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Function Based Benchmarks to Abstract Parallel Hardware and Predict Efficient Code Partitioning

机译:基于功能的基准测试,用于抽象并行硬件并预测有效的代码分区

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To increase the performance of a program, developers have to parallelize their code due to trends in modern hardware development. Since the parallelization of source code is paired with additional programming effort, it is desirable to know if a parallelization would result in an advantage in performance before implementing it. This paper examines the use of benchmarks for estimating the performance gain looking at the parallelization of Population Based Algorithms (PBAs) like Genetic Algorithms (GAs) and Particle Swarm Optimization Algorithms (PSOs) to be implemented on multi- and many-cores. These benchmarks are named function based benchmarks due to their dependence on the PBAs' functions. Furthermore, the software-hardware mapping with the most performance gain is suggested.
机译:为了提高程序的性能,由于现代硬件开发的趋势,开发人员必须并行化其代码。由于源代码的并行化需要额外的编程工作,因此希望在实施并行化之前先了解并行化是否会带来性能优势。本文考察了将基准用于评估性能增益的方法,着眼于将在多核和多核上实现的基于种群的算法(PBA)(如遗传算法(GA)和粒子群优化算法(PSO))的并行化。这些基准基于对PBA功能的依赖而被称为基于功能的基准。此外,建议使用性能最高的软硬件映射。

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