Scientific Programming

Scientific Programming / 2009 / Article
Special Issue

Software Development for Multi-core Computing Systems

View this Special Issue

Open Access

Volume 17 |Article ID 681708 |

John E. Savage, Mohammad Zubair, "Evaluating Multicore Algorithms on the Unified Memory Model", Scientific Programming, vol. 17, Article ID 681708, 14 pages, 2009.

Evaluating Multicore Algorithms on the Unified Memory Model


One of the challenges to achieving good performance on multicore architectures is the effective utilization of the underlying memory hierarchy. While this is an issue for single-core architectures, it is a critical problem for multicore chips. In this paper, we formulate the unified multicore model (UMM) to help understand the fundamental limits on cache performance on these architectures. The UMM seamlessly handles different types of multiple-core processors with varying degrees of cache sharing at different levels. We demonstrate that our model can be used to study a variety of multicore architectures on a variety of applications. In particular, we use it to analyze an option pricing problem using the trinomial model and develop an algorithm for it that has near-optimal memory traffic between cache levels. We have implemented the algorithm on a two Quad-Core Intel Xeon 5310 1.6 GHz processors (8 cores). It achieves a peak performance of 19.5 GFLOPs, which is 38% of the theoretical peak of the multicore system. We demonstrate that our algorithm outperforms compiler-optimized and auto-parallelized code by a factor of up to 7.5.

Copyright © 2009 Hindawi Publishing Corporation. 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.

Related articles

No related content is available yet for this article.
 PDF Download Citation Citation
 Order printed copiesOrder

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.