture-independent LLVM-based instrumentation tool only once in an application’s lifetime. The model uses the memory trace and other parameters from an instrumented sequentially executed binary. We use probabilistic and computationally efficient reuse profiles to predict the cache hit rates and runtimes of OpenMP programs’ parallel sections. We model Intel’s Broadwell, Haswell, and AMD’s Zen2 architectures and validate our framework using different applications from PolyBench and PARSEC benchmark suites. The results show that PPT-Multicore can predict cache hit rates with an overall average error rate of 1.23% while predicting the runtime with an error rate of 9.08%.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1922761

Report Number(s):: LA-UR-21-22749

Journal Information:: Journal of Supercomputing, Journal Name: Journal of Supercomputing Journal Issue: 2 Vol. 78; ISSN 0920-8542

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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$$\mathrm{PPT}$$-Multicore: performance prediction of Open$$\mathrm{MP}$$ applications using reuse profiles and analytical modeling

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