PortLBM: A Portable Lattice Boltzmann Tool Leveraging SYCL on AMD, NVIDIA, and Intel GPUs
arXiv 2607.20650•921d069f14d0a10c01c5d5a5f3eee49b99ab3cd43a1e5b8aee41eac85b47f205
AMDCPDPDDPFPGAGPUIntelLattice Boltzmann (LBM)MultimmitNVIDIASYCLTSPblockchainconsensus algorithmsconsensus numberdata-parallel trainingdistributed ML trainingdistributed systemsfinalityhardware accelerationintuitionistic fuzzy setsperformance portabilityprivacyreputation systemsstate machine replicationuntraceable cryptocurrency
Paper metadata
- arXiv ID
- 2607.20650
- Version
- Not specified by this published record
- Category
- Computer Science — Distributed, Parallel, and Cluster Computing (cs.DC)
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Evidence and limitations
- Source ID
- arxiv_cs_dc
- Record identifier
- 921d069f14d0a10c01c5d5a5f3eee49b99ab3cd43a1e5b8aee41eac85b47f205
- Enrichment time
- 2026-07-24T08:52:23Z
- AI-assisted enrichment
- Yes
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