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Multilevel Summation Of Electrostatic Potentials Using Graphics Processing Units

机译:使用图形处理单元的多级静电势求和

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Physical and engineering practicalities involved in microprocessor design have resulted in flat performance growth for traditional single-core microprocessors. The urgent need for continuing increases in the performance of scientific applications requires the use of many-core processors and accelerators such as graphics processing units (CPUs). This paper discusses GPU acceleration of the multilevel summation method for computing electrostatic potentials and forces for a system of charged atoms, which is a problem of paramount importance in biomolecular modeling applications. We present and test a new GPU algorithm for the long-range part of the potentials that computes a cutoff pair potential between lattice points, essentially convolving a fixed 3D lattice of "weights" over all sub-cubes of a much larger lattice. The implementation exploits the different memory subsystems provided on the GPU to stream optimally sized data sets through the multiprocessors. We demonstrate for the full multilevel summation calculation speedups of up to 26 using a single GPU and 46 using multiple CPUs, enabling the computation of a high-resolution map of the electrostatic potential for a system of 1.5 million atoms in under 12 s.
机译:微处理器设计中涉及的物理和工程实践导致传统单核微处理器的性能平稳增长。迫切需要不断提高科学应用程序的性能,这需要使用多核处理器和加速器,例如图形处理单元(CPU)。本文讨论了用于计算带电原子系统的静电势和力的多级求和方法的GPU加速,这在生物分子建模应用中是至关重要的问题。我们针对电势的远距离部分提出并测试了一种新的GPU算法,该算法计算晶格点之间的截止对电势,本质上是将“权重”的固定3D晶格卷积在更大的晶格的所有子立方体上。该实现利用GPU上提供的不同内存子系统,以通过多处理器流式传输最佳大小的数据集。我们演示了使用单个GPU最多可进行26次完整多级求和计算的速度,使用多个CPU最多可实现46次的计算速度,可在12 s内计算出150万个原子的系统的静电势的高分辨率图。

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