• Corpus ID: 38066628

Operating Systems Challenges for GPU Resource Management

@inproceedings{Kato2011OperatingSC,
  title={Operating Systems Challenges for GPU Resource Management},
  author={Shinpei Kato and Scott A. Brandt and Yutaka Ishikawa and Ragunathan Raj Rajkumar},
  year={2011}
}
The graphics processing unit (GPU) is becoming a very powerful platform to accelerate graphics and data-paralle l compute-intensive applications. [] Key Method A list of operating systems challenge s is also provided to highlight future directions of this rese arch domain, including specific ideas of GPU scheduling for realtime systems. Our preliminary evaluation demonstrates tha t the performance of open-source software is competitive wit h that of proprietary software, and hence operating systems r esearch…
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20 Citations
Highly Influential Citations
2
Background Citations
9
Methods Citations
3

20 Citations

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Citation Type

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Two conceptual frameworks for GPU applications to adjust their task execution times based on total workload are presented, which enable smart GPU resource management when many applications share GPU resources while the workloads of those applications vary.
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This paper presents an operating system extension that allows leveraging the GPU accelerator memory for operating system purposes, and utilizes graphics card memory as cache for virtual memory pages, which can improve the overall system responsiveness, especially under heavy load.
A GPU Kernel Transactionization Scheme for Preemptive Priority Scheduling
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This work proposes an approach to transactionize GPU kernels at the operating system (OS) level and limits the preemption delay to 18 μs in the 99.9th percentile with an average delay in execution time less than 10 % in most cases for high-priority tasks under a heavy load.
Towards using the Graphics Processing Unit (GPU) for embedded systems
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Proceedings of the 4th Workshop on Adaptive and Reconfigurable Embedded Systems Workshop Organization Program Chairs Program Committee Steering Committee  Network Challenges in Cyber-physical Systems Session 1: Adaptive Resource Management  towards Adaptive Gpu Resource Management for Embedded Rea
TLDR
Two conceptual frameworks for GPU applications to adjust their task execution times based on total workload are presented, which enable smart GPU resource management when many applications share GPU resources while the workloads of those applications vary.
EDGE: Event-Driven GPU Execution
TLDR
An event-driven GPU execution model that enables non-CPU devices to directly launch preconfigured tasks on a GPU without CPU interaction is proposed, and it is estimated that EDGE can reduce the kernel launch latency by 4.4x compared to the baseline CPU-launched approach.
Accelerated test execution using GPUs
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