National Laboratory for Parallel&Distributed Processing, Department of Computer Science, National University of Defense Technology, ChangSha, 410073, China.
BMC Bioinformatics. 2011 Feb 15;12 Suppl 1(Suppl 1):S5. doi: 10.1186/1471-2105-12-S1-S5.
Protein is an important molecule that performs a wide range of functions in biological systems. Recently, the protein folding attracts much more attention since the function of protein can be generally derived from its molecular structure. The GOR algorithm is one of the most successful computational methods and has been widely used as an efficient analysis tool to predict secondary structure from protein sequence. However, the execution time is still intolerable with the steep growth in protein database. Recently, FPGA chips have emerged as one promising application accelerator to accelerate bioinformatics algorithms by exploiting fine-grained custom design.
In this paper, we propose a complete fine-grained parallel hardware implementation on FPGA to accelerate the GOR-IV package for 2D protein structure prediction. To improve computing efficiency, we partition the parameter table into small segments and access them in parallel. We aggressively exploit data reuse schemes to minimize the need for loading data from external memory. The whole computation structure is carefully pipelined to overlap the sequence loading, computing and back-writing operations as much as possible. We implemented a complete GOR desktop system based on an FPGA chip XC5VLX330.
The experimental results show a speedup factor of more than 430x over the original GOR-IV version and 110x speedup over the optimized version with multi-thread SIMD implementation running on a PC platform with AMD Phenom 9650 Quad CPU for 2D protein structure prediction. However, the power consumption is only about 30% of that of current general-propose CPUs.
蛋白质是一种重要的分子，在生物系统中执行广泛的功能。最近，蛋白质折叠引起了更多的关注，因为蛋白质的功能通常可以从其分子结构中推导出来。GOR 算法是最成功的计算方法之一，已被广泛用作从蛋白质序列预测二级结构的有效分析工具。然而，执行时间仍然难以忍受蛋白质数据库的急剧增长。最近，FPGA 芯片作为一种有前途的应用加速器出现，通过利用细粒度的定制设计来加速生物信息学算法。
在本文中，我们提出了一种完整的细粒度并行硬件实现，用于在 FPGA 上加速 2D 蛋白质结构预测的 GOR-IV 包。为了提高计算效率，我们将参数表划分为小片段并并行访问它们。我们积极利用数据重用方案，以最大程度地减少从外部存储器加载数据的需求。整个计算结构被精心流水线化，以尽可能多地重叠序列加载、计算和回写操作。我们在 FPGA 芯片 XC5VLX330 上实现了一个完整的 GOR 桌面系统。
实验结果表明，与原始的 GOR-IV 版本相比，速度提高了 430 多倍，与在 AMD Phenom 9650 Quad CPU 上运行的具有多线程 SIMD 实现的 PC 平台上的优化版本相比，速度提高了 110 倍，用于 2D 蛋白质结构预测。然而，功耗仅为当前通用 CPU 的约 30%。
