Analyzing Data Locality on GPU Caches Using Static Profiling of Workloads
The diversity of workloads drives studies to use GPU more effectively to overcome the limited memory of GPUs. Precisely, it is essential to understand and utilize data locality of workloads to utilize the memory and cache efficiently, which is relatively smaller than CPU ’ s. It is import...
Main Authors: | Jieun Kim, Hyeonsang Eom, Yoonhee Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10225495/ |
Similar Items
-
GPGPU Task Scheduling Technique for Reducing the Performance Deviation of Multiple GPGPU Tasks in RPC-Based GPU Virtualization Environments
by: Jihun Kang, et al.
Published: (2021-03-01) -
Locality-Based Cache Management and Warp Scheduling for Reducing Cache Contention in GPU
by: Juan Fang, et al.
Published: (2021-10-01) -
GPU Hızlandırmalı Veri Demetleme Algoritmalarının İncelenmesi
by: Murat Hacıömeroğlu, et al.
Published: (2013-04-01) -
Prediction-Based Error Correction for GPU Reliability with Low Overhead
by: Hyunyul Lim, et al.
Published: (2020-11-01) -
A Survey of Cache Bypassing Techniques
by: Sparsh Mittal
Published: (2016-04-01)