arXiv (All)
open access
Argus: Orchestrating Cross-Layer GPU Performance Measurements around Semantic Regions
GPU developers and automated optimizers need performance evidence for semantic code regions--such as neural-network operator implementations and pipeline stages--but this evidence is fragmented across profiling tools. Answering a region-level question can require manually constructing probes and pro
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
- SURGE: SuperBatch Unified Resource-efficient GPU Encoding for Heterogeneous Partitioned Data#155262
- GPUAlert: A Zero-Instrumentation Process-Boundary Monitor for Diagnosing GPU Training-Job Failures#332616
- Exploiting Dependency and Parallelism: Real-Time Scheduling and Analysis for GPU Tasks#2742
- TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters#405624
- KREX: Concurrent Kernel Benchmarking on Shared GPUs via Region-Granular Exclusivity#1108864
Record · ID 1000257
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