Next Article in Journal
Sandor Type Fuzzy Inequality Based on the (s,m)-Convex Function in the Second Sense
Next Article in Special Issue
Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing
Previous Article in Journal
NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier
Previous Article in Special Issue
Blockchain Security in Cloud Computing: Use Cases, Challenges, and Solutions
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Symmetry 2017, 9(9), 180;

Qinling: A Parametric Model in Speculative Multithreading

1,†,* and 2
School of Electronic and Information Engineering, Xi’an Jiaotong University, No 28, Xianning West Road, Xi’an 710049, China
College of Information Engineering, NorthWest Agriculture and Forestry University, No 22, Xinong Road, Yangling 712100, China
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 22 July 2017 / Revised: 20 August 2017 / Accepted: 28 August 2017 / Published: 2 September 2017
(This article belongs to the Special Issue Advanced in Artificial Intelligence and Cloud Computing)
Full-Text   |   PDF [1484 KB, uploaded 8 September 2017]   |  


Speculative multithreading (SpMT) is a thread-level automatic parallelization technique that can accelerate sequential programs, especially for irregular applications that are hard to be parallelized by conventional approaches. Thread partition plays a critical role in SpMT. Conventional machine learning-based thread partition approaches applied machine learning to offline guide partition, but could not explicitly explore the law between partition and performance. In this paper, we build a parametric model (Qinling) with a multiple regression method to discover the inherent law between thread partition and performance. The paper firstly extracts unpredictable parameters that determine the performance of thread partition in SpMT; secondly, we build a parametric model Qinling with extracted parameters and speedups, and train Qinling offline, as well as apply it to predict the theoretical speedups of unseen applications. Finally, validation is done. Prophet, which consists of an automatic parallelization compiler and a multi-core simulator, is used to obtain real speedups of the input programs. Olden and SPEC2000 benchmarks are used to train and validate the parametric model. Experiments show that Qinling delivers a good performance to predict speedups of unseen programs, and provides feedback guidance for Prophet to obtain the optimal partition parameters. View Full-Text
Keywords: parametric model; speculative multithreading; Prophet parametric model; speculative multithreading; Prophet

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Li, Y.; Zhao, Y.; Liu, B. Qinling: A Parametric Model in Speculative Multithreading. Symmetry 2017, 9, 180.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top