Paper Info
Title
Exploring Parallelism in MiBench with Loop and Procedure Level Speculation
Abstract
The effective utilization of the abundant computation resources provided by Chip Multi-Processor (CMP? to speedup serial programs, has been received with various methods by many researchers making it a major research hotspot. However, embedded applications have not yet been thoroughly examined in thread level speculation (TLS), as compared to how researchers have focused and addressed other areas. In this paper, we propose kernel data structures of loop and procedure level speculation to accelerate serial programs. To verify the hypothesis, we choose some applications from Mibench, discuss codes and the impact of TLS technology features on the speedup such as coverage parallelism, dependence features, threads size, and core numbers. The experiment results prove that firstly, speculative thread level parallelism is better than instruction level parallel technology. Secondly, the best dijkstra application has a result 13.3x speedup in loop level speculation and a 29.7x speedup in procedure level. Thirdly, in the field of embedded applications, the TLS technology can effectively utilize resources of 4 to 8 core computing and procedure level speculation is better than loop level speculation.
Year
DOI
Venue
2018
10.1109/BDCloud.2018.00033
2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom)
Keywords
Field
DocType
Chip Multi-Processor, Mibench, thread level speculation
Kernel (linear algebra),Speculation,Data structure,Computer science,Task parallelism,Parallel computing,Speculative multithreading,Thread (computing),Human–computer interaction,Speedup,Dijkstra's algorithm
Conference
ISSN
ISBN
Citations 
2158-9178
978-1-7281-1141-4
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Deqing Bu100.34
Yaobin Wang201.01
Ling Li33118.52
Zhi-qin Liu4124.93
Wenxin Yu5612.26
Manasah Musariri600.34