Interactive Performance Visualization and Analysis of Execution Traces for Pattern-Based Parallel Programming

We introduce the design and implementation of a performance visualization system for high-level programming of heterogeneous parallel systems. The system consists of an execution trace extension to the algorithmic skeleton programming framework SkePU and an interactive tool that visualizes the execu...

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Vydáno v:International journal of parallel programming Ročník 53; číslo 5; s. 29
Hlavní autoři: Ernstsson, August, Frankell, Elin, Kessler, Christoph
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.10.2025
Springer Nature B.V
Témata:
Algorithms
Artificial neural networks
Computer Science
Optimization
Parallel programming
Performance evaluation
Processor Architectures
Software Engineering/Programming and Operating Systems
Source code
Theory of Computation
Visualization
Execution tracing
Performance analysis
Performance visualization
Algorithmic skeletons
Single-source heterogeneous programming
ISSN:0885-7458, 1573-7640
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Shrnutí:We introduce the design and implementation of a performance visualization system for high-level programming of heterogeneous parallel systems. The system consists of an execution trace extension to the algorithmic skeleton programming framework SkePU and an interactive tool that visualizes the execution trace as a graph model with a connected source code browser. The main contribution of the work lies in addressing the gap of performance analysis and debugging for programming of parallel and heterogeneous architectures at high abstraction levels, where the tool should present information at the same level of abstraction as the programming model itself. We evaluate the work by usability user surveys, observations, and interviews of novice to intermediate-level SkePU users. Additionally, we conduct performance evaluation to quantify the overhead of execution tracing of SkePU programs. Finally, we evaluate how trace lengths impact responsiveness and interactivity of the visualization tool and apply it to a larger program for training convolutional neural network models.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-025-00805-3