arXiv (All)
open access
FLARE: A Dataflow-Aware and Scalable Hardware Architecture for Neural-Hybrid Scientific Lossy Compression
Scientific simulation leveraging high-performance computing (HPC) systems is crucial for modeling complex systems and phenomena in fields such as astrophysics, climate science, and fluid dynamics, generating massive datasets that often reach petabyte to exabyte scales. However, managing these vast 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
- Two bits about lossy compression: On the limits of compression in cosmology#1154502
- Schedules Are Solvable Symbols: Tuning-Free Compilation of Tile Programs on Dataflow Architectures#1077148
- GRADSOLVE: fast exact gradients for ODE ensembles on GPUs#924992
- GRADSOLVE: fast exact gradients for ODE ensembles on GPUs#920758
- Mixed-precision GPU algorithms for efficient turbulent flow simulations with Raviart-Thomas finite elements#1014410
Record · ID 974205
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