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)
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
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: