Optimization Techniques and Performance Analyses of Two Life Science Algorithms for Novel GPU Architectures

In this paper we evaluate two life science algorithms, namely Needleman-Wunsch sequence alignment and Direct Coulomb Summation, for GPUs. Whereas for Needleman-Wunsch it is difficult to get good performance numbers, Direct Coulomb Summation is particularly suitable for graphics cards. We present sev...

Full description

Saved in:
Bibliographic Details
Published in2012 20th Euromicro International Conference on Parallel, Distributed and Network-based Processing pp. 376 - 383
Main Authors Dilch, D., Mehofer, E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2012
Subjects
Computer architecture
CUDA
Fermi
Graphics processing unit
Hardware
Instruction sets
Kernel
life science algorithms
OpenCL
Optimization
optimizations
performance evaluation
Synchronization
Online AccessGet full text

Cover

More Information
Summary:In this paper we evaluate two life science algorithms, namely Needleman-Wunsch sequence alignment and Direct Coulomb Summation, for GPUs. Whereas for Needleman-Wunsch it is difficult to get good performance numbers, Direct Coulomb Summation is particularly suitable for graphics cards. We present several optimization techniques, analyze the theoretical potential of the optimizations with respect to the algorithms, and measure the effect on execution times. We target the recent NVIDIA Fermi architecture to evaluate the performance impacts of novel hardware features like the cache subsystem on optimizing transformations. We compare the execution times of CUDA and OpenCL code versions for Fermi and predecessor models with parallel OpenMP versions executed on the main CPU.
ISBN:1467302260
9781467302265
ISSN:1066-6192
2377-5750
DOI:10.1109/PDP.2012.87