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Towards Interactive Visual Exploration of Parallel Programs using a Domain-Specific Language

Tobias Klein, Stefan Bruckner, M. Eduard Gröller and Markus Hadwiger, Peter Rautek

CONFERENCE PAPER: In Proceedings of the International Workshop on OpenCL 2016, 2016. DOI: 10.1145/2909437.2909459

Abstract

The use of GPUs and the massively parallel computing paradigm have become wide-spread. We describe a framework for the interactive visualization and visual analysis of the run-time behavior of massively parallel programs, especially OpenCL kernels. This facilitates understanding a program's function and structure, finding the causes of possible slowdowns, locating program bugs, and interactively exploring and visually comparing different code variants in order to improve performance and correctness. Our approach enables very specific, user-centered analysis, both in terms of the recording of the run-time behavior and the visualization itself. Instead of having to manually write instrumented code to record data, simple code annotations tell the source-to-source compiler which code instrumentation to generate automatically. The visualization part of our framework then enables the interactive analysis of kernel run-time behavior in a way that can be very specific to a particular problem or optimization goal, such as analyzing the causes of memory bank conflicts or understanding an entire parallel algorithm.

Published

Proceedings of the International Workshop on OpenCL 2016

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BibTeX

@INPROCEEDINGS{Klein-2016-TIV,
  author = {Tobias Klein and Stefan Bruckner and M. Eduard Gr{\"o}ller and
	Markus Hadwiger and Peter Rautek},
  title = {Towards Interactive Visual Exploration of Parallel Programs using
	a Domain-Specific Language},
  booktitle = {Proceedings of the International Workshop on OpenCL 2016},
  year = {2016},
  month = apr,
  abstract = {The use of GPUs and the massively parallel computing paradigm have
	become wide-spread. We describe a framework for the interactive visualization
	and visual analysis of the run-time behavior of massively parallel
	programs, especially OpenCL kernels. This facilitates understanding
	a program's function and structure, finding the causes of possible
	slowdowns, locating program bugs, and interactively exploring and
	visually comparing different code variants in order to improve performance
	and correctness. Our approach enables very specific, user-centered
	analysis, both in terms of the recording of the run-time behavior
	and the visualization itself. Instead of having to manually write
	instrumented code to record data, simple code annotations tell the
	source-to-source compiler which code instrumentation to generate
	automatically. The visualization part of our framework then enables
	the interactive analysis of kernel run-time behavior in a way that
	can be very specific to a particular problem or optimization goal,
	such as analyzing the causes of memory bank conflicts or understanding
	an entire parallel algorithm.},
  doi = {10.1145/2909437.2909459},
  event = {IWOCL 2016},
  keywords = {domain specific languages, GPU programming, visual exploration},
  location = {Vienna, Austria},
  owner = {bruckner},
}






 Last Modified: Stefan Bruckner, 2017-08-23