Misleading performance in the supercomputing field
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Hierarchical tiling for improved superscalar performance
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
Schedule-independent storage mapping for loops
Proceedings of the eighth international conference on Architectural support for programming languages and operating systems
ACM Transactions on Computer Systems (TOCS)
A Study of Architectural Optimization Methods in Bioinformatics Applications
International Journal of High Performance Computing Applications
The new SIMD Implementation of the Smith-Waterman Algorithm on Cell Microprocessor
Fundamenta Informaticae
Acceleration of the Smith-Waterman algorithm using single and multiple graphics processors
Journal of Computational Physics
Optimization schemes and performance evaluation of Smith–Waterman algorithm on CPU, GPU and FPGA
Concurrency and Computation: Practice & Experience
Fine-grained parallel implementations for SWAMP+ Smith-Waterman alignment
Parallel Computing
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The Smith-Waterman algorithm is a computationally-intensive string-matching operation that is fundamental to the analysis of proteins and genes. In this paper, we explore the use of some standard and novel techniques for improving its performance. We begin by tuning the algorithm using conventional techniques. These make modest performance improvements by providing efficient cache usage and inner-loop code. One novel technique uses the z-buffer operations of the Intel i860 architecture to perform 4 independent computations in parallel. This achieves a five-fold speedup over the optimized code (six-fold over the original). We also describe a related technique that could be used by processors that have 64-bit integer operations, but no z-buffer. Another new technique uses floating-point multiplies and adds in place of the standard algorithm's integer additions and maximum operations. This gains more than a three-fold speedup on the IBM POWER2 processor. This method doesn't give the identical answers as the original program, but experimental evidence shows that the inaccuracies are small and do not affect which strings are chosen as good matches by the algorithm.