利用片上便笺存储器实现深度时间分块：二维 Jacobian 五点迭代模板核（j2d5pt）案例研究
Exploiting Scratchpad Memory for Deep Temporal Blocking: A case study for 2D Jacobian 5-point iterative stencil kernel (j2d5pt)
AI总结：
针对GPGPU模板计算未充分利用便笺存储器的问题，本文以j2d5pt为案例提出大分块深度时间分块方法，在片上处理多时间步，以更简单且无需代码自动生成的实现达到接近最先进方法的性能。
摘要：
通用图形处理器（GPGPU）被用于高性能计算（HPC）中的大多数顶级系统。过去十年间，片上便笺存储器的总容量增长了40多倍。然而，现有采用时间分块的模板计算优化并未充分利用便笺存储器的大容量。本工作以二维 Jacobian 五点迭代模板为案例，研究大容量便笺存储器的使用。不同于现有研究以线程块方式对计算域进行分块，我们对计算域分块时使每个分块足够大，以利用GPU上所有可用的便笺存储器。因此，我们在单个分块内处理多个时间步，然后再将结果卸载回全局存储器。评估表明，我们的性能可与最先进的实现相媲美，同时实现要简单得多，且不需要代码自动生成。
英文摘要：
General Purpose Graphics Processing Units (GPGPU) are used in most of the top systems in HPC. The total capacity of scratchpad memory has increased by more than 40 times in the last decade. However, existing optimizations for stencil computations using temporal blocking have not aggressively exploited the large capacity of scratchpad memory. This work uses the 2D Jacobian 5-point iterative stencil as a case study to investigate the use of large scratchpad memory. Unlike existing research that tiles the domain in a thread block fashion, we tile the domain so that each tile is large enough to utilize all available scratchpad memory on the GPU. Consequently, we process several time steps inside a single tile before offloading the result back to global memory. Our evaluation shows that our performance is comparable to state-of-the-art implementations, yet our implementation is much simpler and does not require auto-generation of code.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
