论文标题
异地自动调节方法 - 绩效模型驱动的自动调用应用于并行显式ode方法
Offsite Autotuning Approach -- Performance Model Driven Autotuning Applied to Parallel Explicit ODE Methods
论文作者
论文摘要
自动调用技术是一种有希望的方法，可以最大程度地减少原本繁琐的手动努力，以优化特定目标平台的科学应用。理想情况下，一种自动调用方法能够通过应用合适的程序转换和分析模型来可靠地识别新目标系统的最有效实现变体或输入的新特征。在这项工作中，我们介绍了Ordsite，这是一种离线自动调整方法，该方法通过基于分析性能模型对实现变体进行评估而无需时间耗时的运行时实验，从而在安装时间自动化此选择过程。从抽象的多级YAML描述语言，OffSite自动从可能实现变体中得出优化，平台特定和特定于问题的代码，并将绩效模型应用于这些实现变体。 我们将异地应用于普通微分方程（ODE）的并行数值方法。特别是，我们研究了针对共享内存系统上各种初始值问题（IVP）调整特定类别的显式ODE求解器（PIRK方法）。我们的实验表明，异地能够可靠地确定一组为给定的测试配置（ODE求解器，IVP，平台）的最有效的实现变体，并且能够有效地处理重要的自动调整方案。
Autotuning techniques are a promising approach to minimize the otherwise tedious manual effort of optimizing scientific applications for a specific target platform. Ideally, an autotuning approach is capable of reliably identifying the most efficient implementation variant(s) for a new target system or new characteristics of the input by applying suitable program transformations and analytic models. In this work, we introduce Offsite, an offline autotuning approach which automates this selection process at installation time by rating implementation variants based on an analytic performance model without requiring time-consuming runtime experiments. From abstract multilevel YAML description languages, Offsite automatically derives optimized, platform-specific and problem-specific code of possible implementation variants and applies the performance model to these implementation variants. We apply Offsite to parallel numerical methods for ordinary differential equations (ODEs). In particular, we investigate tuning a specific class of explicit ODE solvers (PIRK methods) for various initial value problems (IVPs) on shared-memory systems. Our experiments demonstrate that Offsite is able to reliably identify a set of the most efficient implementation variants for given test configurations (ODE solver, IVP, platform) and is capable of effectively handling important autotuning scenarios.
