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Benchmarking MD systems simulations on the graphics processing unit and multi-core systems

机译:在图形处理单元和多核系统上对MD系统仿真进行基准测试

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Molecular dynamics facilitates the simulation of a complex system to be analyzed at molecular and atomic levels. Simulations can last a long period of time, even months. Due to this cause the graphics processing units (GPUs) and multi-core systems are used as solutions to overcome this impediment. The current paper describes a comparison done between these two kinds of systems. The first system used implies the graphics processing unit, respectively CUDA with the OpenMM molecular dynamics package and OpenCL that allows the kernels to run on the GPU. This simulation is done on a new thermostat which mixes the Berendsen thermostat with the Langevin dynamics. The second comprises the molecular dynamics simulation and energy minimization package GROMACS which is based on a parallelization through MPI (Message Passing Interface) on multi-core systems. The second simulation uses another new thermostat algorithm related respectively, dissipative particle dynamics - isotropic type (DPD-ISO). Both thermostats are innovative, based on a new theory developed by us. Results show that parallelization on multi-core systems has a performance up to 33 times greater than the one performed on the graphics processing unit. In both cases temperature of the system was maintained close to the one taken as reference. For the simulation using the CUDA GPU, the faster runtime was obtained when the number of processors was equal to four, the simulation speed being 3.67 times faster compared to the case of only one processor.
机译:分子动力学有助于在分子和原子水平上对复杂系统进行仿真。模拟可以持续很长时间,甚至几个月。由于这个原因,图形处理单元(GPU)和多核系统被用作解决此问题的解决方案。本文描述了这两种系统之间的比较。使用的第一个系统是图形处理单元,分别是带有OpenMM分子动力学软件包的CUDA和允许内核在GPU上运行的OpenCL。该模拟是在一个新的恒温器上完成的,该恒温器将Berendsen恒温器和Langevin动力学混合在一起。第二个包括分子动力学仿真和能量最小化软件包GROMACS,该软件包基于在多核系统上通过MPI(消息传递接口)进行的并行化。第二个模拟使用另一个分别与耗散粒子动力学相关的新的恒温器算法-各向同性类型(DPD-ISO)。两种恒温器都是创新的,基于我们开发的新理论。结果表明,多核系统上的并行化性能比图形处理单元上的并行化性能高33倍。在这两种情况下,系统的温度都保持接近于参考温度。对于使用CUDA GPU进行的仿真,当处理器数量等于4时,运行时间更快,与只有一个处理器的情况相比,仿真速度快了3.67倍。

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