Auto-tuning is a popular approach to program optimization: it automatically finds good configurations of a program’s so-called tuning parameters whose values are crucial for achieving high performance for a particular parallel architecture and characteristics of input/output data. We present three new contributions of the Auto-Tuning Framework (ATF), which enable a key advantage in general-purpose auto-tuning : efficiently optimizing programs whose tuning parameters have interdependencies among them. We make the following contributions to the three main phases of general-purpose auto-tuning: (1) ATF generates the search space of interdependent tuning parameters with high performance by efficiently exploiting parameter constraints; (2) ATF stores such search spaces efficiently in memory, based on a novel chain-of-trees search space structure; (3) ATF explores these search spaces faster, by employing a multi-dimensional search strategy on its chain-of-trees search space representation. Our experiments demonstrate that, compared to the state-of-the-art, general-purpose auto-tuning frameworks, ATF substantially improves generating, storing, and exploring the search space of interdependent tuning parameters, thereby enabling an efficient overall auto-tuning process for important applications from popular domains, including stencil computations, linear algebra routines, quantum chemistry computations, and data mining algorithms.

中文翻译：

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通过自动调整框架 (ATF) 对具有相互依赖的调整参数的并行程序进行有效的自动调整

自动调整是一种流行的程序优化方法：它自动找到程序所谓调整参数的良好配置，这些参数的值对于实现特定并行架构的高性能和输入/输出数据的特性至关重要。我们提出了自动调整框架 (ATF) 的三个新贡献，它们在以下方面具有关键优势通用自动调整: 有效优化调整参数有的程序相互依赖他们之中。我们对通用自动调整的三个主要阶段做出以下贡献： (1) ATF生成通过有效地利用参数约束，以高性能的相互依赖的调整参数的搜索空间；(2) 自动变速箱油商店这种搜索空间在内存中有效，基于一种新颖的树链搜索空间结构；(3) 自动变速箱油探索通过在其树链搜索空间表示上采用多维搜索策略，这些搜索空间更快。我们的实验表明，与最先进的通用自动调整框架相比，ATF 显着改进了相互依赖的调整参数的生成、存储和探索搜索空间，从而实现了高效的整体自动调整过程适用于流行领域的重要应用，包括模板计算、线性代数例程、量子化学计算和数据挖掘算法。