Model-Based Performance Diagnosis of Master-Worker Parallel Computations
Model-Based Performance Diagnosis of Master-Worker Parallel Computations
复制标题
基于模型的Master-Worker并行计算性能诊断
DOI：
10.1007/11823285_5
复制
发表时间：
2006
影响因子：
4.1
通讯作者：
A. Malony
中科院分区：
文献类型：
--
作者：
Li Li;A. Malony
并行性能调优自然涉及一个诊断过程，以定位和解释程序效率低下的原因。提出了一种利用并行计算模式（模型）进行诊断发现的方法。性能问题的知识和假设搜索的推理规则是从模型语义和分析专业知识中设计出来的。以这种方式，性能诊断过程可以是自动化的，并且适合于并行模型变化。我们演示了基于模型的性能诊断的经典的主-工人模式的实现。我们的研究结果表明，基于模式的性能知识可以提供有效的指导，定位和解释性能错误的高层次的程序抽象。
Parallel performance tuning naturally involves a diagnosis process to locate and explain sources of program inefficiency. Proposed is an approach that exploits parallel computation patterns (models) for diagnosis discovery. Knowledge of performance problems and inference rules for hypothesis search are engineered from model semantics and analysis expertise. In this manner, the performance diagnosis process can be automated as well as adapted for parallel model variations. We demonstrate the implementation of model-based performance diagnosis on the classic Master-Worker pattern. Our results suggest that pattern-based performance knowledge can provide effective guidance for locating and explaining performance bugs at a high level of program abstraction.