Mixed precision techniques in numerical simulations

数值模拟中的混合精度技术

• 批准号: 2213533
• 金额: --
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2213533
• 关键词: Mixed; precision; techniques; numerical; simulations

Discrete Element Methods (DEM) simulate the interaction of large numbers of rigid, incompressible objects with each other. Mainstream DEM codes focus on analytical shapes (spheres, ellipsoids, ...) to streamline the identification of contacts between objects. This step nevertheless continues to dominate the simulation time. We study mixed precision, heterogeneous programming and data structure engineering techniques to tackle these performance hotspots plus to enable simulations to use nonanalytical, i.e. triangulated shapes.We study how we can separate data storage and exchange formats from compute formats such that we reduce the memory that needs to be accessed to check collisions. Next we investigate the potential for heterogeneous computing to accelerate simulations by offloading compute intense operations to GPUs while trying to hide/avoid the data transfer overheads of moving data between host and device memory. Alongside the work on heterogeneous compute there will be investigations into time stepping and integration schemes that complement the communication capabilities of modern heterogeneous systems. All of these steps are realised with different precision formats and combine different precisions and precision algorithms for different substeps. Lastly, we will explore what a programming language extension looks like to enable programmers to more easily take advantage of any mixed precision benefits we develop, i.e. we try to generalise our ideas to make them beneficial for more application areas.

离散元法（DEM）模拟大量刚性不可压缩物体之间的相互作用。主流DEM代码侧重于分析形状（球体、椭球体等）以简化对象之间的接触的识别。然而，这一步继续主导模拟时间。我们研究混合精度，异构编程和数据结构工程技术，以解决这些性能热点，并使模拟使用非分析，即三角形形状。我们研究如何将数据存储和交换格式与计算格式分开，以减少需要访问的内存来检查冲突。接下来，我们研究了异构计算的潜力，通过将计算密集型操作卸载到GPU来加速模拟，同时尝试隐藏/避免在主机和设备内存之间移动数据的数据传输开销。除了异构计算的工作，还将调查时间步进和集成方案，以补充现代异构系统的通信能力。所有这些步骤都以不同的精度格式实现，并且针对不同的子步骤组合联合收割机不同的精度和精度算法。最后，我们将探索编程语言扩展是什么样子的，以使程序员能够更容易地利用我们开发的任何混合精度优势，即我们试图概括我们的想法，使它们有利于更多的应用领域。