(150 word max) We present a first-of-kind mesh-refined (MR) massively parallel Particle-In-Cell (PIC) code for kinetic plasma simulations optimized on the Frontier, Fugaku, Summit, and Perlmutter supercomputers. Major innovations, implemented in the WarpX PIC code, include: (i) a three level parallelization strategy that demonstrated performance portability and scaling on millions of A64FX cores and tens of thousands of AMD and Nvidia GPUs (ii) a groundbreaking mesh refinement capability that provides between 1.5 x to 4 x savings in computing requirements on the science case reported in this paper, (iii) an efficient load balancing strategy between multiple MR levels. The MR PIC code enabled 3D simulations of laser-matter interactions on Frontier, Fugaku, and Summit, which have so far been out of the reach of standard codes. These simulations helped remove a major limitation of compact laser-based electron accelerators, which are promising candidates for next generation high-energy physics experiments and ultra-high dose rate FLASH radiotherapy.

WarpX is a general purpose electromagnetic particle-in-cell code that was originally designed to run on many-core CPU architectures. We describe the strategy, based on the AMReX library, followed to allow WarpX to use the GPU-accelerated nodes on OLCF’s Summit supercomputer, a strategy we believe will extend to the upcoming machines Frontier and Aurora. We summarize the challenges encountered, lessons learned, and give current performance results on a series of relevant benchmark problems.

Astrophysical explosions such as supernovae are fascinating events that require sophisticated algorithms and substantial computational power to model. Castro and MAESTROeX are nuclear astrophysics codes that simulate thermonuclear fusion in the context of supernovae and X-ray bursts. Examining these nuclear burning processes using high resolution simulations is critical for understanding how these astrophysical explosions occur. In this paper we describe the changes that have been made to these codes to transform them from standard MPI + OpenMP codes targeted at petascale CPU-based systems into a form compatible with the pre-exascale systems now online and the exascale systems coming soon. We then discuss what new science is possible to run on systems such as Summit and Perlmutter that could not have been achieved on the previous generation of supercomputers.

GPUs are employed to accelerate scientific applications however they require much more programming effort from the programmers particularly because of the disjoint address spaces between the host and the device. OpenACC and OpenMP 4.0 provide directive based programming solutions to alleviate the programming burden however synchronous data movement can create a performance bottleneck in fully taking advantage of GPUs. We propose a tiling based programming model and its library that simplifies the development of GPU programs and overlaps the data movement with computation. The programming model decomposes the data and computation into tiles and treats them as the main data transfer and execution units, which enables pipelining the transfers to hide the transfer latency. Moreover, partitioning application data into tiles allows the programmer to still take advantage of GPU even though application data cannot fit into the device memory. The library leverages C++ lambda functions, OpenACC directives, CUDA streams and tiling API from TiDA to support both productivity and performance. We show the performance of the library on a data transfer-intensive and a compute-intensive kernels and compare its speedup against OpenACC and CUDA. The results indicate that the library can hide the transfer latency, handle the cases where there is no sufficient device memory, and achieves reasonable performance.

Hardware architecture is increasingly complex, urging the development of asynchronous runtime systems with advance resource and locality management supports. However, these supports may come at the cost of complicating the user interface while programming remains one of the major constraints to wide adoption of asynchronous runtimes in practice. In this paper, we propose a solution that leverages application metadata to enable challenging optimizations as well as to facilitate the task of transforming legacy code to an asynchronous representation. We develop Perilla, a task graph-based runtime system that requires only modest programming effort. Perilla utilizes metadata of an AMR software framework to enable various optimizations at the communication layer without complicating its API. Experimental results with different applications on up to 24K processor cores show that Perilla can realize up to 1.44x speedup over the synchronous code variant. The metadata enabled optimizations account for 25% to 100% of the performance improvement.