Motivation: The Basic Local Alignment Search Tool (BLAST) is one of the most widely used bioinformatics tools. The widespread impact of BLAST is reflected in over 53 000 citations that this software has received in the past two decades, and the use of the word ‘blast’ as a verb referring to biological sequence comparison. Any improvement in the execution speed of BLAST would be of great importance in the practice of bioinformatics, and facilitate coping with ever increasing sizes of biomolecular databases.Results: Using a general-purpose graphics processing unit (GPU), we have developed GPU-BLAST, an accelerated version of the popular NCBI-BLAST. The implementation is based on the source code of NCBI-BLAST, thus maintaining the same input and output interface while producing identical results. In comparison to the sequential NCBI-BLAST, the speedups achieved by GPU-BLAST range mostly between 3 and 4.Availability: The source code of GPU-BLAST is freely available at http://archimedes.cheme.cmu.edu/biosoftware.html.Contact: sahinidis@cmu.eduSupplementary information: Supplementary data are available at Bioinformatics online.

This paper presents a hardware architecture for embedded real-time model predictive control (MPC). The computational cost of an MPC problem, which relies on the solution of an optimization problem at every time step, is dominated by operations on real matrices. In order to design an efficient and low-cost application-specific processor, we analyze the computational cost of MPC, and we propose a limited-resource host processor to be connected with an application-specific matrix coprocessor. The coprocessor uses a 16-b logarithmic number system arithmetic unit, which is designed using cotransformation, to carry out the required arithmetic operations. The proposed architecture is implemented by means of a hardware description language and then prototyped and emulated on a field-programmable gate array. Results on computation time and architecture area are presented and analyzed, and the functionality of the proposed architecture is verified using two case studies: a linear problem of a rotating antenna and a nonlinear glucose-regulation problem. The proposed MPC architecture yields a small-in-size and energy-efficient implementation that is capable of solving the aforementioned problems on the order of milliseconds, and we compare its performance and area requirements with other MPC designs that have appeared in the literature.Index Terms-Embedded systems, logarithmic number system (LNS), Microdevice, model predictive control (MPC), optimal control, system-on-chip.

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The graphics processing unit (GPU) is used to solve large linear systems derived from partial differential equations. The differential equations studied are strongly convection-dominated, of various sizes, and common to many fields, including computational fluid dynamics, heat transfer, and structural mechanics. The paper presents comparisons between GPU and CPU implementations of several well-known iterative methods, including Kaczmarz's, Cimmino's, component averaging, conjugate gradient normal residual (CGNR), symmetric successive overrelaxation-preconditioned conjugate gradient, and conjugate-gradient-accelerated component-averaged row projections (CARP-CG). Computations are preformed with dense as well as general banded systems. The results demonstrate that our GPU implementation outperforms CPU implementations of these algorithms, as well as previously studied parallel implementations on Linux clusters and shared memory systems. While the CGNR method had begun to fall out of favor for solving such problems, for the problems studied in this paper, the CGNR method implemented on the GPU performed better than the other methods, including a cluster implementation of the CARP-CG method.