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• W3186150174 abstract "The NAS Parallel Benchmarks (NPB), originally implemented mostly in Fortran, is a consolidated suite containing several benchmarks extracted from Computational Fluid Dynamics (CFD) models. The benchmark suite has important characteristics such as intensive memory communications, complex data dependencies, different memory access patterns, and hardware components/sub-systems overload. Parallel programming APIs, libraries, and frameworks that are written in C++ as well as new optimizations and parallel processing techniques can benefit if NPB is made fully available in this programming language. In this paper we present NPB-CPP, a fully C++ translated version of NPB consisting of all the NPB kernels and pseudo-applications developed using OpenMP, Intel TBB, and FastFlow parallel frameworks for multicores. The design of NPB-CPP leverages the Structured Parallel Programming methodology (essentially based on parallel design patterns). We show the structure of each benchmark application in terms of composition of few patterns (notably Map and MapReduce constructs) provided by the selected C++ frameworks. The experimental evaluation shows the accuracy of NPB-CPP with respect to the original NPB source code. Furthermore, we carefully evaluate the parallel performance on three multi-core systems (Intel, IBM Power, and AMD) with different C++ compilers (gcc, icc, and clang) by discussing the performance differences in order to give to the researchers useful insights to choose the best parallel programming framework for a given type of problem. • The NAS Parallel Benchmarks for evaluating C++ parallel programming. • Structured parallel programming with OpenMP, Intel TBB and FastFlow. • Performance analysis of the parallel implementations on different multicore platforms." @default.
• W3186150174 created "2021-08-02" @default.
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• W3186150174 date "2021-12-01" @default.
• W3186150174 modified "2023-10-02" @default.
• W3186150174 title "The NAS Parallel Benchmarks for evaluating C++ parallel programming frameworks on shared-memory architectures" @default.
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• W3186150174 doi "https://doi.org/10.1016/j.future.2021.07.021" @default.
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