PortLBM: A Portable Lattice Boltzmann Tool Leveraging SYCL on AMD, NVIDIA, and Intel GPUs
2026-07-24T08:52:23Z•921d069f14d0a10c01c5d5a5f3eee49b99ab3cd43a1e5b8aee41eac85b47f205
AMDCPDPDDPFPGAGPUIntelLattice Boltzmann (LBM)MultimmitNVIDIASYCLTSPblockchainconsensus algorithmsconsensus numberdata-parallel trainingdistributed ML trainingdistributed systemsfinalityhardware accelerationintuitionistic fuzzy setsperformance portabilityprivacyreputation systemsstate machine replicationuntraceable cryptocurrency
What happened
This document is an arXiv feed (multiple CS papers) announcing new research across systems, distributed computing, and applied ML/hardware. Highlights include PortLBM — a SYCL-based portable Lattice Boltzmann framework evaluated on AMD/NVIDIA/Intel GPUs showing vendor-specific tuning trade-offs; a reputation-aware uninorm-driven consensus framework using intuitionistic fuzzy sets for fairer blockchain validator selection; formal analysis of linear vs. constant untraceable asset-transfer objects and their consensus numbers/privacy trade-offs; Multimmit — a multi-chain SMR protocol for faster,鲁?
Why it matters
A reviewed impact interpretation has not been published for this record.
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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