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GPU-Initiated Discrete Simulated Bifurcation: Low-Latency Requests and Streaming Dense Couplings — arXiv:2610.06622

Source: arXiv
Original: https://arxiv.org/abs/2610.06622
License (as archived): CC0-1.0
Archived: 2026-10-06T05:29:04.974Z
SHA-256 of the archived text: a889b14505606ca0ab2233702af463e97d1ffefca0339a00228e67c61cee45d6
Stable URL of this page: https://metraipolis.com/documento/dc_2178ea85bf2d

How to cite

GPU-Initiated Discrete Simulated Bifurcation: Low-Latency Requests and Streaming Dense Couplings — arXiv:2610.06622 — arXiv, https://arxiv.org/abs/2610.06622 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2178ea85bf2d (Metraipolis archive dc_2178ea85bf2d, sha256 a889b14505606ca0ab2233702af463e97d1ffefca0339a00228e67c61cee45d6)

BibTeX

@misc{dc2178ea85bf2d,
  title = {GPU-Initiated Discrete Simulated Bifurcation: Low-Latency Requests and Streaming Dense Couplings — arXiv:2610.06622},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.06622},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2178ea85bf2d; sha256 a889b14505606ca0ab2233702af463e97d1ffefca0339a00228e67c61cee45d6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.06622. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

GPU-based optimization faces two communication bottlenecks: coordinating frequent requests and delivering dense models that exceed device memory. We present a discrete simulated bifurcation (dSB) architecture that addresses both through NVIDIA DOCA GPUNetIO. For resident models, a persistent service receives field updates, executes each solve within one GPU thread block, and returns the result. Exact integer coupling sums, GPU work queues, and batched transmission keep the receive--solve--reply path on the device without a dedicated CPU data-path core. In comparisons with socket-based servers using the same solver, the largest latency gains occur under concurrent load. As the offered load increases from 400 to 800 thousand requests per second, median round-trip latency rises by only 6\%. At the highest tested load, median and 99th-percentile latencies are 189 and 218~$\mu$s, compared with 288 and 609~$\mu$s for the tuned persistent CPU proxy across repeated runs. For models larger than device memory, a streaming solver retains dynamical state on the GPU and reuses incoming coupling tiles across replicas. It evaluates ten-million-variable dense binary matrices at approximately 307~Gb/s, consuming a 12.5-TB logical matrix through a 64-MiB packet buffer. Ground-state recovery on planted instances and agreement with reference executions verify the computation. Together, the two modes scale dSB to concurrent requests and dense models beyond GPU memory.

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv cs.DC, nuovi lavori del giorno).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC) ; Computational Physics (physics.comp-ph); Quantum Physics (quant-ph).
Pagina del lavoro: https://arxiv.org/abs/2610.06622 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:24:35.320Z dalla pagina https://arxiv.org/list/cs.DC/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).