GPU Optimizations for a Production Molecular Docking Code.

Modeling molecular docking is critical to both understanding life processes and designing new drugs. In previous work we created the first published GPU-accelerated docking code (PIPER) which achieved a roughly 5× speed-up over a contemporaneous 4 core CPU. Advances in GPU architecture and in the CPU code, however, have since reduced this relalative performance by a factor of 10. In this paper we describe the upgrade of GPU PIPER. This required an entire rewrite, including algorithm changes and moving most remaining non-accelerated CPU code onto the GPU. The result is a 7× improvement in GPU performance and a 3.3× speedup over the CPU-only code. We find that this difference in time is almost entirely due to the difference in run times of the 3D FFT library functions on CPU (MKL) and GPU (cuFFT), respectively. The GPU code has been integrated into the ClusPro docking server which has over 4000 active users.

分子对接建模对于理解生命过程和设计新药都至关重要。在之前的工作中，我们创建了首个已发表的GPU加速对接代码（PIPER），它比同期的4核CPU速度大约提高了5倍。然而，GPU架构和CPU代码的进步此后使这种相对性能降低了10倍。在本文中，我们描述了GPU - PIPER的升级。这需要完全重写，包括算法更改以及将大多数剩余的未加速的CPU代码转移到GPU上。结果是GPU性能提高了7倍，比仅使用CPU的代码速度提高了3.3倍。我们发现，这种时间上的差异几乎完全是由于3D FFT库函数分别在CPU（MKL）和GPU（cuFFT）上的运行时间不同所致。GPU代码已集成到拥有4000多名活跃用户的ClusPro对接服务器中。