TY - JOUR
T1 - Accelerating Phylogenetics Using FPGAs in the Cloud
AU - Alachiotis, Nikolaos
AU - Brokalakis, Andreas
AU - Amourgianos, Vasilis
AU - Ioannidis, Sotiris
AU - Malakonakis, Pavlos
AU - Bokalidis, Tasos
N1 - Publisher Copyright:
© 1981-2012 IEEE.
PY - 2021/7/1
Y1 - 2021/7/1
N2 - 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.
AB - 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.
U2 - 10.1109/MM.2021.3075848
DO - 10.1109/MM.2021.3075848
M3 - Article
VL - 41
SP - 24
EP - 30
JO - IEEE micro
JF - IEEE micro
SN - 0272-1732
IS - 4
M1 - 9416903
ER -