High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP

This paper explores the pros and cons of reconfigurable computing in the form of FPGAs for high performance efficient computing. In particular, the paper presents the results of a comparative study between three different acceleration technologies, namely, Field Programmable Gate Arrays (FPGAs), Graphics Processor Units (GPUs), and IBM’s Cell Broadband Engine (Cell BE), in the design and implementation of the widely-used Smith-Waterman pairwise sequence alignment algorithm, with general purpose processors as a base reference implementation. Comparison criteria include speed, energy consumption, and purchase and development costs. The study shows that FPGAs largely outperform all other implementation platforms on performance per watt criterion and perform better than all other platforms on performance per dollar criterion, although by a much smaller margin. Cell BE and GPU come second and third, respectively, on both performance per watt and performance per dollar criteria. In general, in order to outperform other technologies on performance per dollar criterion (using currently available hardware and development tools), FPGAs need to achieve at least two orders of magnitude speed-up compared to general-purpose processors and one order of magnitude speed-up compared to domain-specific technologies such as GPUs

Parallel progressive multiple sequence alignment on reconfigurable meshes

<p>Abstract</p> <p>Background</p> <p>One of the most fundamental and challenging tasks in bio-informatics is to identify related sequences and their hidden biological significance. The most popular and proven best practice method to accomplish this task is aligning multiple sequences together. However, multiple sequence alignment is a computing extensive task. In addition, the advancement in DNA/RNA and Protein sequencing techniques has created a vast amount of sequences to be analyzed that exceeding the capability of traditional computing models. Therefore, an effective parallel multiple sequence alignment model capable of resolving these issues is in a great demand.</p> <p>Results</p> <p>We design <it>O</it>(1) run-time solutions for both local and global dynamic programming pair-wise alignment algorithms on reconfigurable mesh computing model. To align <it>m </it>sequences with max length <it>n</it>, we combining the parallel pair-wise dynamic programming solutions with newly designed parallel components. We successfully reduce the progressive multiple sequence alignment algorithm's run-time complexity from <it>O</it>(<it>m </it>× <it>n</it>⁴) to <it>O</it>(<it>m</it>) using <it>O</it>(<it>m </it>× <it>n</it>³) processing units for scoring schemes that use three distinct values for match/mismatch/gap-extension. The general solution to multiple sequence alignment algorithm takes <it>O</it>(<it>m </it>× <it>n</it>⁴) processing units and completes in <it>O</it>(<it>m</it>) time.</p> <p>Conclusions</p> <p>To our knowledge, this is the first time the progressive multiple sequence alignment algorithm is completely parallelized with <it>O</it>(<it>m</it>) run-time. We also provide a new parallel algorithm for the Longest Common Subsequence (LCS) with <it>O</it>(1) run-time using <it>O</it>(<it>n</it>³) processing units. This is a big improvement over the current best constant-time algorithm that uses <it>O</it>(<it>n</it>⁴) processing units.</p

SWAPHI: Smith-Waterman Protein Database Search on Xeon Phi Coprocessors

The maximal sensitivity of the Smith-Waterman (SW) algorithm has enabled its wide use in biological sequence database search. Unfortunately, the high sensitivity comes at the expense of quadratic time complexity, which makes the algorithm computationally demanding for big databases. In this paper, we present SWAPHI, the first parallelized algorithm employing Xeon Phi coprocessors to accelerate SW protein database search. SWAPHI is designed based on the scale-and-vectorize approach, i.e. it boosts alignment speed by effectively utilizing both the coarse-grained parallelism from the many co-processing cores (scale) and the fine-grained parallelism from the 512-bit wide single instruction, multiple data (SIMD) vectors within each core (vectorize). By searching against the large UniProtKB/TrEMBL protein database, SWAPHI achieves a performance of up to 58.8 billion cell updates per second (GCUPS) on one coprocessor and up to 228.4 GCUPS on four coprocessors. Furthermore, it demonstrates good parallel scalability on varying number of coprocessors, and is also superior to both SWIPE on 16 high-end CPU cores and BLAST+ on 8 cores when using four coprocessors, with the maximum speedup of 1.52 and 1.86, respectively. SWAPHI is written in C++ language (with a set of SIMD intrinsics), and is freely available at http://swaphi.sourceforge.net.Comment: A short version of this paper has been accepted by the IEEE ASAP 2014 conferenc