Skip to content
Free planNo credit card requiredStart writing

article · 2014

The performance impact analysis of loop unrolling

Goran Velkoski, Marjan Gušev, Sasko Ristov

Cited by 9Abstract and details only

Abstract

Loop unrolling is a well known technique, which usually results with speedup of a program that contains loops. The effect is obtained by reducing the operations that require counter increases and branch jumps at the end of the loops. This paper analyzes the impact of loop unrolling on various processor types and memory patterns. The experiments show a high correlation between the cache and the problem size. The loop unrolling results with a higher speedup for the execution of a smaller size problem, while it does not have impact for a problem whose size is greater than the capacity of the last level cache size, due to the huge number of cache misses. Another important result is that the loop unrolling achieves greater speedup on Intel, rather than AMD CPU. In this paper we analyze and discuss the various behaviors of loop unrolling.

Cite this paper

APA 7

Velkoski, G., Gušev, M., & Ristov, S. (2014). The performance impact analysis of loop unrolling. https://doi.org/10.1109/mipro.2014.6859582

MLA 9

Velkoski, Goran, et al. “The performance impact analysis of loop unrolling.” vol. 2, 2014, pp. 307–12, https://doi.org/10.1109/mipro.2014.6859582.

Chicago

Velkoski, Goran, Marjan Gušev, and Sasko Ristov. 2014. “The performance impact analysis of loop unrolling.” https://doi.org/10.1109/mipro.2014.6859582.

Harvard

Velkoski, G., Gušev, M. and Ristov, S. (2014) ‘The performance impact analysis of loop unrolling’. Available at: https://doi.org/10.1109/mipro.2014.6859582.

BibTeX
@article{velkoski2014performance,
  title = {{The performance impact analysis of loop unrolling}},
  author = {Velkoski, Goran and Gušev, Marjan and Ristov, Sasko},
  volume = {2},
  pages = {307--312},
  year = {2014},
  doi = {10.1109/mipro.2014.6859582}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.