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Self-Assembly of Gamma-Modified Peptide Nucleic Acids into Complex Nanostructures in Organic Solvent Mixtures

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Published on: June 26, 2020

Optimizing Data Intensive GPGPU Computations for DNA Sequence Alignment.

Cole Trapnell¹, Michael C Schatz

¹Center for Bioinformatics and Computational Biology, University of Maryland.

Parallel Computing

|February 18, 2010

Summary

This summary is machine-generated.

MUMmerGPU 2.0 accelerates DNA sequence alignment using graphics processing units (GPUs), offering significant speedups for genomics and disease genotyping applications.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing generates vast amounts of data requiring efficient alignment tools.
Existing alignment algorithms can be computationally intensive, limiting throughput for large-scale projects.

Purpose of the Study:

To enhance the performance of DNA sequence alignment using graphics processing units (GPUs).
To introduce MUMmerGPU 2.0, an optimized version of the MUMmerGPU software for faster sequence alignment.

Main Methods:

Utilized highly-parallel commodity graphics processing units (GPUs) for accelerating DNA sequence alignment.
Implemented a new stackless depth-first-search print kernel in MUMmerGPU 2.0.
Exhaustively examined 128 GPU data layout configurations to optimize performance.

Main Results:

MUMmerGPU 2.0 achieved a 13x speedup compared to the serial CPU version.
Total computation time was nearly 4x faster than MUMmerGPU 1.0.
Higher GPU occupancy demonstrated a greater impact on performance than reduced latency.

Conclusions:

MUMmerGPU 2.0 provides a substantial performance improvement for DNA sequence alignment.
GPU acceleration is highly effective for data-intensive bioinformatics tasks.
Optimization of GPU data layouts, particularly occupancy, is crucial for maximizing alignment speed.