Efficient simulation of complex plasma dynamics is crucial for advancing fusion energy research. Particle-in-Cell (PIC) Monte Carlo (MC) simulations provide insights into plasma behavior, including turbulence and confinement, which are essential for optimizing fusion reactor performance. Transitioning to exascale simulations introduces significant challenges, with traditional file input/output (I/O) inefficiencies remaining a key bottleneck. This work advances BIT1, an electrostatic PIC MC code, by improving the particle mover with OpenMP task-based parallelism, integrating openPMD’s streaming API, and enabling in-memory data streaming with the ADIOS2 Sustainable Staging Transport (SST) engine to enhance I/O performance, computational efficiency, and system storage utilization. We employ profiling tools such as gprof, perf, IPM and Darshan, which provide insights into computation, communication, and I/O operations. We implement time-dependent data checkpointing with the openPMD API enabling seamless data movement and in-situ visualization for real-time analysis without interrupting the simulation. We demonstrate improvements in simulation runtime, data accessibility and real-time insights by comparing traditional file I/O with the ADIOS2 BP4 and SST backends. The proposed hybrid BIT1 openPMD SST enhancement introduces a new paradigm for real-time scientific discovery in plasma simulations, enabling faster insights and more efficient use of exascale computing resources.

Maximizing resource efficiency is crucial when designing cloud-based systems,which are primarily built to meet specific quality-of-service requirements.Common optimization techniques include containerization, workflow orchestration,elasticity, and vertical scaling, all aimed at improving resource utilizationand reducing costs. In contrast, on-premises high-performance computingsystems prioritize maximum performance, typically relying on static resourceallocation. While this approach offers certain advantages over cloud systems,it can be restrictive in handling the increasingly dynamic resource demands oftightly coupled HPC workloads, making adaptive resource management challenging.

This thesis explores the execution of high-performance workloads in cloudbasedenvironments, investigating both horizontal and vertical scaling strategiesas well as the feasibility of running HPC workflows in the cloud. Additionally,we will evaluate the costs of deploying these workloads in containerizedenvironments and examine the advantages of using object storagein cloud-based HPC systems.

Att maximera resurseffektiviteten ar avgörande vid utformningen av molnbaserade system, som framst byggs för att uppfylla specifika krav på tjänstekvalitet. Vanliga optimeringstekniker inkluderar containerisering, arbetsflödesorkestrering, elasticitet och vertikal skalning, med målet att förbättra resursutnyttjandet och minska kostnaderna. I kontrast fokuserar lokala högprestandaberäkningssystem (HPC) på maximal prestanda och förlitar sig oftast på statisk resursallokering. Även om denna strategi har vissa fördelar jämfört med molnlösningar, kan den vara begränsande när det gäller att hantera de allt mer dynamiska resursbehoven hos tätt sammankopplade HPC-arbetslaster, vilket gör adaptiv resursförvaltning utmanande. Denna avhandling undersöker körningen av högprestandaarbetslaster i molnbaserade miljöer, med fokus på både horisontell och vertikal skalning samt möjligheten att köra HPC-arbetsflöden i molnet. Dessutom kommer vi att analysera kostnaderna for att distribuera dessa arbetslaster i containeriserade miljöer och utvärdera fördelarna med att använda objektlagring i molnbaserade HPC-system.

Recent development in lightweight OS-level virtualization, containers, provides a potential solution for running HPC applications on the cloud platform. In this work, we focus on the impact of different layers in a containerized environment when migrating HPC containers from a dedicated HPC system to a cloud platform. On three ARM-based platforms, including the latest Nvidia Grace CPU, we use six representative HPC applications to characterize the impact of container virtualization, host OS and kernel, and rootless and privileged container execution. Our results indicate less than 4% container overhead in DGEMM, miniMD, and XSBench, but 8%-10% overhead in FFT, HPCG, and Hypre. We also show that changing between the container execution modes results in negligible performance differences in the six applications.

High-performance GPU-accelerated particle filter methods are critical for object detection applications, ranging from autonomous driving, robot localization, to time-series prediction. In this work, we investigate the design, development and optimization of particle-filter using half-precision on CUDA cores and compare their performance and accuracy with single- and double-precision baselines on Nvidia V100, A100, A40 and T4 GPUs. To mitigate numerical instability and precision losses, we introduce algorithmic changes in the particle filters. Using half-precision leads to a performance improvement of 1.5–2 × and 2.5–4.6 × with respect to single- and double-precision baselines respectively, at the cost of a relatively small loss of accuracy.

Large-scale HPC simulations of plasma dynamics in fusion devices require efficient parallel I/O to avoid slowing down the simulation and to enable the post-processing of critical information. Such complex simulations lacking parallel I/O capabilities may encounter performance bottlenecks, hindering their effectiveness in data-intensive computing tasks. In this work, we focus on introducing and enhancing the efficiency of parallel I/O operations in Particle-in-Cell Monte Carlo simulations. We first evaluate the scalability of BIT1, a massively-parallel electrostatic PIC MC code, determining its initial write throughput capabilities and performance bottlenecks using an HPC I/O performance monitoring tool, Darshan. We design and develop an adaptor to the openPMD I/O interface that allows us to stream PIC particle and field information to I/O using the BP4 backend, aggressively optimized for I/O efficiency, including the highly efficient ADIOS2 interface. Next, we explore advanced optimization techniques such as data compression, aggregation, and Lustre file striping, achieving write throughput improvements while enhancing data storage efficiency. Finally, we analyze the enhanced high-throughput parallel I/O and storage capabilities achieved through the integration of openPMD with rapid metadata extraction in BP4 format. Our study demonstrates that the integration of openPMD and advanced I/O optimizations significantly enhances BIT1's I/O performance and storage capabilities, successfully introducing high throughput parallel I/O and surpassing the capabilities of traditional file I/O.

The new emerging scientific workloads to be executed in the upcoming exascale supercomputers face major challenges in terms of storage, given their extreme volume of data. In particular, intelligent data placement, instrumentation, and workflow handling are central to application performance. The IO-SEA project developed multiple solutions to aid the scientific community in adressing these challenges: a Workflow Manager, a hierarchical storage management system, and a semantic API for storage. All of these major products incorporate additional minor products that support their mission. In this paper, we discuss both the roles of all these products and how they can assist the scientific community in achieving exascale performance.

Matrix multiplication is a core computational part of deep learning and scientific workloads. The emergence of Matrix Cores in high-end AMD GPUs, a building block of Exascale computers, opens new opportunities for optimizing the performance and power efficiency of compute-intensive applications. This work provides a timely, comprehensive characterization of the novel Matrix Cores in AMD GPUs. We develop low-level micro-benchmarks for leveraging Matrix Cores at different levels of parallelism, achieving up to 350, 88, and 69 TFLOPS for mixed, float, and double precision on one GPU. Using results obtained from the micro-benchmarks, we provide a performance model of Matrix Cores that can guide application developers in performance tuning. We also provide the first quantitative study and modeling of the power efficiency of Matrix Cores at different floating-point data types. Finally, we evaluate the high- level programmability of Matrix Cores through the rocBLAS library in a wide range of matrix sizes from 16 to 64K. Our results indicate that application developers can transparently leverage Matrix Cores to deliver more than 92% peak computing throughput by properly selecting data types and interfaces.

OpenCUBE aims to develop an open-source full software stack for Cloud computing blueprint deployed on EPI hardware, adaptable to emerging workloads across the computing continuum. OpenCUBE prioritizes energy awareness and utilizes open APIs, Open Source components, advanced SiPearl Rhea processors, and RISC-V accelerator. The project leverages representative workloads, such as cloud-native workloads and workflows of weather forecast data management, molecular docking, and space weather, for evaluation and validation.