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Hybrid Parallelism for Volume Rendering on Large-, Multi-, and Many-Core Systems.

M Howison, E W Bethel, H Childs

IEEE Transactions on Visualization and Computer Graphics

|February 2, 2011

Summary

Hybrid parallelism enhances raycasting volume rendering on modern multi-core systems. This approach optimizes communication costs, significantly improving performance for large-scale scientific visualization.

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Area of Science:

Computer Science
Scientific Visualization
High-Performance Computing

Background:

The trend towards multi- and many-core processors necessitates efficient parallelization strategies.
Standard visualization algorithms like raycasting volume rendering face scalability challenges on modern hardware.

Purpose of the Study:

To investigate the benefits of a hybrid parallelism approach for raycasting volume rendering.
To evaluate the performance gains of hybrid parallelism on large-scale datasets and high concurrency levels.

Main Methods:

Implementing a hybrid parallelism strategy combining distributed-memory and shared-memory parallelism.
Conducting weak and strong scaling studies with up to 216,000 concurrent processes.
Analyzing performance on datasets up to 12.2 trillion cells.

Main Results:

Hybrid parallelism significantly improves the performance of raycasting volume rendering.
The primary benefit is observed in reducing communication overhead, which is dominant at high concurrency.
Performance gains are demonstrated across a wide range of concurrency levels and data sizes.

Conclusions:

Hybrid parallelism is an effective strategy for optimizing scientific visualization algorithms on contemporary computing architectures.
Reducing the number of participating nodes through hybrid approaches yields substantial performance improvements.
This method addresses the challenges of large-scale data processing in scientific visualization.