1st Workshop on Visual Performance Analysis (VPA)

Held in conjunction with SC14: The International Conference on High Performance Computing, Networking, Storage and Analysis

New Orleans, LA, USA
November 21, 2014

llnl julich utah

Extended Submission Deadline: August 8, 2014

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 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 and practitioners from the areas of performance analysis, application optimization, visualization, and data analysis and provide a forum to discuss novel ideas on how to improve performance understanding, analysis and optimization through novel techniques in scientific and information visualization.

Workshop Topics

Scalable displays of performance data
Interactive visualization of performance data
Data models to enable data analysis and visualization
Graph representation of unstructured performance data
Collection and representation of meta data to enable fine grained attribution
Message trace visualization
Memory and network traffic visualization
Representation of hardware architectures

Papers By Session (See Official Page)

Session 1: Visualizing Large-Scale Communication Properties

Visualization of Performance Data for MPI Applications Using Circular Hierarchies
by Felix Schmitt, Robert Dietrich, René Kuß, Jens Doleschal, Andreas Knüpfer

TorusVis?ND: Unraveling High-Dimensional Torus Networks for Network Traffic Visualizations
by Shenghui Cheng, Pradipta De, Shaofeng H.-C. Jiang, Klaus Mueller

Down to Earth - How to Visualize Traffic on High-dimensional Torus Networks
by Lucas Theisen, Aamer Shah, Felix Wolf

Visualizing the Five-dimensional Torus Network of the IBM Blue Gene/Q
by Collin M. McCarthy, Katherine E. Isaacs, Abhinav Bhatele, Peer-Timo Bremer, Bernd Hamann

Session 2: Visualizing Complex Program Behavior

CommGram: A New Visual Analytics Tool for Large Communication Trace Data
by Jieting Wu, Jianping Zeng, Hongfeng Yu, Joseph P. Kenny

Discovering Barriers to Efficient Execution, Both Obvious and Subtle, Using Instruction-Level Visualization
by David M. Koppelman, Chris J. Michael

Visualization of Memory Access Behavior on Hierarchical NUMA Architectures
by Benjamin Weyers, Christian Terboven, Dirk Schmidl, Joachim Herber, Torsten W. Kuhlen, Matthias S. Müller, Bernd Hentschel

Linking Performance Data into Scientific Visualization Tools
by Kevin A. Huck, Kristin Potter, Doug W. Jacobsen, Hank Childs, Allen D. Malony

Paper Submission

We solicit two types of papers both covering original and previously unpublished ideas: 8 page regular papers and 4 page short papers. To be considered, your manuscript should be formatted according to the double-column IEEE format for Conference Proceedings (IEEEtran LaTeX Class (template) V1.8 packages and IEEEtran V1.12 BibTeX (bibliography)). Margins and font sizes should not be modified. The templates for "IEEEtran LaTeX Class (template) V1.8 packages and IEEEtran V1.12 BibTeX (bibliography)" can be found at http://www.ieee.org/conferences_events/conferences/publishing/templates.html

All papers must be submitted through Easychair at: https://www.easychair.org/conferences/?conf=vpa14

Accepted Papers

Jieting Wu, Jianping Zeng, Hongfeng Yu and Joseph Kenny. CommGram: A New Visual Analytics Tool for Large Communication Trace Data

Shenghui Cheng, Pradipta De, Shaofeng H.-C. Jiang and Klaus Mueller. TorusVisND: Unraveling High-Dimensional Torus Networks for Network Traffic Visualizations

Felix Schmitt, Robert Dietrich, René Kuß, Jens Doleschal and Andreas Knüpfer. Visualization of Performance Data for MPI Applications Using Circular Hierarchies

Collin M. McCarthy, Katherine E. Isaacs, Abhinav Bhatele, Peer-Timo Bremer and Bernd Hamann. Visualizing the Five-dimensional Torus Network of the IBM Blue Gene/Q

Benjamin Weyers, Christian Terboven, Dirk Schmidl, Joachim Herber, Torsten W. Kuhlen, Matthias S. Müller and Bernd Hentschel. Visualization of Memory Access Behavior on Hierarchical NUMA Architectures

David Koppelman and Chris Michael. Discovering Barriers to Efficient Execution, Both Obvious and Sutble, Using Instruction-Level Visualization
Kevin Huck, Kristin Potter, Doug Jacobsen, Hank Childs and Allen Malony. Linking Performance Data into Scientific Visualization Tools

Lucas Theisen, Aamer Shah and Felix Wolf. Down to Earth -- How to Visualize Traffic on High-dimensional Torus Networks

Logistics

All logistics, including registration, hotel reservations, and visa requests, will be handled by SC14.

Important Dates

August 8th: extended submission deadline for full and short papers
September 15th: notification of acceptance
October 6th: final paper and copyrights due

Workshop Organizers

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

Contact

This email address is being protected from spambots. You need JavaScript enabled to view it.

Program Committee

Carlos Scheidegger, AT&T
Naoya Maruyama, RIKEN AICS
Felix Wolf, German Research School for Simulation Sciences
Matthias Mueller, RWTH Aachen University
Holger Brunst, ZIH / TU Dresden
Joshua Levine, Clemson University
Derek Wang, Charlotte Visualization Center, UNCC
Todd Gamblin, Lawrence Livermore National Laboratory
Hank Childs, University of Oregon
Markus Geimer, Jülich Supercomputing Centre
Judit Gimenez, Barcelona Supercomputing Center / Universitat Politècnica de Catalunya
Remco Chang, Tufts University