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Analysis of Parallel Algorithms on SMP Node and Cluster of Workstations Using Parallel Programming Models with New
1Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.
Bioinformatics and Biology Insights
|December 10, 2016
Summary
This study optimizes DNA sequence alignment using parallel programming. We explored OpenMP and MPI tradeoffs, developing new techniques for efficient load balancing and cache optimization on multicore architectures.
Area of Science:
- Bioinformatics
- Computational Biology
- Computer Science
Background:
- Sequence alignment is crucial for understanding DNA relationships.
- Existing algorithms vary in efficiency and sequence modeling.
- Parallel programming on modern supercomputers presents challenges.
Purpose of the Study:
- To achieve optimal alignment for biological DNA sequences.
- To analyze and optimize parallel algorithm performance, focusing on time, speedup, and efficiency.
- To explore the tradeoffs between OpenMP and Message Passing Interface (MPI) for parallel DNA sequence alignment.
Main Methods:
- Investigated parallel programming paradigms: OpenMP and MPI.
- Developed a communication model to approximate overhead in OpenMP loops.
- Created custom load balancing and cache optimization techniques for the message passing model.
Main Results:
- Demonstrated significant communication overhead in OpenMP, increasing with core count.
- Showcased that custom techniques yield optimal parallel algorithm performance across various multicore architectures.
- Validated performance with diverse input parameters like sequence and tile size.
Conclusions:
- OpenMP and MPI present distinct scalability and communication overhead challenges.
- Developed optimization techniques significantly enhance parallel DNA sequence alignment efficiency.
- The study offers valuable insights for optimizing high-performance computing in bioinformatics.
