CLC number: TP368.1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2018-02-14
Cited: 0
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Yang Zhang, Zuo-cheng Xing, Cang Liu, Chuan Tang. CWLP: coordinated warp scheduling and locality-protected cache allocation on GPUs[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(2): 206-220.
@article{title="CWLP: coordinated warp scheduling and locality-protected cache allocation on GPUs",
author="Yang Zhang, Zuo-cheng Xing, Cang Liu, Chuan Tang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="2",
pages="206-220",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700059"
}
%0 Journal Article
%T CWLP: coordinated warp scheduling and locality-protected cache allocation on GPUs
%A Yang Zhang
%A Zuo-cheng Xing
%A Cang Liu
%A Chuan Tang
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 2
%P 206-220
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700059
TY - JOUR
T1 - CWLP: coordinated warp scheduling and locality-protected cache allocation on GPUs
A1 - Yang Zhang
A1 - Zuo-cheng Xing
A1 - Cang Liu
A1 - Chuan Tang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 2
SP - 206
EP - 220
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700059
Abstract: As we approach the exascale era in supercomputing, designing a balanced computer system with a powerful computing ability and low power requirements has becoming increasingly important. The graphics processing unit (GPU) is an accelerator used widely in most of recent supercomputers. It adopts a large number of threads to hide a long latency with a high energy efficiency. In contrast to their powerful computing ability, GPUs have only a few megabytes of fast on-chip memory storage per streaming multiprocessor (SM). The GPU cache is inefficient due to a mismatch between the throughput-oriented execution model and cache hierarchy design. At the same time, current GPUs fail to handle burst-mode long-access latency due to GPU&x2019;s poor warp scheduling method. Thus, benefits of GPU&x2019;s high computing ability are reduced dramatically by the poor cache management and warp scheduling methods, which limit the system performance and energy efficiency. In this paper, we put forward a coordinated warp scheduling and locality-protected (CWLP) cache allocation scheme to make full use of data locality and hide latency. We first present a locality-protected cache allocation method based on the instruction program counter (LPC) to promote cache performance. Specifically, we use a PC-based locality detector to collect the reuse information of each cache line and employ a prioritised cache allocation unit (PCAU) which coordinates the data reuse information with the time-stamp information to evict the lines with the least reuse possibility. Moreover, the locality information is used by the warp scheduler to create an intelligent warp reordering scheme to capture locality and hide latency. Simulation results show that CWLP provides a speedup up to 19.8% and an average improvement of 8.8% over the baseline methods.
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