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Creators/Authors contains: "Bakita, Joshua"

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  1. ; (, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)
    Mancuso, Renato (Ed.)
    As GPU-using tasks become more common in embedded, safety-critical systems, efficiency demands necessitate sharing a single GPU among multiple tasks. Unfortunately, existing ways to schedule multiple tasks onto a GPU often either result in a loss of ability to meet deadlines, or a loss of efficiency. In this work, we develop a system-level spatial compute partitioning mechanism for NVIDIA GPUs and demonstrate that it can be used to execute tasks efficiently without compromising timing predictability. Our tool, called nvtaskset, supports composable systems by not requiring task, driver, or hardware modifications. In our evaluation, we demonstrate sub-1-μs overheads, stronger partition enforcement, and finer-granularity partitioning when using our mechanism instead of NVIDIA’s Multi-Process Service (MPS) or Multi-instance GPU (MiG) features. 
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  2. ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available March 12, 2026
  3. ; (, IEEE)
    Free, publicly-accessible full text available May 6, 2026
  4. ; (, IEEE)
    Full Text Available 
  5. ; (, Proceedings of the 30th IEEE Real-Time and Embedded Technology and Appli cations Symposium)
    Accepted Manuscript Full Text Available
  6. ; (, Proceedings of the 29th IEEE Real-Time and Embedded Technology and Applications Symposium)
    Embedded and autonomous systems are increasingly integrating AI/ML features, often enabled by a hardware accelerator such as a GPU. As these workloads become increasingly demanding, but size, weight, power, and cost constraints remain unyielding, ways to increase GPU capacity are an urgent need. In this work, we provide a means by which to spatially partition the computing units of NVIDIA GPUs transparently, allowing oft-idled capacity to be reclaimed via safe and effcient GPU sharing. Our approach works on any NVIDIA GPU since 2013, and can be applied via our easy-to-use, user-space library titled libsmctrl. We back the design of our system with deep investigations into the hardware scheduling pipeline of NVIDIA GPUs. We provide guidelines for the use of our system, and demonstrate it via an object detection case study using YOLOv2. 
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  7. ; (, roceedings of the 43rd IEEE Real-Time Systems Symposium)
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  8. ; ; ; ; (, Proceedings of the 28th IEEE Real-Time and Embedded Technology and Applications Symposium)
    Full Text Available 
  9. ; ; ; ; ; (, Proceedings of the 42nd IEEE Real-Time Systems Symposium)
    Full Text Available 
  10. ; ; ; ; ; ; (, Proceedings of the 27th IEEE Real-Time and Embedded Technology and Applications Symposium)
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