Abstracting Parallel Programming and Its Analysis Towards Framework Independent Development

Arndt, Oliver Jakob; Lefherz, Tile; Blume, Holger

doi:10.1109/mcsoc.2015.22

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…In our earlier work [25], we presented the middleware layer MPAL (Modular Parallelization Abstraction Layer) for parallel programming, which automatically extracts the metrics that we use to build descriptive characterizations (features), summarized in Table 1 In Section 4, we will present an evaluation of the meaningfulness and validity of these parameters to be used as descriptive features for characterization of parallel workloads. Thereby, exemplary workload characteristics from our current database as well as an analysis of the correlation benchmarks between derived distances and corresponding prediction errors will be given.…”

Section: Scalability Characteristicsmentioning

confidence: 99%

“…As prediction accuracy relies on database quality, the basic idea is to have a large set of varying benchmarks for more likely providing close prediction candidates. All benchmarks and required libraries like MPAL [25] for parallelization, profiling, and parameter extraction and PAPI [29] for performance counter usage are compressed in a tar-ball. A Makefile automatically extracts and compiles libraries, executes benchmarks one by one, and compresses all results in addition to the extracted characteristics (t (1) and sc) in a new tar-ball.…”

Section: Databasementioning

confidence: 99%

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Multicore Performance Prediction with MPET

Arndt

Lüders

Riggers

et al. 2020

J Sign Process Syst

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Multicore processors serve as target platforms in a broad variety of applications ranging from high-performance computing to embedded mobile computing and automotive applications. But, the required parallel programming opens up a huge design space of parallelization strategies each with potential bottlenecks. Therefore, an early estimation of an application’s performance is a desirable development tool. However, out-of-order execution, superscalar instruction pipelines, as well as communication costs and (shared-) cache effects essentially influence the performance of parallel programs. While offering low modeling effort and good simulation speed, current approximate analytic models provide moderate prediction results so far. Virtual prototyping requires a time-consuming simulation, but produces better accuracy. Furthermore, even existing statistical methods often require detailed knowledge of the hardware for characterization. In this work, we present a concept called Multicore Performance Evaluation Tool (MPET) and its evaluation for a statistical approach for performance prediction based on abstract runtime parameters, which describe an application’s scalability behavior and can be extracted from profiles without user input. These scalability parameters not only include information on the interference of software demands and hardware capabilities, but indicate bottlenecks as well. Depending on the database setup, we achieve a competitive accuracy of 20% mean prediction error (11% median), which we also demonstrate in a case study.

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Section: Scalability Characteristicsmentioning

confidence: 99%

Section: Databasementioning

confidence: 99%