This article presents GRAM (GPU-based Runtime Adaption for Mixed-precision) a framework for the effective use of mixed precision arithmetic for CUDA programs. ...
Feb 9, 2021 · Our method provides a fine-grain tradeoff between output error and performance. It can create many variants that satisfy different accuracy ...
This article presents GRAM ( G PU-based R untime A daption for M ixed-precision) a framework for the effective use of mixed precision arithmetic for CUDA ...
People also ask
What is gram in GPU?
What is the difference between GPU and GPGPU?
GRAM: A Framework for Dynamically Mixing Precisions in GPU Applications. record by Weng-Fai Wong • GRAM: A Framework for Dynamically Mixing Precisions in GPU ...
GRAM: A framework for dynamically mixing precisions in GPU applications. NM Ho, HD silva, WF Wong. ACM Transactions on Architecture and Code Optimization (TACO) ...
ACM Transactions on Architecture and Code Optimization, Volume 18
dblp.org › journals › taco › taco18
... Programs with Interdependent Tuning Parameters via Auto-Tuning Framework (ATF). ... GRAM: A Framework for Dynamically Mixing Precisions in GPU Applications. 19 ...
GRAM: A Frame- work for Dynamically Mixing Precisions in GPU Applications. ... SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities.
Feb 9, 2024 · GRAM: A Framework for Dynamically Mixing Precisions in GPU Applications ... use of mixed precision arithmetic for CUDA programs. Our method ...
Jan 8, 2022 · To address this issue, we propose a compiler framework for optimizing the use of dynamic parallelism in applications with nested parallelism.
Missing: GRAM: Precisions
In this paper, we present a system called AMPT-GA that selects application-level data precisions to maximize performance while satisfying accuracy constraints.