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Updated: Jun 23, 2026
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Published on: April 8, 2020
A compute unified system architecture for graphics clusters incorporating data locality.
Christoph Müller1, Steffen Frey, Magnus Strengert
1Visualisierungsinstitut der Universität Stuttgart, Stuttgart, Germany. christoph.mueller@vis.uni-stuttgart.de
Summary
This research introduces a development environment for distributed graphics processing unit (GPU) computing, enhancing scalability for multi-GPU systems and clusters. The system optimizes data locality and GPU utilization for faster execution in parallel processing.
Area of Science:
- Computer Science
- High-Performance Computing
- Parallel Computing
Background:
- Distributed GPU computing is crucial for accelerating complex computations.
- Existing systems often face scalability challenges in multi-GPU and cluster environments.
- Graphics Processing Units (GPUs) offer significant parallel processing capabilities.
Purpose of the Study:
- To present a development environment for distributed GPU computing.
- To extend the CUDA parallel programming model to higher levels of parallelism (PCI bus, network interconnects).
- To enhance scalability and performance in multi-GPU systems and graphics clusters.
Main Methods:
- Developed a system based on CUDA, extending its programming model.
- Implemented an extended API mimicking global memory across distribution layers.
- Introduced an automatic, data-locality-aware GPU-accelerated scheduling mechanism.
- Handled underlying communication mechanisms transparently for developers.
Main Results:
- The system effectively extends parallelism to PCI bus and network interconnects.
- The data-locality-aware scheduler significantly reduces transmitted data.
- Improved GPU utilization and faster execution times were observed.
- Demonstrated performance and scalability on multi-GPU systems and graphics clusters.
Conclusions:
- The presented development environment enhances distributed GPU computing.
- The system offers high scalability, particularly in network-interconnected environments.
- Automatic scheduling based on data locality is key to efficient distributed GPU computing.
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