arXiv (All)
open access
WarpDRF: Data-Race Freedom for Warp-Level Programming
Warp primitives such as tensor core operations, shuffles, reductions, and barriers are critical to high-performance GPU kernels, and every major GPU language supports some set of them. The threads that participate in a primitive, and therefore synchronize, are determined dynamically by how threads d
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
- Byzantine-linearizable Single-Writer Multi-Reader Register in the GPU-Inspired Shared Memory Model#1325439
- GPU-Accelerated First-Order Method with Randomized Sampling for Binary Integer Programs#1062806
- SURGE: SuperBatch Unified Resource-efficient GPU Encoding for Heterogeneous Partitioned Data#155262
- Taming Bitwise Behavior in GPU Kernels with Tensor Core: Black-Box Reconstruction, Compiler Enforcement, and Static Verification#673469
- FlashGPU-sim: Enabling GPU Modeling for Modern Architectures and AI Workloads#1109341
Record · ID 1216916
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