- Sponsor:
- sighpc
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.
Proceeding Downloads
Visualization of performance data for MPI applications using circular hierarchies
One of the challenges for the developer of highly-parallel MPI applications running on distributed high performance computing systems is to understand the complex behavior of their applications. It requires to identify inefficiencies, and to optimize ...
TorusVisND: unraveling high-dimensional torus networks for network traffic visualizations
Torus networks are widely used in supercomputing. However, due to their complex topology and their large number of nodes, it is difficult for analysts to perceive the messages flow in these networks. We propose a visualization framework called TorusVis...
Down to earth: how to visualize traffic on high-dimensional torus networks
High-dimensional torus networks are becoming common in flagship HPC systems, with five of the top ten systems in June 2014 having networks with more than three dimensions. Although such networks combine performance with scalability at reasonable cost, ...
Visualizing the five-dimensional torus network of the IBM blue gene/Q
Understanding the interactions between a parallel application and the interconnection network over which it exchanges data is critical to optimizing performance in modern supercomputers. However, recent supercomputing architectures use networks that do ...
CommGram: a new visual analytics tool for large communication trace data
The performance of massively parallel program is often impacted by the cost of communication across computing nodes. Analysis of communication patterns is critical for understanding and optimizing massively parallel programs. Visualization can help ...
Discovering barriers to efficient execution, both obvious and subtle, using instruction-level visualization
CPU performance is determined by the interaction between available resources, microarchitectural features, the execution of instructions, and by the data. These elements can interact in complex ways, making it difficult for those seeing only aggregate ...
Visualization of memory access behavior on hierarchical NUMA architectures
- Benjamin Weyers,
- Christian Terboven,
- Dirk Schmidl,
- Joachim Herber,
- Torsten W. Kuhlen,
- Matthias S. Müller,
- Bernd Hentschel
The available memory bandwidth of existing high performance computing platforms turns out as being more and more the limitation to various applications. Therefore, modern microarchitectures integrate the memory controller on the processor chip, which ...
Linking performance data into scientific visualization tools
Understanding the performance of program execution is essential when optimizing simulations run on high-performance supercomputers. Instrumenting and profiling codes is itself a difficult task and interpreting the resulting complex data is often ...
- Proceedings of the First Workshop on Visual Performance Analysis
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| VPA '15 | 6 | 5 | 83% |
| Overall | 6 | 5 | 83% |