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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用于估计蛋白质与蛋白质相互作用的 STRIKE 字符串内核算法的硬件加速

蛋白质-蛋白质相互作用 (PPI) 是生物信息学中的一个重要领域，有助于了解疾病和设计治疗方法。PPI 旨在估计蛋白质序列及其共同区域的相似性。STRIKE 作为一种 PPI 算法被引入，它能够对现有的 PPI 预测方法进行合理的改进。尽管与大多数其他最先进的 PPI 预测方法相比，它消耗的执行时间较短，但其计算密集型性质和蛋白质数据库中的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 STRIKE 加速器执行时间比未加权 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，引入了复制加权 STRIKE 模块的并行模块加速器组织，实现了 100 个或更多字符的长序列的接近线性加速。正如 Verilog 仿真和 FPGA 运行所证明的那样，加权 STRIKE 模块加速器的速度比多核和集群计算机提高了三个数量级。使用并行模块加速器可以实现更高的加速。与多核和集群计算机相比，加权 STRIKE 模块加速器的速度提高了三个数量级。使用并行模块加速器可以实现更高的加速。与多核和集群计算机相比，加权 STRIKE 模块加速器的速度提高了三个数量级。使用并行模块加速器可以实现更高的加速。