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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chandramowlishwaran, A. Vuduc, R.W. |
| Copyright Year | 2012 |
| Abstract | We present new analysis, algorithmic techniques, and implementations of the Fast Multipole Method (FMM) for solving N-body problems. Our research specifically addresses two key challenges. The first challenge is how to engineer fast code for today's platforms. We present the first in-depth study of multicore optimizations and tuning for FMM, along with a systematic approach for transforming a conventionally parallelized FMM into a highly-tuned one. We introduce novel optimizations that significantly improve the within-node scalability of the FMM, thereby enabling high-performance in the face of multicore and many core systems. The second challenge is how to understand scalability on future systems. We present a new algorithmic complexity analysis of the FMM that considers both intra- and inter-node communication costs. This analysis yields the surprising prediction that although the FMM is largely compute-bound today, and therefore highly scalable on current systems, the trajectory of processor architecture designs -- if there are no significant changes -- could cause it to become communication-bound as early as the year 2020. This prediction suggests the utility of our analysis approach, which directly relates algorithmic and architectural characteristics, for enabling a new kind of high-level algorithm-architecture co-design. |
| Starting Page | 2462 |
| Ending Page | 2465 |
| File Size | 383586 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467309745 |
| DOI | 10.1109/IPDPSW.2012.303 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-21 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Multicore processing Computational modeling Scalability Predictive models Tuning Optimization |
| Content Type | Text |
| Resource Type | Article |
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