An Analysis of BLASTP Implementation on NVIDIA GPUs
نویسنده
چکیده
1.0 Introduction Given a new protein sequence, scanning existing protein databases for similar sequences is becoming an important and repeated task in bioinformics. Initially, the Smith-Waterman [1] algorithm was developed to find local, optimal matches between an input sequence and a given database of existing protein sequences. The Smith-Waterman algorithm uses a recursive algorithm (dynamic programming) to perform the search. Unfortunately, the algorithm is too compute intensive and has an execution time that is quadratic with respect to the length of protein sequences [2].
منابع مشابه
An approach to Improve Particle Swarm Optimization Algorithm Using CUDA
The time consumption in solving computationally heavy problems has always been a concern for computer programmers. Due to simplicity of its implementation, the PSO (Particle Swarm Optimization) is a suitable meta-heuristic algorithm for solving computationally heavy problems. However, despite the simplicity, the algorithm is inefficient for solving real computationally heavy problems but the pr...
متن کاملA GEMM interface and implementation on NVIDIA GPUs for multiple small matrices
We present an interface and an implementation of the General Matrix Multiply (GEMM) routine for multiple small matrices processed simultaneously on NVIDIA graphics processing units (GPUs). We focus on matrix sizes under 16. The implementation can be easily extended to larger sizes. For single precision matrices, our implementation is 30% to 600% faster than the batched cuBLAS implementation dis...
متن کاملBatched Kronecker product for 2-D matrices and 3-D arrays on NVIDIA GPUs
We describe an interface and an implementation for performing Kronecker product actions on NVIDIA GPUs for multiple small 2-D matrices and 3-D arrays processed in parallel as a batch. This method is suited to cases where the Kronecker product component matrices are identical but the operands in a matrix-free application vary in the batch. Any batched GEMM (General Matrix Multiply) implementatio...
متن کاملImplementing Blocked Sparse Matrix-Vector Multiplication on NVIDIA GPUs
We discuss implementing blocked sparse matrix-vector multiplication for NVIDIA GPUs. We outline an algorithm and various optimizations, and identify potential future improvements and challenging tasks. In comparison with previously published implementation, our implementation is faster on matrices having many high fill-ratio blocks but slower on matrices with low number of non-zero elements per...
متن کاملA new approach for sparse matrix vector product on NVIDIA GPUs
The sparse matrix vector product (SpMV) is a key operation in engineering and scientific computing and, hence, it has been subjected to intense research for a long time. The irregular computations involved in SpMV make its optimization challenging. Therefore, enormous effort has been devoted to devise data formats to store the sparse matrix with the ultimate aim of maximizing the performance. G...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2012