Fourth International Workshop on Visual Performance Analysis (VPA 17)

Denver, Colorado, USA

November 17, 2017


Held in conjunction with SC17: The International Conference on High Performance Computing, Networking, Storage and Analysis, in cooperation with TCHPC: The IEEE Computer Society Technical Consortium on High Performance Computing

Over the last decades an incredible amount of resources has been devoted to building ever more powerful supercomputers. However, exploiting the full capabilities of these machines is becoming exponentially more difficult with each new generation of hardware. To help understand and optimize the behavior of massively parallel simulations the performance analysis community has created a wide range of tools and APIs to collect performance data, such as flop counts, network traffic or cache behavior at the largest scale. However, this success has created a new challenge, as the resulting data is far too large and too complex to be analyzed in a straightforward manner. Therefore, new automatic analysis and visualization approaches must be developed to allow application developers to intuitively understand the multiple, interdependent effects that their algorithmic choices have on the final performance.

This workshop will bring together researchers from the fields of performance analysis and visualization to discuss new approaches of applying visualization and visual analytics techniques to large scale applications.

Workshop Topics

  • Scalable displays of performance data
  • Data models to enable scalable visualization
  • Graph representation of unstructured performance data
  • Presentation of high-dimensional data
  • Visual correlations between multiple data source
  • Human-Computer Interfaces for exploring performance data
  • Multi-scale representations of performance data for visual exploration

Previous Workshops


Call for Papers

We solicit 8-page full papers as well as 4-page short papers that focus on techniques at the intersection of performance analysis and visualization, and either use visualization techniques to display large scale performance data or that develop new visualization or visual analytics methods that help create new insights.

Papers must be submitted in PDF format (readable by Adobe Acrobat Reader 5.0 and higher) and formatted for 8.5” x 11” (U.S. Letter). Submissions are limited to 8 pages in the ACM format, using the sample-sigconf template. The 8-page limit includes figures, tables, and references.

All papers must be submitted through Easychair at:


Important Dates

  • Submission deadline (extended): August 21, 2017 (AoE)
  • Notification of acceptance: September 18, 2017 (AoE)
  • Camera-ready deadline: October 9, 2017 (AoE)

Technical Program



Steering Committee

Peer-Timo Bremer, Lawrence Livermore National Laboratory
Bernd Mohr, Juelich Supercomputing Center
Valerio Pascucci, University of Utah
Martin Schulz, Lawrence Livermore National Laboratory

Workshop Chairs

Fabian Beck, University of Duisburg-Essen
Abhinav Bhatele, Lawrence Livermore National Laboratory
Judit Gimenez, Barcelona Supercomputing Center
Joshua A. Levine, University of Arizona

Program Committee

Harsh Bhatia, Lawrence Livermore National Laboratory
Holger Brunst, TU Dresden
Alexandru Calotoiu, Technical University Darmstadt
Todd Gamblin, Lawrence Livermore National Laboratory
Marc-Andre Hermanns, Juelich Supercomputing Center
Kevin Huck, University of Oregon
Katherine Isaacs, University of Arizona
Yarden Livnat, University of Utah
Naoya Maruyama, Lawrence Livermore National Laboratory
Bernd Mohr, Juelich Supercomputing Center
Ananya Muddukrishna, KTH Royal Institute of Technology
Matthias Mueller, RWTH Aachen University
Valerio Pascucci, University of Utah
Paul Rosen, University of South Florida
Carlos Scheidegger, University of Arizona
Chad Steed, Oak Ridge National Laboratory