Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/D0548287-BD72-4A1B-A12F-40FE85519E32 | ||
| Year | 2021 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | N. Alachiotis, A. Brokalakis, V. Amourgianos, S. Ioannidis, P. Malakonakis and T. Bokalidis, "Accelerating phylogenetics using FPGAs in the cloud," IEEE Micro, vol. 41, no. 4, pp. 24-30, 1 July-Aug. 2021, doi: 10.1109/MM.2021.3075848. https://doi.org/10.1109/MM.2021.3075848 | ||
| Appears in Collections |
Phylogenetics study the evolutionary history of organisms using an iterative process of creating and evaluating phylogenetic trees. This process is very computationally intensive; constructing a large phylogenetic tree requires hundreds to thousands of CPU hours. In this article, we describe an FPGA-based system that can be deployed on AWS EC2 F1 cloud instances to accelerate phylogenetic analyses by boosting performance of the phylogenetic likelihood function, i.e., a widely employed tree-evaluation function that accounts for up to 95% of the overall analysis time. We exploit domain-specific knowledge to reduce the amount of transferred data that limits overall system performance. Our proof-of-concept implementation reveals that the effective accelerator throughput nearly quadruples with optimized data movement, reaching up to 75% of its theoretical peak and nearly 10× faster processing than a CPU using AVX2 extensions.
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