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

iWAS – A novel approach to analyzing Next Generation Sequence data for immunology

In this communication we describe a novel way to use Next Generation Sequence from the receptors expressed on T and B cells. This informatics methodology is named iWAS, for immunonome Wide Association Study, where we use the immune receptor sequences derived from T and B cells and the features of those receptors (sequences themselves, V/J gene usage, length and character each of the CDR3 sub-regions) to define biomarkers of health and disease, as well as responses to therapies. Unlike GWAS, which do not provide immediate access to mechanism, the associations with immune receptors immediately suggest possible and plausible entrée's into disease pathogenesis and treatment

Timing-Driven Nonuniform Depopulation-Based Clustering

Low-cost FPGAs have comparable number of Configurable Logic Blocks (CLBs) with respect to resource-rich FPGAs but have much less routing tracks. For CAD tools, this situation increases the difficulty of successfully mapping a circuit into the low-cost FPGAs. Instead of switching to resource-rich FPGAs, the designers could employ depopulation-based clustering techniques which underuse CLBs, hence improve routability by spreading the logic over the architecture. However, all depopulation-based clustering algorithms to this date increase critical path delay. In this paper, we present a timing-driven nonuniform depopulation-based clustering technique, T-NDPack, that targets critical path delay and channel width constraints simultaneously. T-NDPack adjusts the CLB capacity based on the criticality of the Basic Logic Element (BLE). Results show that T-NDPack reduces minimum channel width by 11.07% while increasing the number of CLBs by 13.28% compared to T-VPack. More importantly, T-NDPack decreases critical path delay by 2.89%