arXiv (All)
open access
Distributed Linear Programming on GPU Clusters at Extreme Scale
Large linear programs can exceed the memory of a single compute node. Although first-order methods replace sparse factorizations with GPU-suited matrix-vector products, other solver phases can reintroduce a single-node memory limit. We present SHARDLP, a distributed GPU LP solver that keeps the matr
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Distributed Linear Programming on GPU Clusters at Extreme Scale#667973
- GPU-Accelerated First-Order Method with Randomized Sampling for Binary Integer Programs#1062806
- dSTAR: Straggler Tolerant and Byzantine Resilient Distributed SGD#1035272
- RAFI -- A Ray/Work Forwarding Infrastructure for Data Parallel Multi-Node/Multi-GPU Computing#238560
- Magnum.np.distributed: Accelerating Finite Difference Micromagnetic Simulations with Multiple GPUs#246525
Record · ID 986711
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive