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A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

Coupling SIMD and SIMT architectures to boost performance of a phylogeny-aware alignment kernel.

Nikolaos Alachiotis¹, Simon A Berger, Alexandros Stamatakis

¹The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Germany. Nikolaos.Alachiotis@h-its.org

BMC Bioinformatics

|August 11, 2012

Summary

Accelerating DNA read alignment with PaPaRa using vector intrinsics and GPUs significantly boosts performance. This optimization allows for faster analysis of large phylogenetic datasets by leveraging both CPU and GPU architectures.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Short DNA read alignment is crucial for determining biological origin and phylogenetic analysis.
The PaPaRa tool uses a dynamic programming algorithm to align reads to reference alignments and evolutionary trees.
The algorithm's computational demands necessitate exploring acceleration techniques.

Purpose of the Study:

To optimize and parallelize the PaPaRa alignment kernel for improved performance.
To investigate the use of vector intrinsics (SIMD) and Graphics Processing Units (GPUs) for acceleration.

Main Methods:

Implemented Single Instruction, Multiple Data (SIMD) SSE 4.1 intrinsics and multi-threading for CPU optimization.
Utilized OpenCL for Single Instruction, Multiple Threads (SIMT) execution on GPUs.
Developed a hybrid CPU-GPU system combining both approaches.

Main Results:

Achieved a 9-fold acceleration on a single CPU core and linear speedups with core count.
Reached peak CPU performance of 18.1 GCUPS and average of 12.33 GCUPS.
Attained peak GPU performance of 22.1 GCUPS and average of 18.4 GCUPS, with a hybrid system reaching 33.8 GCUPS peak.

Conclusions:

The accelerated PaPaRa version offers substantial performance gains for analyzing large datasets.
SIMD and SIMT architectures show comparable performance for this dynamic programming kernel.
Hybrid CPU-GPU systems with effective load balancing significantly enhance overall performance.