Design and analysis of an accelerated seed generation stage for BLASTP on the Mercury system - Master\u27s Thesis, August 2006

NCBI BLASTP is a popular sequence analysis tool used to study the evolutionary relationship between two protein sequences. Protein databases continue to grow exponentially as entire genomes of organisms are sequenced, making sequence analysis a computationally demanding task. For example, a search of the E. coli. k12 proteome against the GenBank Non-Redundant database takes 36 hours on a standard workstation. In this thesis, we look to address the problem by accelerating protein searching using Field Programmable Gate Arrays. We focus our attention on the BLASTP heuristic, building on work done earlier to accelerate DNA searching on the Mercury platform. We analyze the performance characteristics of the BLASTP algorithm and explore the design space of the seed generation stage in detail. We propose a hardware/software architecture and evaluate the performance of the individual stage, and its effect on the overall BLASTP pipeline running on the Mercury system. The seed generation stage is 13x faster than the software equivalent, and the integrated BLASTP pipeline is predicted to yield a speedup of 50x over NCBI BLASTP. Mercury BLASTP also shows a 2.5x speed improvement over the only other BLASTP-like accelerator for FPGAs while consuming far fewer logic resources

FPGA acceleration of sequence analysis tools in bioinformatics

Thesis (Ph.D.)--Boston UniversityWith advances in biotechnology and computing power, biological data are being produced at an exceptional rate. The purpose of this study is to analyze the application of FPGAs to accelerate high impact production biosequence analysis tools. Compared with other alternatives, FPGAs offer huge compute power, lower power consumption, and reasonable flexibility. BLAST has become the de facto standard in bioinformatic approximate string matching and so its acceleration is of fundamental importance. It is a complex highly-optimized system, consisting of tens of thousands of lines of code and a large number of heuristics. Our idea is to emulate the main phases of its algorithm on FPGA. Utilizing our FPGA engine, we quickly reduce the size of the database to a small fraction, and then use the original code to process the query. Using a standard FPGA-based system, we achieved 12x speedup over a highly optimized multithread reference code. Multiple Sequence Alignment (MSA)--the extension of pairwise Sequence Alignment to multiple Sequences--is critical to solve many biological problems. Previous attempts to accelerate Clustal-W, the most commonly used MSA code, have directly mapped a portion of the code to the FPGA. We use a new approach: we apply prefiltering of the kind commonly used in BLAST to perform the initial all-pairs alignments. This results in a speedup of from 8Ox to 190x over the CPU code (8 cores). The quality is comparable to the original according to a commonly used benchmark suite evaluated with respect to multiple distance metrics. The challenge in FPGA-based acceleration is finding a suitable application mapping. Unfortunately many software heuristics do not fall into this category and so other methods must be applied. One is restructuring: an entirely new algorithm is applied. Another is to analyze application utilization and develop accuracy/performance tradeoffs. Using our prefiltering approach and novel FPGA programming models we have achieved significant speedup over reference programs. We have applied approximation, seeding, and filtering to this end. The bulk of this study is to introduce the pros and cons of these acceleration models for biosequence analysis tools

Reconfigurable acceleration of genetic sequence alignment: A survey of two decades of efforts

Genetic sequence alignment has always been a computational challenge in bioinformatics. Depending on the problem size, software-based aligners can take multiple CPU-days to process the sequence data, creating a bottleneck point in bioinformatic analysis flow. Reconfigurable accelerator can achieve high performance for such computation by providing massive parallelism, but at the expense of programming flexibility and thus has not been commensurately used by practitioners. Therefore, this paper aims to provide a thorough survey of the proposed accelerators by giving a qualitative categorization based on their algorithms and speedup. A comprehensive comparison between work is also presented so as to guide selection for biologist, and to provide insight on future research direction for FPGA scientists

Acceleration of Gapped Alignment in BLASTP Using the Mercury System

Protein databases have grown exponentially over the last decade. This exponential growth has made extracting valuable information from these databases increasingly time consuming. This project presents a new method of accelerating a commonly used program for performing similarity searching on protein databases, BLASTP. This project describes the design and implementation of Mercury BLASTP, a customized hardware accelerated variant of BLASTP. This project focuses on the gapped alignment stage of Mercury BLASTP and provides design details and implementation results

配列類似性検索のFPGAを用いた並列演算

２種の生物学的配列から類似箇所を算出する配列類似性検索は，バイオインフォマティクスの分野で広く利用されている．またBLAST（Basic Local Alignment Search Tool）は，既知な配列データベースから，クエリ配列と類似した配列を検索し，類似度を示す高速なシーケンスアラインメントアルゴリズムとして利用されている.この配列データベースは，学者により新しい配列が日々発見され，増加している．日々増加する配列データベースのようなビッグデータ解析において，データの増加速度の方がプロセッサの速度向上よりも速いため，計算性能の向上が求められる．この向上方法として，専用ハードウェアによるBLASTの処理の一部または全体を，FPGAにオフロードする研究は多く行われてきた．FPGAによるデータの入力速度で計算する手法に取り組むことは解析時間の短縮に多く貢献する．別の向上方法として，複数の計算機によるBLASTの並列化であるmpiBLASTといった取り組みがある．計算処理を高速化する点と計算処理の並列化する点，ふたつの観点からのBLASTの高速化取り組み方法があるが，どちらの研究も個別の観点からの実装である．そこで両方の観点からBLASTの専用ハードウェアによる並列演算の取り組みが必要となる．本研究では，先行研究で行われた並列演算するためのデータパーティショニングのハードウェア実装とBLASTにおける処理負荷が高い部分のハードウェア実装を結合し，mpiBLASTで行われるCPU処理の一部を専用ハードウェアにオフロードする．またBLASTの一部処理をデータI/Oの速度で処理を行える専用ハードウェアを開発し，BLASTの並列演算の高速化を図る．実装したハードウェアに対し，mpiBLASTと実行時間を比較し，データパーティショニングでは1.1倍，BLASTでは6.5~840倍の高速化を確認した．また並列演算全体の実行時間を見積もり，1.16~2.64倍の高速化を確認した．電気通信大学201

Design and Evaluation of a BLAST Ungapped Extension Accelerator, Master\u27s Thesis

The amount of biosequence data being produced each year is growing exponentially. Extracting useful information from this massive amount of data is becoming an increasingly difficult task. This thesis focuses on accelerating the most widely-used software tool for analyzing genomic data, BLAST. This thesis presents Mercury BLAST, a novel method for accelerating searches through massive DNA databases. Mercury BLAST takes a streaming approach to the BLAST computation by offloading the performance-critical sections onto reconfigurable hardware. This hardware is then used in combination with the processor of the host system to deliver BLAST results in a fraction of the time of the general-purpose processor alone. Mercury BLAST makes use of new algorithms combined with reconfigurable hardware to accelerate BLAST-like similarity search. An evaluation of this method for use in real BLAST-like searches is presented along with a characterization of the quality of results associated with using these new algorithms in specialized hardware. The primary focus of this thesis is the design of the ungapped extension stage of Mercury BLAST. The architecture of the ungapped extension stage is described along with the context of this stage within the Mercury BLAST system. The design is compact and performs over 20× faster than that of the standard software ungapped extension, yielding close to 50× speedup over the complete software BLAST application. The quality of Mercury BLAST results is essentially equivalent to the standard BLAST results

FPGA acceleration of DNA sequence alignment: design analysis and optimization

Existing FPGA accelerators for short read mapping often fail to utilize the complete biological information in sequencing data for simple hardware design, leading to missed or incorrect alignment. In this work, we propose a runtime reconfigurable alignment pipeline that considers all information in sequencing data for the biologically accurate acceleration of short read mapping. We focus our efforts on accelerating two string matching techniques: FM-index and the Smith-Waterman algorithm with the affine-gap model which are commonly used in short read mapping. We further optimize the FPGA hardware using a design analyzer and merger to improve alignment performance. The contributions of this work are as follows. 1. We accelerate the exact-match and mismatch alignment by leveraging the FM-index technique. We optimize memory access by compressing the data structure and interleaving the access with multiple short reads. The FM-index hardware also considers complete information in the read data to maximize accuracy. 2. We propose a seed-and-extend model to accelerate alignment with indels. The FM-index hardware is extended to support the seeding stage while a Smith-Waterman implementation with the affine-gap model is developed on FPGA for the extension stage. This model can improve the efficiency of indel alignment with comparable accuracy versus state-of-the-art software. 3. We present an approach for merging multiple FPGA designs into a single hardware design, so that multiple place-and-route tasks can be replaced by a single task to speed up functional evaluation of designs. We first experiment with this approach to demonstrate its feasibility for different designs. Then we apply this approach to optimize one of the proposed FPGA aligners for better alignment performance.Open Acces

PLAST: parallel local alignment search tool for database comparison

Background: Sequence similarity searching is an important and challenging task in molecular biology and next-generation sequencing should further strengthen the need for faster algorithms to process such vast amounts of data. At the same time, the internal architecture of current microprocessors is tending towards more parallelism, leading to the use of chips with two, four and more cores integrated on the same die. The main purpose of this work was to design an effective algorithm to fit with the parallel capabilities of modern microprocessors. Results: A parallel algorithm for comparing large genomic banks and targeting middle-range computers has been developed and implemented in PLAST software. The algorithm exploits two key parallel features of existing and future microprocessors: the SIMD programming model (SSE instruction set) and the multithreading concept (multicore). Compared to multithreaded BLAST software, tests performed on an 8-processor server have shown speedup ranging from 3 to 6 with a similar level of accuracy. Conclusions: A parallel algorithmic approach driven by the knowledge of the internal microprocessor architecture allows significant speedup to be obtained while preserving standard sensitivity for similarity search problems.

ROACH accelerated BLAST

Includes abstract.Includes bibliographical references (p. 115-118).Reconfigurable computing, in recent years, has been taking great strides in becoming part of mainstream computing largely due to the rapid growth in the size of FPGAs and their ability to adapt to certain complex applications efficiently. This dissertation investigates the reuse of application specific hardware developed for radio astronomy in accelerating a popular bioinformatics algorithm