Optimization Techniques for Explicit Methods for GPU-Accelerated Solution of Initial Value Problems of Ordinary Differential Equations (OTEGO)

GPU 加速求解常微分方程初值问题 (OTEGO) 显式方法的优化技术

• 批准号: 277319075
• 负责人: Privatdozent Dr. Matthias Korch
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/277319075?language=en
• 关键词: Optimization; Techniques; Explicit; Methods; GPU

Graphics Processing Units (GPUs) are used increasingly to accelerate compute intensive applications, e.g., in the domain of scientific computing, by exploiting massive parallelism. The project proposed investigates parallel implementations of explicit solution methods for initial value problems (IVPs) of systems of ordinary differential equations (ODEs) on GPUs. In the first phase of the project, a systematic general approach for the optimization and self-adaptation of ODE methods was developed, which is based on a representation of the methods by data flow graphs and which is applicable to arbitrary explicit ODE methods. The goal of the self-adaptation is to reach the best possible runtime for the given IVP to be solved on the given GPU hardware and, thus, to reach portability of performance. Single GPUs as well as homogeneous multi-GPU clusters were considered as target platforms. Building on the results of the first phase, the second phase investigates new optimization techniques and improves the self-adaptation capabilities of the solvers. This includes the automatic generation of specialized implementation variants which are adapted to the method coefficients and the access distance of the ODE system. In particular for ODE systems with limited access distance, temporal and spatial tiling strategies which extend over the stages of a time step and over several time steps are investigated. The range of target platforms is extended to heterogeneous multi-GPU clusters, including the available CPU cores.

图形处理单元（gpu）越来越多地用于加速计算密集型应用程序，例如，在科学计算领域，通过利用大规模并行性。该项目提出了在gpu上研究常微分方程（ode）系统的初值问题（IVPs）的显式求解方法的并行实现。在项目的第一阶段，开发了一种系统的ODE方法优化和自适应的通用方法，该方法基于数据流图的方法表示，适用于任意显式ODE方法。自适应的目标是在给定的GPU硬件上实现给定IVP的最佳运行时，从而实现性能的可移植性。考虑单gpu和同构多gpu集群作为目标平台。在第一阶段成果的基础上，第二阶段研究新的优化技术，提高求解器的自适应能力。这包括适应ODE系统的方法系数和访问距离的专门实现变体的自动生成。特别是对于具有有限访问距离的ODE系统，研究了在一个时间步长和几个时间步长的时间和空间平铺策略。目标平台的范围扩展到异构多gpu集群，包括可用的CPU内核。