Split and Merge Functions for Supporting Multiple Processing Pipelines in Mercury BLASTN

Biosequence similarity search is an important application in computational biology. Mercury BLASTN, an FPGA-based implementation of BLAST for DNA, is one of the alternatives for fast DNA sequence comparison. The re-design of BLAST into a streaming application combined with a high-throughput hardware pipeline have enabled Mercury BLAST to emerge as one of the fastest implementations of bio-sequence similarity search. This performance can be further enhanced by exploiting the data-level parallelism present within the application. Here we present a multiple FPGA-based Mercury BLASTN design in order to double the speed and throughput of DNA sequence computation. This paper describes a dual Mercury BLASTN design, the detailed design of the split and merge functions, and simulation results

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

Mercury BLASTN Biosequence Similarity Search System: Technical Reference Guide

This guide documents the operation of the Mercury BLASTN system for hardware-accelerated DNA similarity search. It includes detailed information on the syntax and limitations of the system\u27s component commands, as well as a description of the system\u27s hardware platform suitable for administrators who need to maintain a Mercury BLASTN system. Mercury BLASTN is a product of the High Performance COmputational Biology Group at Washington University

Mercury BLAST dictionaries: analysis and performance measurement

This report describes a hashing scheme for a dictionary of short bit strings. The scheme, which we call near-perfect hashing, was designed as part of the construction of Mercury BLAST, an FPGA-based accelerator for the BLAST family of biosequence comparison algorithms. Near-perfect hashing is a heuristic variant of the well-known displacement hashing approach to building perfect hash functions. It uses a family of hash functions composed from linear transformations on bit vectors and lookups in small precomputed tables, both of which are especially appropriate for implementation in ardware logic. We show empirically that for inputs derived from genomic DNA sequences, our scheme obtains a good tradeoff between the size of the hash table and the time required to ompute it from a set of input strings, while generating few or no collisions between keys in the table. One of the building blocks of our scheme is the H_3 family of hash functions, which are linear transformations on bit vectors. We show that the uniformity of hashing performed with randomly chosen linear transformations depends critically on their rank, and that randomly chosen transformations have a high probability of having the maximum possible uniformity. A simple test is sufficient to ensure that a randomly chosen H3 hash function will not cause an unexpectedly large number of collisions. Moreover, if two such functions are chosen independently at random, the second function is unlikely to hash together two keys that were hashed together by the first. Hashing schemes based on H3 hash functions therefore tend to distribute their inputs more uniformly than would be expected under a simple uniform hashing model, and schemes using pairs of these functions are more uniform than would be assumed for a pair of independent hash functions

Protein alignment HW/SW optimizations

Biosequence alignment recently received an amazing support from both commodity and dedicated hardware platforms. The limitless requirements of this application motivate the search for improved implementations to boost processing time and capabilities. We propose an unprecedented hardware improvement to the classic Smith-Waterman (S-W) algorithm based on a twofold approach: i) an on-the-fly gap-open/gap-extension selection that reduces the hardware implementation complexity; ii) a pre-selection filter that uses reduced amino-acid alphabets to screen out not-significant sequences and to shorten the S-Witerations on huge reference databases.We demonstrated the improvements w.r.t. a classic approach both from the point of view of algorithm efficiency and of HW performance (FPGA and ASIC post-synthesis analysis)

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

Performance Evaluation for Hybrid Architectures

In this dissertation we discuss methologies for estimating the performance of applications on hybrid architectures, systems that include various types of computing resources (e.g. traditional general-purpose processors, chip multiprocessors, reconfigurable hardware). A common use of hybrid architectures will be to deploy coarse pipeline stages of application on suitable compute units with communication path for transferring data. The first problem we focus on relates to the sizing the data queues between the different processing elements of an hybrid system. Much of the discussion centers on our analytical models that can be used to derive performance metrics of interest such as, throughput and stalling probability for networks of processing elements with finite data buffering between them. We then discuss to the reliability of performance models. There we start by presenting scenarios where our analytical model is reliable, and introduce tests that can detect their inapplicability. As we transition into the question of reliability of performance models, we access the accuracy and applicability of various evaluation methods. We present results from our experiments to show the need for measuring and accounting for operating system effects in architectural modeling and estimation

Acceleration of Profile-HMM Search for Protein Sequences in Reconfigurable Hardware - Master\u27s Thesis, May 2006

Profile Hidden Markov models are highly expressive representations of functional units, or motifs, conserved across protein sequences. Profile-HMM search is a powerful computational technique that is used to annotate new sequences by identifying occurrences of known motifs in them. With the exponential growth of protein databases, there is an increasing demand for acceleration of such techniques. We describe an accelerator for the Viterbi algorithm using a two-stage pipelined design in which the first stage is implemented in parallel reconfigurable hardware for greater speedup. To this end, we identify algorithmic modifications that expose a high level of parallelism and characterize their impact on the accuracy and performance relative to a standard software implementation. We develop a performance model to evaluate any accelerator design and propose two alternative architectures that recover the accuracy lost by a basic architecture. We compare the performance of the two architectures to show that speedups of up to 3 orders of magnitude may be achieved. We also investigate the use of the Forward algorithm in the first pipeline stage of the accelerator using floating-point arithmetic and report its accuracy and performance