Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essential for reliable predictions of multiphysics interactions, but remains a grand challenge even for exascale supercomputers and advanced deep learning models. The extreme-resolution data required to represent turbulence, ranging from billions to trillions of grid points, pose prohibitive computational costs for models based on architectures like vision transformers. To address this challenge, we introduce a multiscale hierarchical Turbulence Transformer that reduces sequence length from billions to a few millions and a novel RingX sequence parallelism approach that enables scalable long-context learning. We perform scaling and science runs on the Frontier supercomputer. Our approach demonstrates excellent performance up to 1.1 EFLOPS on 32,768 AMD GPUs, with a scaling efficiency of 94%. To our knowledge, this is the first AI model for turbulence that can capture small-scale eddies down to the dissipative range.

Science ecosystems with networked computing systems and physical instruments are increasingly being deployed with a goal to achieve the productivity promised by AI-supported remote automation. In support of these efforts, the virtual infrastructure twins (VITs) have been successfully utilized to develop the orchestration codes for these ecosystems without requiring physical access to expensive instruments, such as electron microscopes. Currently, the utility of such a VIT is severely limited by the computing capacity and capability of the computing system used as its host. Furthermore, codes developed on the VIT typically need to be transferred and refactored for production use, particularly, on high-performance systems with accelerators. In response, we develop a design-to-deployment continuum platform wherein a VIT runs natively on the ecosystem's own computing system, and thereby facilitates the continualin-situtesting and transition of codes for production use. We describe the development and testing of software for remote microscope steering and GPU-based image reconstruction using this platform on a multi-GPU computing system networked to Nion microscopes. We demonstrate a continual transition of steering and reconstruction codes developed under VIT platform to production ecosystem deployment.

We propose a framework to develop cyber solutions to support remote steering of science instruments and measurements collection over instrument-computing ecosystems. It is based on provisioning separate data and control connections at the network level, and developing software modules consisting of Python wrappers for instrument commands and Pyro server-client codes that make them available across the ecosystem network. We demonstrate automated measurement transfers and remote steering operations in a microscopy use case for materials research over an ecosystem of Nion microscopes and computing platforms connected over site networks. The proposed framework is currently under further refinement and being adopted to science workflows with automated remote experiments steering for autonomous chemistry laboratories and smart energy grid simulations.

Advanced electron microscopy workflows require an ecosystem of microscope instruments and computing systems possibly located at different sites to conduct remotely steered and automated experiments. Current workflow executions involve manual operations for steering and measurement tasks, which are typically performed from control workstations co-located with microscopes; consequently, their operational tempo and effectiveness are limited. We propose an approach based on separate data and control channels for such an ecosystem of Scanning Transmission Electron Microscopes (STEM) and computing systems, for which no general solutions presently exist, unlike the neutron and light source instruments. We demonstrate automated measurement transfers and remote steering of Nion STEM physical instruments over site networks. We propose a Virtual Infrastructure Twin (VIT) of this ecosystem, which is used to develop and test our steering software modules without requiring access to the physical instrument infrastructure. Additionally, we develop a VIT for a multiple laboratory scenario, which illustrates the applicability of this approach to ecosystems connected over wide-area networks, for the development and testing of software modules and their later field deployment.

Summary The growth in size of networked high performance computers along with novel accelerator‐based node architectures has further emphasized the importance of communication efficiency in high performance computing. The world's largest high performance computers are usually operated as shared user facilities due to the costs of acquisition and operation. Applications are scheduled for execution in a shared environment and are placed on nodes that are not necessarily contiguous on the interconnect. Furthermore, the placement of tasks on the nodes allocated by the scheduler is sub‐optimal, leading to performance loss and variability. Here, we investigate the impact of task placement on the performance of two massively parallel application codes on the Titan supercomputer, a turbulent combustion flow solver (S3D) and a molecular dynamics code (LAMMPS). Benchmark studies show a significant deviation from ideal weak scaling and variability in performance. The inter‐task communication distance was determined to be one of the significant contributors to the performance degradation and variability. A genetic algorithm‐based parallel optimization technique was used to optimize the task ordering. This technique provides an improved placement of the tasks on the nodes, taking into account the application's communication topology and the system interconnect topology. Application benchmarks after task reordering through genetic algorithm show a significant improvement in performance and reduction in variability, thereby enabling the applications to achieve better time to solution and scalability on Titan during production. Copyright © 2015 John Wiley & Sons, Ltd.

Hybridization is the process of converting an application with a single level of parallelism to an application with multiple levels of parallelism. Over the past 15 years a majority of the applications that run on High Performance Computing systems have employed MPI for all of the parallelism within the application. In the Peta-Exascale computing regime, effective utilization of the hardware requires multiple levels of parallelism matched to the macro architecture of the system to achieve good performance. A hybridized code base is performance portable when sufficient parallelism is expressed in an architecture agnostic form to achieve good performance on a range of available systems. The hybridized S3D code is performance portable across today's leading many core and GPU accelerated systems. The OpenACC framework allows a unified code base to be deployed for either (Manycore CPU or Manycore CPU+GPU) while permitting architecture specific optimizations to expose new dimensions of parallelism to be utilized.

BlueGene/P (BG/P) is the second generation BlueGene architecture from IBM, succeeding BlueGene/L (BG/L). BG/P is a system-on-a-chip (SoC) design that uses four PowerPC 450 cores operating at 850 MHz with a double precision, dual pipe floating point unit per core. These chips are connected with multiple interconnection networks including a 3-D torus, a global collective network, and a global barrier network. The design is intended to provide a highly scalable, physically dense system with relatively low power requirements per flop. In this paper, we report on our examination of BG/P, presented in the context of a set of important scientific applications, and as compared to other major large scale supercomputers in use today. Our investigation confirms that BG/P has good scalability with an expected lower performance per processor when compared to the Cray XT4's Opteron. We also find that BG/P uses very low power per floating point operation for certain kernels, yet it has less of a power advantage when considering science driven metrics for mission applications.

The scientific simulation capabilities of next generation high-end computing technology will depend on striking a balance among memory, processor, I/O, and local and global network performance across the breadth of the scientific simulation space. The Cray XT4 combines commodity AMD dual core Opteron processor technology with the second generation of Cray's custom communication accelerator in a system design whose balance is claimed to be driven by the demands of scientific simulation. This paper presents an evaluation of the Cray XT4 using micro-benchmarks to develop a controlled understanding of individual system components, providing the context for analyzing and comprehending the performance of several petascale-ready applications. Results gathered from several strategic application domains are compared with observations on the previous generation Cray XT3 and other high-end computing systems, demonstrating performance improvements across a wide variety of application benchmark problems.

Typical large-scale scientific applications periodically write checkpoint files to save the computational state throughout execution. Existing parallel file systems improve such write-only I/O patterns through the use of client-side file caching and write-behind strategies. In distributed environments where files are rarely accessed by more than one client concurrently, file caching has achieved significant success; however, in parallel applications where multiple clients manipulate a shared file, cache coherence control can serialize I/O. We have designed a thread based caching layer for the MPI I/O library, which adds a portable caching system closer to user applications so more information about the application’s I/O patterns is available for better coherence control. We demonstrate the impact of our caching solution on parallel write performance with a comprehensive evaluation that includes a set of widely used I/O benchmarks and production application I/O kernels. 1.