基于协同异构模型的成形模拟计算加速
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摘要
成形模拟中的多场耦合分析,非线性多尺度耦合分析等问题通常需要对大量数据进行多次计算以得到有效的结果,另外这些计算又必须在合理的时间内完成。本文主要从并行计算架构方面研究成形模拟中的计算加速方法,提出了基于CPU/GPU架构的协同异构计算模型来缩短成形模拟过程中的计算时间。
本文把工作重点放在了如何优化CPU/GPU协同异构计算模型,缩短计算时间问题的研究上,通过对成形模拟中求解多物理场耦合,多尺度耦合分析过程中的温度场问题,应力应变问题经常用到的有限差分法,矩阵与向量乘积等常用计算方法的GPU架构的程序实现,极大的提高了计算效率,将大量计算转移到计算效率较高的GPU架构上来,减轻CPU的计算压力,缩短计算时间。本文通过对六个数据存储优化方案的实验分析,不断优化CPU/GPU异构计算架构的设计方法,经过各种实验数据证明,得出了比较合适的数据存储方案,针对GPU架构在科学计算方面的一些缺陷,通过CPU来协同完成。通过将完全位串链表与位掩码结合在一起使用优化了存储器结构对计算性能的影响,测试发现,优化方案比没有优化过的方案有25倍的性能提升。在程序设计和收敛求和计算过程中充分利用共享内存空间,减少不必要的数据同步,减少导致缓存命中率下降的因素。通过使用分支同步的方法来处理条件分支,在发生条件分支时,转移方向相同线程的先执行完分支中的指令,然后另外一个转移方向的线程再执行另外一个分支中的指令优化了单指令多线程技术对计算性能的影响。
通过实验评估,CPU/GPU协同异构计算模型能解决比单个CPU或者GPU计算系统大16倍左右的问题,却能得到少于20%的错误率,系统负载率只有之前的60%,系统计算效率有50%以上的提升。通过实验结果和并行计算系统现在的发展趋势,相信CPU/GPU协同异构计算将成为并行计算发展历程中越来越重要的一环。本文的研究内容具有重要的工程意义和广阔的应用前景。
Multi-physics coupling, nonlinear multi-scale coupling and other factors make large amounts of data need to be calculated in forming simulation,while these jobs must also be completed within a reasonable time.This thesis mainly focus on the research of parallel computing architecture accelerate computing for forming simulation, proposed collaborative heterogeneous computing model to shorten forming simulation computing time based on CPU/GPU architecture.
In this thesis focus on how to optimize the CPU/GPU collaborative heterogeneous computing model and reduce the computing time. Some algorithm such as finite difference method, matrix and vector product are often used in calculating stress filed and temperature filed,which considered multi-physics coupling and multi-scale coupling. Those algorithms have been implemented under GPU architecture. Large calculation jobs are transferred to GPU architecture,which has a higher calculation efficiency, alleviate CPU calculation pressure and shorten computing time.Greatly improves calculation efficiency.Optimize CPU/GPU collaborative heterogeneous computing model design based on six experiments of data storage optimization analysis.A variety of experiment data show that had obtained better data storage solution.There are many shortcomings in GPU architecture for scientific computing, so must be coordinated by CPU to complete.Optimize data storage architecture on the computational performance of model through bit string array used together with bitmask.Test found no optimized program can be as high as 25 times compared to the optimized one.Make good use of shared memory,reduce unnecessary data synchronization,reduce cache hit rate of decline factors through program design and convergence calculation process. Synchronized by using the branch method to deal with conditional branches, conditional branches in the same direction, the transfer of the same thread of the first implementation of the direction of branch instructions completed, and then switch to the direction of another thread and then another branch of the implementation of optimized SIMT instructions threading the calculation of performance.
With CPU/GPU collaborative and heterogeneous computing prototype shows that the performance model can predict the execution time of problem sizes that are 16 times as large as the profile runs with less than 20% error, and that the predicted optimal load distribution ratios have less than 60% and the resulting performance improvement using both CPUs and GPUs can be as high as 50% compared to using either a CPU core or a GPU.Based on our results and current trends in microarchitecture,Efficient use of CPU and GPU collaborative and heterogeneous computing environment will become increasingly important to high performance computing. The study in this paper is of great significance and wide application prospect.
本文把工作重点放在了如何优化CPU/GPU协同异构计算模型,缩短计算时间问题的研究上,通过对成形模拟中求解多物理场耦合,多尺度耦合分析过程中的温度场问题,应力应变问题经常用到的有限差分法,矩阵与向量乘积等常用计算方法的GPU架构的程序实现,极大的提高了计算效率,将大量计算转移到计算效率较高的GPU架构上来,减轻CPU的计算压力,缩短计算时间。本文通过对六个数据存储优化方案的实验分析,不断优化CPU/GPU异构计算架构的设计方法,经过各种实验数据证明,得出了比较合适的数据存储方案,针对GPU架构在科学计算方面的一些缺陷,通过CPU来协同完成。通过将完全位串链表与位掩码结合在一起使用优化了存储器结构对计算性能的影响,测试发现,优化方案比没有优化过的方案有25倍的性能提升。在程序设计和收敛求和计算过程中充分利用共享内存空间,减少不必要的数据同步,减少导致缓存命中率下降的因素。通过使用分支同步的方法来处理条件分支,在发生条件分支时,转移方向相同线程的先执行完分支中的指令,然后另外一个转移方向的线程再执行另外一个分支中的指令优化了单指令多线程技术对计算性能的影响。
通过实验评估,CPU/GPU协同异构计算模型能解决比单个CPU或者GPU计算系统大16倍左右的问题,却能得到少于20%的错误率,系统负载率只有之前的60%,系统计算效率有50%以上的提升。通过实验结果和并行计算系统现在的发展趋势,相信CPU/GPU协同异构计算将成为并行计算发展历程中越来越重要的一环。本文的研究内容具有重要的工程意义和广阔的应用前景。
Multi-physics coupling, nonlinear multi-scale coupling and other factors make large amounts of data need to be calculated in forming simulation,while these jobs must also be completed within a reasonable time.This thesis mainly focus on the research of parallel computing architecture accelerate computing for forming simulation, proposed collaborative heterogeneous computing model to shorten forming simulation computing time based on CPU/GPU architecture.
In this thesis focus on how to optimize the CPU/GPU collaborative heterogeneous computing model and reduce the computing time. Some algorithm such as finite difference method, matrix and vector product are often used in calculating stress filed and temperature filed,which considered multi-physics coupling and multi-scale coupling. Those algorithms have been implemented under GPU architecture. Large calculation jobs are transferred to GPU architecture,which has a higher calculation efficiency, alleviate CPU calculation pressure and shorten computing time.Greatly improves calculation efficiency.Optimize CPU/GPU collaborative heterogeneous computing model design based on six experiments of data storage optimization analysis.A variety of experiment data show that had obtained better data storage solution.There are many shortcomings in GPU architecture for scientific computing, so must be coordinated by CPU to complete.Optimize data storage architecture on the computational performance of model through bit string array used together with bitmask.Test found no optimized program can be as high as 25 times compared to the optimized one.Make good use of shared memory,reduce unnecessary data synchronization,reduce cache hit rate of decline factors through program design and convergence calculation process. Synchronized by using the branch method to deal with conditional branches, conditional branches in the same direction, the transfer of the same thread of the first implementation of the direction of branch instructions completed, and then switch to the direction of another thread and then another branch of the implementation of optimized SIMT instructions threading the calculation of performance.
With CPU/GPU collaborative and heterogeneous computing prototype shows that the performance model can predict the execution time of problem sizes that are 16 times as large as the profile runs with less than 20% error, and that the predicted optimal load distribution ratios have less than 60% and the resulting performance improvement using both CPUs and GPUs can be as high as 50% compared to using either a CPU core or a GPU.Based on our results and current trends in microarchitecture,Efficient use of CPU and GPU collaborative and heterogeneous computing environment will become increasingly important to high performance computing. The study in this paper is of great significance and wide application prospect.
引文
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