R-based visualization techniques for the StarPU runtime
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StarVZ consists in a performance analysis workflow that combines the power of the R language (and the tidyverse realm) and many auxiliary tools to provide a consistent, flexible, extensible, fast, and versatile framework for the performance analysis of task-based applications that run on top of the StarPU runtime (with its MPI layer for multi-node support). Its goal is to provide a fruitful prototypical environment to conduct performance analysis hypothesis-checking for task-based applications that run on heterogeneous (multi-GPU, multi-core) multi-node HPC platforms.

The source code of this framework is released under the GPLv3 license.


Origin and Publications

A preliminary version of this framework has been released in the companion website (check the reproducible paper link below) of the VPA 2016 workshop (held during the SC16 conference). A second release of the framework is available in the companion website of a manuscript submitted to Wiley’s Concurrent and Computation: Practice and Experience.

  • Vinicius Garcia Pinto, Luka Stanisic, Arnaud Legrand, Lucas Mello Schnorr, Samuel Thibault, Vincent Danjean, “Analyzing Dynamic Task-Based Applications on Hybrid Platforms: An Agile Scripting Approach”, In Third Workshop on Visual Performance Analysis, VPA@SC 2016, Salt Lake, UT, USA, November 18, 2016, pp. 17-24, 2016 (Reproducible Paper and DOI)
  • A Visual Performance Analysis Framework for Task-based Parallel Applications running on Hybrid Clusters. Vinicius Garcia Pinto, Lucas Mello Schnorr, Luka Stanisic, Arnaud Legrand, Samuel Thibault, Vincent Danjean. Concurrency and Computation: Practice and Experience, Wiley, 2018, 30 (18), pp.1-31. (DOI, Draft, and Companion website)

Docker container

Please check this DockerFile to create a docker container with all the necessary requirements for a basic utilization of the starvz framework (in the form of an R package). Assuming that you have docker installed in your system, you may want to simply pull and run this container from Docker Hub, like this:

docker pull schnorr/starvz
docker run -it schnorr/starvz

After entering the container, run R and load the starvz package with: