Mapping Computations in Heterogeneous Multicore Systems with Statistical Regression on Program Inputs

J. C. R. Da Silva, Lorena Leão, V. Petrucci, A. Gamatie, Fernando Magno Quintão Pereira
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Abstract

A hardware configuration is a set of processors and their frequency levels in a multicore heterogeneous system. This article presents a compiler-based technique to match functions with hardware configurations. Such a technique consists of using multivariate linear regression to associate function arguments with particular hardware configurations. By showing that this classification space tends to be convex in practice, this article demonstrates that linear regression is not only an efficient tool to map computations to heterogeneous hardware, but also an effective one. To demonstrate the viability of multivariate linear regression as a way to perform adaptive compilation for heterogeneous architectures, we have implemented our ideas onto the Soot Java bytecode analyzer. Code that we produce can predict the best configuration for a large class of Java and Scala benchmarks running on an Odroid XU4 big.LITTLE board; hence, outperforming prior techniques such as ARM’s GTS and CHOAMP, a recently released static program scheduler.
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程序输入统计回归的异构多核系统映射计算
硬件配置是多核异构系统中的一组处理器及其频率级别。本文介绍了一种基于编译器的技术,用于将函数与硬件配置相匹配。这种技术包括使用多元线性回归将函数参数与特定硬件配置关联起来。通过表明该分类空间在实践中趋于凸,本文证明了线性回归不仅是将计算映射到异构硬件的有效工具,而且是有效的工具。为了证明多元线性回归作为一种对异构架构执行自适应编译的方法的可行性,我们已经在Soot Java字节码分析器上实现了我们的想法。我们生成的代码可以预测在Odroid XU4上运行的大型Java和Scala基准测试的最佳配置。小板;因此,优于先前的技术,如ARM的GTS和CHOAMP(最近发布的静态程序调度程序)。
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