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Graph-Centric Performance Analysis for Large-Scale Parallel Applications
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DOI：10.1109/TPDS.2024.3396849.png)
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Abstract
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Performance analysis is essential for understanding the performance behaviors of parallel programs and detecting performance bottlenecks. Whereas, complex interconnections across several types of performance bugs, as well as inter-process communications and data dependence, make efficient performance analysis even more difficult. Despite the fact that many performance tools have been developed, accurately identifying underlying performance bottlenecks for such complex scenarios requires specific in-depth analysis. Significant human efforts and analysis knowledge are often required to implement each specific analytic task. To alleviate the complexity of developing specific performance analytic tasks, we present a programmable performance analysis tool, called PERFLOW. In PERFLOW, a step-by-step performance analysis process is represented as an Analysis Flow Diagram, which is constructed with several performance analysis sub-tasks, namely passes, that can be defined by developers or provided by PERFLOW's built-in analysis pass library. Furthermore, we define a Performance Abstraction Graph to describe the performance behavior of a parallel program, where the edges indicate the interactions between parallel units, therefore the analytic sub-tasks are converted to graph analysis tasks. PERFLOW provides plentiful Python APIs for developing analytic tasks. Several case studies of real-world applications with up to 700 K lines of code are used to demonstrate the effectiveness of PERFLOW. The results indicate that PERFLOW makes it much easier to implement specific performance analytic tasks, and these tasks are performed automatically and efficiently to detect underlying performance bottlenecks.
Keywords:
Parallel applications
performance analysis
dataflow abstraction
graph analysis
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