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Analysis of the parallel programming models in Haskell for many-core systems

机译：Haskell中用于多核系统的并行编程模型的分析

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摘要

The Moore's law has reached its limitation since the integration and economic issues of CPU. Therefore, the trend of chip design is moving to the increase of the number of cores rather than stressing the density of circuits. Consequently, the parallel programming is attracting interests. In this trend, functional languages are getting popular for parallel programming since they have inherent parallelism. This paper aims to compare and analyze two Haskell programming models for many-core environment. We developed applications based on a Haskell parallel programming model named Eval monad and Cloud Haskell, respectively to compare the performance of them. We test the application on both 32 cores and 120 cores CPU. The experimental result shows that on 32 cores, the performances are similar, but on the 120 cores, Cloud Haskell performs 32% faster on run-time, and 123% better on scalability. This result implies that Cloud Haskell is more appropriate for a large number of cores than Eval monad, and the latter is more suitable for simple parallelism involving just tens of cores.
机译：自从CPU的集成和经济问题以来，摩尔定律已达到其极限。因此，芯片设计的趋势正在转向增加芯数，而不是强调电路的密度。因此，并行编程引起了人们的兴趣。在这种趋势下，功能语言由于具有固有的并行性而在并行编程中变得越来越流行。本文旨在比较和分析针对多核环境的两种Haskell编程模型。我们分别基于名为Eval monad和Cloud Haskell的Haskell并行编程模型开发了应用程序，以比较它们的性能。我们在32核和120核CPU上测试应用程序。实验结果表明，在32个内核上，性能是相似的，但是在120个内核上，Cloud Haskell在运行时上的性能提高了32％，在可伸缩性上提高了123％。该结果表明，Cloud Haskell比Eval monad更适合于大量内核，后者比仅涉及数十个内核的简单并行性更适合。

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