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The Smith-Waterman (SW) algorithm is one of the widely used algorithms for sequence alignment in computational biology. With the growing size of the sequence database, there is always a need for even faster implementation of SW. In this paper, we have implemented two Recursive Variable Expansion (RVE) based techniques, which are proved to give better(More)
In this paper we adapted a novel approach for accelerating the Smith-Waterman (S-W) algorithm using Recursive Variable Expansion (RVE), which exposes extra parallelism in the algorithm, as compared to any other technique. The results demonstrate that applying the recursive variable expansion technique speeds up the performance by a factor of 1.36 to 1.41,(More)
—The Smith-Waterman (SW) algorithm is the only optimal local sequence alignment algorithm. There are many SW implementations on FPGA, which show speedups of up to 100x as compared to a general-purpose-processor (GPP). In this paper, we propose a design of the SW traceback, which is done in parallel with the matrix fill stage and which gives the optimal(More)
Loops are an important source of performance improvement , for which there exists a large number of compiler based optimizations. Few optimizations assume that the loop will be fully mapped on hardware. In this paper, we discuss a loop transformation called Recursive Variable Expansion , which can be efficiently implemented in hardware. It removes all the(More)
Many image and signal processing kernels can be optimized for performance consuming a reasonable area by doing loops parallelization with extensive use of pipelining. This paper presents an automated flexible pipeline design algorithm for our unique acceleration technique called Recursive Variable Expansion. The preliminary experimental results on a kernel(More)
The Smith-Waterman (SW) algorithm is a local sequence alignment algorithm that attempts to align two biological sequences of varying length such that the alignment score is maximum. In this paper, we propose a new approach to reduce the time needed to perform the SW algorithm. This is done by applying the concept of recursive variable expansion, which(More)
—Optimization problems are known to be very hard problems requiring a lot of CPU time. Dynamic Programming (DP) is a powerful method, which is typically used to compute large number of discrete optimization problems. This paper presents an improved approach called RVEP (RVE with pre-computation) that allows to design highly parallel hardware accelerators(More)
pyFAI is an open-source software package designed to perform azimuthal integration and, correspondingly, two-dimensional regrouping on area-detector frames for small-and wide-angle X-ray scattering experiments. It is written in Python (with binary submodules for improved performance), a language widely accepted and used by the scientific community today,(More)
In Pakistan, millions of people have access to internet and now it has become essential part of their lives. It is now a driving force for innovation of all industries. After arrival of wireless service providers, internet users are growing rapidly. On the other hand, internet is still just a source of information sharing and social interaction. Its role in(More)
In this paper we implement in hardware, a novel approach for accelerating the S-W algorithm using Recursive Variable Expansion (RVE) technique, which enhances inherent parallelism and exposes extra parallelism as compared to any other technique. The results demonstrate that applying recursive variable expansion technique speeds up the performance by a(More)