The Parallelism Motifs of Genomic Data Analysis

Genomic data sets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share this data with the research community, but some of these genomic data analysis problems require large scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high end parallel systems today and place different requirements on programming support, software libraries, and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high performance genomics analysis, including alignment, profiling, clustering, and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or motifs that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing

DOPA: GPU-based protein alignment using database and memory access optimizations

Background Smith-Waterman (S-W) algorithm is an optimal sequence alignment method for biological databases, but its computational complexity makes it too slow for practical purposes. Heuristics based approximate methods like FASTA and BLAST provide faster solutions but at the cost of reduced accuracy. Also, the expanding volume and varying lengths of sequences necessitate performance efficient restructuring of these databases. Thus to come up with an accurate and fast solution, it is highly desired to speed up the S-W algorithm. Findings This paper presents a high performance protein sequence alignment implementation for Graphics Processing Units (GPUs). The new implementation improves performance by optimizing the database organization and reducing the number of memory accesses to eliminate bandwidth bottlenecks. The implementation is called Database Optimized Protein Alignment (DOPA) and it achieves a performance of 21.4 Giga Cell Updates Per Second (GCUPS), which is 1.13 times better than the fastest GPU implementation to date. Conclusions In the new GPU-based implementation for protein sequence alignment (DOPA), the database is organized in equal length sequence sets. This equally distributes the workload among all the threads on the GPU's multiprocessors. The result is an improved performance which is better than the fastest available GPU implementation.MicroelectronicsElectrical Engineering, Mathematics and Computer Scienc

Holographic opto-fluidic microscopy.

Over the last decade microfluidics has created a versatile platform that has significantly advanced the ways in which micro-scale organisms and objects are controlled, processed and investigated, by improving the cost, compactness and throughput aspects of analysis. Microfluidics has also expanded into optics to create reconfigurable and flexible optical devices such as reconfigurable lenses, lasers, waveguides, switches, and on-chip microscopes. Here we present a new opto-fluidic microscopy modality, i.e., Holographic Opto-fluidic Microscopy (HOM), based on lensless holographic imaging. This imaging modality complements the miniaturization provided by microfluidics and would allow the integration of microscopy into existing on-chip microfluidic devices with various functionalities. Our imaging modality utilizes partially coherent in-line holography and pixel super-resolution to create high-resolution amplitude and phase images of the objects flowing within micro-fluidic channels, which we demonstrate by imaging C. elegans, Giardia lamblia, and Mulberry pollen. HOM does not involve complicated fabrication processes or precise alignment, nor does it require a highly uniform flow of objects within microfluidic channels

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