Deukhyun Cha, Alexander Rand, Qin Zhang, Rezaul A Chowdhury, Jesmin Jahan Tithi, Chandrajit Bajaj
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The key idea is to trade off accuracy of pairwise, long-range atomistic energetics for higher speed of execution. A simple yet efficient CUDA kernel for GPU acceleration is presented which ensures high arithmetic intensity and memory efficiency. Our CUDA kernel uses a cache-friendly, recursive and linear-space octree data structure to handle very large molecular structures with up to several million atoms. Based on this CUDA kernel, we present a hybrid method which simultaneously exploits both CPU and GPU cores to provide the best performance based on selected parameters of the approximation scheme. Our CUDA kernels achieve more than two orders of magnitude speedup over serial computation for many of the molecular energetics terms. 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引用次数: 0
摘要
动机:尽管有报道称可编程图形处理器(GPU)在分子建模和模拟工具的加速方面取得了一些成功,但人们普遍关注的是小分子的快速计算。这主要是由于 GPU 的内存容量有限。此外,同时使用 CPU 和 GPU 内核执行单个内核--这是实现高并行性的必要条件--也未得到充分考虑:我们提出了分子力学(伦纳德-琼斯和库仑)和广义玻恩溶解能的快速计算方法,可在商用多核 CPU 和多核 GPU 上运行。其关键思路是以更高的执行速度换取成对长程原子能量学的准确性。本文介绍了一种用于 GPU 加速的简单而高效的 CUDA 内核,它能确保较高的算术强度和内存效率。我们的 CUDA 内核使用缓存友好、递归和线性空间八叉树数据结构来处理多达数百万原子的超大型分子结构。在此 CUDA 内核的基础上,我们提出了一种混合方法,可同时利用 CPU 和 GPU 内核,根据所选的近似方案参数提供最佳性能。与串行计算相比,我们的 CUDA 内核使许多分子能量项的计算速度提高了两个数量级以上。混合方法在所有近似参数值下都能达到最佳性能:源代码和二进制文件作为 PMEOPA(Parallel Molecular Energetic using Octree Pairwise Approximation)免费提供,可从 http://cvcweb.ices.utexas.edu/software 下载。
Accelerated Molecular Mechanical and Solvation Energetics on Multicore CPUs and Manycore GPUs.
Motivation: Despite several reported acceleration successes of programmable GPUs (Graphics Processing Units) for molecular modeling and simulation tools, the general focus has been on fast computation with small molecules. This was primarily due to the limited memory size on the GPU. Moreover simultaneous use of CPU and GPU cores for a single kernel execution - a necessity for achieving high parallelism - has also not been fully considered.
Results: We present fast computation methods for molecular mechanical (Lennard-Jones and Coulombic) and generalized Born solvation energetics which run on commodity multicore CPUs and manycore GPUs. The key idea is to trade off accuracy of pairwise, long-range atomistic energetics for higher speed of execution. A simple yet efficient CUDA kernel for GPU acceleration is presented which ensures high arithmetic intensity and memory efficiency. Our CUDA kernel uses a cache-friendly, recursive and linear-space octree data structure to handle very large molecular structures with up to several million atoms. Based on this CUDA kernel, we present a hybrid method which simultaneously exploits both CPU and GPU cores to provide the best performance based on selected parameters of the approximation scheme. Our CUDA kernels achieve more than two orders of magnitude speedup over serial computation for many of the molecular energetics terms. The hybrid method is shown to be able to achieve the best performance for all values of the approximation parameter.
Availability: The source code and binaries are freely available as PMEOPA (Parallel Molecular Energetic using Octree Pairwise Approximation) and downloadable from http://cvcweb.ices.utexas.edu/software.