大规模机器学习程序的成本生成执行计划
Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs
摘要：
声明式大规模机器学习（ML）旨在以高级语言规范ML算法，并自动生成混合运行时执行计划，范围从单节点，内存计算到MapReduce（MR）或Spark等类似框架上的分布式计算。大规模机器学习程序的编译为自动优化提供了许多机会。先进的基于成本的优化技术需要-作为一个基本的先决条件-一个准确的成本模型来评估优化决策的影响。在本文中，我们分享了一种简单而强大但准确的技术，用于计算ML程序的替代运行时执行计划的成本。我们的成本模型依赖于生成和计算运行时计划，以自动反映所有连续的优化阶段。成本计算运行时计划还捕获控制流结构，如循环和分支，以及各种成本因素，如IO，延迟和计算成本。最后，我们将所有这些成本因素线性化为预期执行时间的单一度量。在SystemML中，这个成本模型被几个高级优化器所利用，如资源优化和全局数据流优化。我们分享我们的经验教训，以便为ML程序的优化提供基础。
英文摘要：
Declarative large-scale machine learning (ML) aims at the specification of ML algorithms in a high-level language and automatic generation of hybrid runtime execution plans ranging from single node, in-memory computations to distributed computations on MapReduce (MR) or similar frameworks like Spark. The compilation of large-scale ML programs exhibits many opportunities for automatic optimization. Advanced cost-based optimization techniques require---as a fundamental precondition---an accurate cost model for evaluating the impact of optimization decisions. In this paper, we share insights into a simple and robust yet accurate technique for costing alternative runtime execution plans of ML programs. Our cost model relies on generating and costing runtime plans in order to automatically reflect all successive optimization phases. Costing runtime plans also captures control flow structures such as loops and branches, and a variety of cost factors like IO, latency, and computation costs. Finally, we linearize all these cost factors into a single measure of expected execution time. Within SystemML, this cost model is leveraged by several advanced optimizers like resource optimization and global data flow optimization. We share our lessons learned in order to provide foundations for the optimization of ML programs.
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