Protein-protein interaction (PPI) is an important field in bioinformatics which helps in understanding diseases and devising therapy. PPI aims at estimating the similarity of protein sequences and their common regions. STRIKE was introduced as a PPI algorithm which was able to achieve reasonable improvement over existing PPI prediction methods. Although it consumes a lower execution time than most of other state-of the-art PPI prediction methods, its compute-intensive nature and the large volume of protein sequences in protein databases necessitate further time acceleration. In this paper, we develop hardware accelerator designs for the STRIKE algorithm. Results indicate that the weighted STRIKE accelerator execution times are about 10x longer than the unweighted STRIKE accelerator execution times. To further accelerate the performance of the weighted STRIKE, a parallel module accelerator organization duplicating the weighted STRIKE modules is introduced, achieving near linear speedups for long sequences of 100 or more characters. As demonstrated by Verilog simulations and FPGA runs, the weighted STRIKE module accelerator exhibits three orders of magnitude speed improvement over multi-core and cluster computers. Much higher speedups are possible with the parallel module accelerator.

中文翻译：

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STRIKE String Kernel算法的硬件加速，用于估计蛋白质与蛋白质之间的相互作用。

蛋白质间相互作用（PPI）是生物信息学中的一个重要领域，有助于理解疾病和制定治疗方法。PPI旨在估算蛋白质序列及其共同区域的相似性。引入STRIKE作为PPI算法，该算法能够对现有的PPI预测方法进行合理的改进。尽管与大多数其他最新的PPI预测方法相比，它消耗的执行时间更少，但其计算密集型的特性以及蛋白质数据库中大量蛋白质序列需要进一步加速时间。在本文中，我们为STRIKE算法开发了硬件加速器设计。结果表明，加权的STRIKE加速器执行时间比未加权的STRIKE加速器执行时间长约10倍。为了进一步加速加权STRIKE的性能，引入了一个并行模块加速器组织，该组织复制了加权STRIKE模块，对于100个或更多字符的长序列实现了近乎线性的加速。正如Verilog仿真和FPGA运行所证明的那样，加权STRIKE模块加速器与多核和集群计算机相比，速度提高了三个数量级。并行模块加速器可以实现更高的加速比。加权STRIKE模块加速器与多核和群集计算机相比，速度提高了三个数量级。并行模块加速器可以实现更高的加速比。加权STRIKE模块加速器与多核和群集计算机相比，速度提高了三个数量级。并行模块加速器可以实现更高的加速比。