Parallel sparse matrix-matrix multiplication: a scalable solution with 1D algorithm
by Mohammad Asadul Hoque; Md Rezaul Karim Raju; Christopher John Tymczak; Daniel Vrinceanu; Kiran Chilakamarri
International Journal of Computational Science and Engineering (IJCSE), Vol. 11, No. 4, 2015

Abstract: This paper presents a novel implementation of parallel sparse matrix-matrix multiplication using distributed memory systems on heterogeneous hardware architecture. The proposed algorithm is expected to be linearly scalable up to several thousands of processors for matrices with dimensions over 106 (million). Our approach of parallelism is based on 1D decomposition and can work for both structured and unstructured sparse matrices. The storage mechanism is based on distributed hash lists, which reduces the latency for accessing and modifying an element of the product matrix, while reducing the overall merging time of the partial results computed by the processors. Theoretically, the time and space complexity of our algorithm is linearly proportional to the total number of non-zero elements in the product matrix C. The results of the performance evaluation show that the algorithm scales much better for sparse matrices with bigger dimensions. The speedup achieved using our algorithm is much better than other existing 1D algorithms. We have been able to achieve about 500 times speedup with only 672 processors. We also identified the impact of hardware architecture on scalability.

Online publication date: Thu, 10-Dec-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email