Device Memory Sharing Study in Broadcast Systems
Metadata
- Publisher
- SMPTE
- Doc Type
- Journal Article
- Content Type
- Original Research
- Abbreviated Title
- SMPTE Motion Imaging Jour.
- Volume
- 135, No. 3, pp. 16–24
- Abstract
- Broadcast systems continue their transition to standard commercial off-the-shelf (COTS) computing platforms and growing reliance on Graphic Processing Unit (GPU)-accelerated processing for real-time media transcoding, AI, and image manipulation demands optimized data exchange within compute nodes and across distributed systems. This shift introduces new challenges in memory sharing and communication. Using internode 1 Remote Direct Memory Access (RDMA): This study presents a comparative evaluation of memory sharing mechanisms across three distinct GPU transfer paths used in High-Performance Computing (HPC) and media processing: inter-node GPU-GPU, intra-node host-GPU and intra-node GPU-GPU memory exchange. The performance of each path is assessed under different software configurations: native memory operations without the aid of any communication framework-referred to as native, and higher-level abstractions, like Unified Communication X (UCX) 2 and Libfabric. 3 ’ Key performance metrics, including PCle 4 (Peripheral Component Interconnect Express) bandwidth, latency, and Central Processing Unit (CPU) utilization, are measured. These results are intended to support the development of applications that require efficient and cost-effective memory sharing between computational devices. Additionnally, this study highlights how higher-level communication frameworks provide flexible abstraction.
- Publication Date
- 2026-05-01
- DOI
10.5594/JMI.2026/TEYI3961- ISSN
- Print:
1545-0279| Electronic:2160-2492 - Link
- https://doi.org/10.5594/JMI.2026/TEYI3961
- Author(s)
- Jonas OhlandMichael LefebvreThomas J. TrueGareth Sylvester-BradleySithideth Viengkhou
- Keyword(s)
- GPU, Libfabric, UCX, COTS, Media Exchange Layer (MXL), RDMA, CUDA
- Copyright
- © 2026 SMPTE
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Jonas Ohland, Michael Lefebvre, Thomas J. True, Gareth Sylvester-Bradley, and Sithideth Viengkhou; Device Memory Sharing Study in Broadcast Systems, MIJ 2026, Volume 135, Number 3 (pp. 16 to 24); SMPTE, 2026. Available at https://doi.org/10.5594/JMI.2026/TEYI3961
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Jonas Ohland, Michael Lefebvre, Thomas J. True, Gareth Sylvester-Bradley, and Sithideth Viengkhou; Device Memory Sharing Study in Broadcast Systems, MIJ 2026, Volume 135, Number 3 (pp. 16 to 24); SMPTE, 2026. Available at https://doi.org/10.5594/JMI.2026/TEYI3961
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Jonas Ohland, Michael Lefebvre, Thomas J. True, Gareth Sylvester-Bradley, and Sithideth Viengkhou; Device Memory Sharing Study in Broadcast Systems, MIJ 2026, Volume 135, Number 3 (pp. 16 to 24); SMPTE, 2026. Available at https://doi.org/10.5594/JMI.2026/TEYI3961
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<span class="citation">Jonas Ohland, Michael Lefebvre, Thomas J. True, Gareth Sylvester-Bradley, and Sithideth Viengkhou; <cite>Device Memory Sharing Study in Broadcast Systems</cite>, MIJ 2026, Volume 135, Number 3 (pp. 16 to 24); SMPTE, 2026. Available at <a href="https://doi.org/10.5594/JMI.2026/TEYI3961" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2026/TEYI3961</a></span>
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Jonas Ohland, Michael Lefebvre, Thomas J. True, Gareth Sylvester-Bradley, and Sithideth Viengkhou; Device Memory Sharing Study in Broadcast Systems, MIJ 2026, Volume 135, Number 3 (pp. 16 to 24); SMPTE, 2026
doi: 10.5594/JMI.2026/TEYI3961
url: https://doi.org/10.5594/JMI.2026/TEYI3961
doi: 10.5594/JMI.2026/TEYI3961
url: https://doi.org/10.5594/JMI.2026/TEYI3961
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<li> Jonas Ohland, Michael Lefebvre, Thomas J. True, Gareth Sylvester-Bradley, and Sithideth Viengkhou; <cite id="bib-10-5594-jmi-2026-teyi3961">Device Memory Sharing Study in Broadcast Systems</cite>, MIJ 2026, Volume 135, Number 3 (pp. 16 to 24); SMPTE, 2026 <span class="doi">10.5594/JMI.2026/TEYI3961</span> </li>