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We review two decades of progress using the Enzo hydrodynamic cosmology code to simulate the Cosmic Dawn, a period of roughly 1 billion years beginning with the formation of the first stars in the universe, and ending with cosmic reionization. Using simulations of increasing size and complexity, working up in length and mass scale and to lower redshifts, a connected narrative is built up covering the entire epoch. In the first part of the paper, we draw on results we and our collaborators have achieved using the Enzo cosmological adaptive mesh refinement code. Topics include the formation of Population III stars, the transition to Population II star formation, chemical enrichment, the assembly of the first galaxies, their high redshift galaxy statistics, and their role in reionization. In the second part of the paper we highlight physical difficulties that will require new, more physically complex simulations to address, drawing from a broader literature survey. We discuss the healthy interplay between self-consistent numerical simulations and analytic and semi-analytic approaches. Finally, we discuss technical advances in hardware and software that will enable a new class of more realistic simulations to be carried out on exascale supercomputers in the future.

This chapter discusses the challenges and requirements of modern Research Data Management (RDM), particularly for biomedical applications in the context of high-performance computing (HPC). The FAIR data principles (Findable, Accessible, Interoperable, Reusable) are of special importance. Data formats, publication platforms, annotation schemata, automated data management and staging, the data infrastructure in HPC centers, file transfer and staging methods in HPC, and the EUDAT components are discussed. Tools and approaches for automated data movement and replication in cross-center workflows are explained, as well as the development of ontologies for structuring and quality-checking of metadata in computational biomedicine. The CompBioMed project is used as a real-world example of implementing these principles and tools in practice. The LEXIS project has built a workflow-execution and data management platform that follows the paradigm of HPC-Cloud convergence for demanding Big Data applications. It is used for orchestrating workflows with YORC, utilizing the data documentation initiative (DDI) and distributed computing resources (DCI). The platform is accessed by a user-friendly LEXIS portal for workflow and data management, making HPC and Cloud Computing significantly more accessible. Checkpointing, duplicate runs, and spare images of the data are used to create resilient workflows. The CompBioMed project is completing the implementation of such a workflow, using data replication and brokering, which will enable urgent computing on exascale platforms., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Checkpoint restart plays an important role in high-performance computing (HPC) applications, allowing simulation runtime to extend beyond a single job allocation and facilitating recovery from hardware failure. Yet, as machines grow in size and in complexity, traditional approaches to checkpoint restart are becoming prohibitive. Current methods store a subset of the application's state and exploit the memory hierarchy in the machine. However, as the energy cost of data movement continues to dominate, further reductions in checkpoint size are needed. Lossy compression, which can significantly reduce checkpoint sizes, offers a potential to reduce computational cost in checkpoint restart. This article investigates the use of numerical properties of partial differential equation (PDE) simulations, such as bounds on the truncation error, to evaluate the feasibility of using lossy compression in checkpointing PDE simulations. Restart from a checkpoint with lossy compression is considered for a fail-stop error in two time-dependent HPC application codes: PlasComCM and Nek5000. Results show that error in application variables due to a restart from a lossy compressed checkpoint can be masked by the numerical error in the discretization, leading to increased efficiency in checkpoint restart without influencing overall accuracy in the simulation. [ABSTRACT FROM AUTHOR]

We propose a novel, minimally intrusive approach to adding fault tolerance to existing complex scientific simulation codes, used for addressing a broad range of time-dependent problems on the next generation of supercomputers. Exascale systems have the potential to allow much larger, more accurate and scale-resolving simulations of transient processes than can be performed on current petascale systems. However, with a much larger number of components, exascale computers are expected to suffer a node failure every few minutes. Many existing parallel simulation codes are not tolerant of these failures and existing resilience methodologies would necessitate major modifications or redesign of the application. Our approach combines the proposed user-level failure mitigation extensions to the Message-Passing Interface (MPI), with the concepts of message-logging and remote in-memory checkpointing, to demonstrate how to add scalable resilience to transient solvers. Logging MPI communication reduces the storage requirement of static data, such as finite element operators, and allows a spare MPI process to rebuild these data structures independently of other ranks. Remote in-memory checkpointing avoids disk I/O contention on large parallel filesystems. A prototype implementation is applied to Nektar++, a scalable, production-ready transient simulation framework. Forward-path and recovery-path performance of the resilience algorithm is analysed through experiments using the solver for the incompressible Navier-Stokes equations, and strong scaling of the approach is observed. [ABSTRACT FROM AUTHOR]

Summary: A multilevel generalization of the dual‐primal finite element tearing and interconnecting (FETI‐DP) domain decomposition method is proposed for very large‐scale discrete problems to address the bottleneck associated with the solution of the coarse problems at such scales. This bottleneck destroys the parallel scalability of the original, two‐level FETI‐DP method when using more than a few thousand processor cores. In the multilevel formulation proposed here, the FETI‐DP method is applied recursively to solve all coarse problems but the smallest one. Crucially, this recursive application of the method is enabled by utilizing a new primal formulation of the augmentation/enrichment process of the coarse problems. The efficiency and scalability of the proposed approach are demonstrated for up to 32 768 processor cores, and large‐scale real‐world and benchmark problems with more than 21 billion degrees of freedom. The obtained performance results show that the three‐ and four‐level FETI‐DP methods exhibit a better scalability than the original two‐level FETI‐DP method. [ABSTRACT FROM AUTHOR]

With various exascale systems in different countries planned over the next three to five years, developing application software for such unprecedented computing capabilities and parallel scaling becomes a major challenge. In this study, we start our discussion with the current 125-Pflops Sunway TaihuLight system in China and its related application challenges and solutions. Based on our current experience with Sunway TaihuLight, we provide a projection into the next decade and discuss potential challenges and possible trends we would probably observe in future high performance computing software. [ABSTRACT FROM AUTHOR]

The ExaNeSt project started on December 2015 and is funded by EU H2020 research framework (call H2020-FETHPC-2014, n. 671553) to study the adoption of low-cost, Linux-based power-efficient 64-bit ARM processors clusters for Exascale-class systems. The ExaNeSt consortium pools partners with industrial and academic research expertise in storage, interconnects and applications that share a vision of an European Exascale-class supercomputer. The common goal is designing and implementing a physical rack prototype together with its cooling system, the non-volatile memory (NVM) architecture and a unified low-latency interconnect able to test different options for network and storage. Furthermore, the consortium goal is to provide real HPC applications to validate the system. In this paper we describe the unified data and storage network architecture, reporting on the status of development of different testbeds and highlighting preliminary benchmark results obtained through the execution of scientific, engineering and data analytics scalable application kernels. [ABSTRACT FROM AUTHOR]

High-performance computing systems continue to increase in size in the quest for ever higher performance. The resulting increased electronic component count, coupled with the decrease in feature sizes of the silicon manufacturing processes used to build these components, may result in future exascale systems being more susceptible to soft errors caused by cosmic radiation than in current high-performance computing systems. Through the use of techniques such as hardware-based error-correcting codes and checkpoint-restart, many of these faults can be mitigated at the cost of increased hardware overhead, run-time, and energy consumption that can be as much as 10–20%. Some predictions expect these overheads to continue to grow over time. For extreme scale systems, these overheads will represent megawatts of power consumption and millions of dollars of additional hardware costs, which could potentially be avoided with more sophisticated fault-tolerance techniques. In this paper we present new software-based fault tolerance techniques that can be applied to one of the most important classes of software in high-performance computing: iterative sparse matrix solvers. Our new techniques enables us to exploit knowledge of the structure of sparse matrices in such a way as to improve the performance, energy efficiency, and fault tolerance of the overall solution. [ABSTRACT FROM AUTHOR]

In the presence of accelerated fault rates, which are projected to be the norm on future exascale systems, it will become increasingly difficult for high-performance computing (HPC) applications to accomplish useful computation. Due to the fault-oblivious nature of current HPC programming paradigms and execution environments, HPC applications are insufficiently equipped to deal with errors. We believe that HPC applications should be enabled with capabilities to actively search for and correct errors in their computations. The redundant multithreading (RMT) approach offers lightweight replicated execution streams of program instructions within the context of a single application process. However, the use of complete redundancy incurs significant overhead to the application performance.In this paper we present RedThreads, an interface that provides application-level fault detection and correction based on RMT, but applies the thread-level redundancy adaptively. We describe the RedThreads syntax and semantics, and the supporting compiler infrastructure and runtime system. Our approach enables application programmers to scope the extent of redundant computation. Additionally, the runtime system permits the use of RMT to be dynamically enabled, or disabled, based on the resiliency needs of the application and the state of the system. Our experimental results demonstrate how adaptive RMT exploits programmer insight and runtime inference to dynamically navigate the trade-off space between an application’s resilience coverage and the associated performance overhead of redundant computation. [ABSTRACT FROM AUTHOR]

The Oak Ridge Leadership Computing Facilitys newest supercomputer, Summit, will supply five to ten times more computing power than current leadership-class systems. From facility enhancements to hardware procurement and application development, the project serves as a blueprint for advancing whats possible in scientific discovery. [ABSTRACT FROM AUTHOR]

Space weather is a rapidly growing field of science which studies processes occurring in the area of space between the Sun and the Earth. The development of space weather forecasting capabilities is a task of great societal relevance: space weather effects may damage a number of technological assets, among which power and communication lines, transformers, pipelines and the telecommunication infrastructure. Exascale computing is a fundamental ingredient for space weather forecasting tools based on physical, rather than statistical, models. We describe here our recent progresses towards a physics-based space weather forecasting tool with exascale computing. We select the semi-implicit, Particle In Cell, Implicit Moment Method implemented in the parallel, object-oriented, C++ iPic3D code as a promising starting point. We analyze the structure and the performances of the current version of the iPic3D code. We describe three algorithmic developments, the fully implicit method, the Multi-Level Multi-Domain method, and the fluid-kinetic method, which can help addressing the multiple spatial and temporal scales present in space weather simulations. We then examine, in a co-design approach, which requirements – vectorization, extreme parallelism and reduced communication – an application has to satisfy to fully exploit architectures such as GPUs and Xeon Phi’s. We address how to modify the iPic3D code to better satisfy these requirements. We then describe how to port the iPic3D code to the DEEP architecture currently under construction. The FP7 project DEEP ( www.deep-project.eu ) aims at building an exascale-ready machine composed of a cluster of Xeon nodes and of a collection of Xeon Phi coprocessors, used as boosters. The aim of the DEEP project is to enable exascale performance for codes, such as iPic3D, composed of parts which exhibit different potential for extreme scalability. Finally, we provide examples of simulations of space weather processes done with the current version of the iPic3D code. [ABSTRACT FROM AUTHOR]

Exascale applications will exploit a massive amount of parallelism. The analysis of computation and communication requirements at thread-level provides important insight into the application behavior useful to optimize the design of the exascale architecture. Performing such an analysis is challenging because the exascale system is not available yet. The target applications can be profiled only on existing machines, processing a significantly smaller amount of data and exploiting significantly less parallelism. To tackle this problem we propose a methodology that couples a) unsupervised machine-learning techniques to consistently classify threads in different program runs, and b) extrapolation techniques to learn how thread classes behave at scale. The main contribution of this work is the classification methodology that assigns a class to each thread observed during a set of experimental runs carried out by varying the parallelism and the processed data size. Based on this classification we generate extrapolation models per thread class to predict the profile at a scale significantly larger than the initial experiments. The availability of per-thread-class extrapolation models simplifies the analysis of exascale systems because we manage a small number of thread classes rather than a huge number of individual threads. We apply the methodology to different computing domains including: large-scale graph analytics, fluid dynamics, and radio astronomy. The proposed approach accurately classifies threads, whereas state-of-the-art techniques fail. The resulting extrapolation models have prediction errors of less than 10% for a real-life radio-astronomy case study. [ABSTRACT FROM AUTHOR]

Large-scale high performance computing is permeating nearly every corner of modern applications spanning from scientific research and business operations, to medical diagnostics, and national security. All these communities rely on computer systems to process vast volumes of data quickly and efficiently, yet progress toward increased computing power has experienced a slowdown in the last number of years. The sheer cost and scale, stemming from the need for extreme parallelism, are among the reasons behind this stall. In particular, very large-scale, ultra-high bandwidth interconnects, essential for maintaining computation performance, represent an increasing portion of the total cost budget. Photonic systems are often cited as ways to break through the energy-bandwidth limitations of conventional electrical wires toward drastically improving interconnect performance. This paper presents an overview of the challenges associated with large-scale interconnects, and reviews how photonic technologies can contribute to addressing these challenges. We review some important aspects of photonics that should not be underestimated in order to truly reap the benefits of cost and power reduction. [ABSTRACT FROM AUTHOR]

The move from current petascale machines to future exascale machines will need both hardware improvements and software changes. Hardware will need to evolve to focus primarily on features that lower energy consumption: near-threshold voltage operation, fine-grained power and clock management and heterogeneity. Software will also need to evolve and be able to express more parallelism, become more dynamic and adaptable in order to be able to operate on a much more variable hardware. In this paper, we present Traleika Glacier, an effort that seeks to evaluate radical design changes to meet the constraints, both in terms of power and cost, of exascale computing. The salient features of the hardware design presented in the work include a) a use of heterogeneous cores, b) a redesign of the memory system that centers around hierarchical scratchpads and a global address space, c) the hardware acceleration of certain memory and network operations through specialized engines and, d) very fine-grained control and monitoring capabilities. On the software side, we describe a task-based runtime system, the Open Community Runtime (OCR) which aims to express a wide range of higher-level programming models with a very limited set of core concepts: event-driven tasks for computation, events for synchronization and relocatable data-blocks for data management. [ABSTRACT FROM AUTHOR]

On the eve of exascale computing, traditional wisdom no longer applies. High-performance computing is gone as we know it. This article discusses a range of new algorithmic techniques emerging in the context of exascale computing, many of which defy the common wisdom of high-performance computing and are considered unorthodox, but could turn out to be a necessity in near future. [ABSTRACT FROM PUBLISHER]

The US Department of Energy's (DOE) Exascale Computing Project is a partnership between the DOE Office of Science and the National Nuclear Security Administration. Its mission is to transform today's high-performance computing (HPC) ecosystem by executing a multifaceted plan: developing mission-critical applications of unprecedented complexity; supporting US national security initiatives; partnering with the US HPC industry to develop exascale computer architectures; collaborating with US software vendors to develop a software stack that is both exascale-capable and usable on US industrial- and academic-scale systems; and training the next-generation workforce of computer and computational scientists, engineers, mathematicians, and data scientists. [ABSTRACT FROM PUBLISHER]

This paper presents an effective method of metadata rebalance in exascale distributed file systems. Exponential data growth has led to the need for an adaptive and robust distributed file system whose typical architecture is composed of a large cluster of metadata servers and data servers. Though each metadata server can have an equally divided subset from the entire metadata set at first, there will eventually be a global imbalance in the placement of metadata among metadata servers, and this imbalance worsens over time. To ensure that disproportionate metadata placement will not have a negative effect on the intrinsic performance of a metadata server cluster, it is necessary to recover the balanced performance of the cluster periodically. However, this cannot be easily done because rebalancing seriously hampers the normal operation of a file system. This situation continues to get worse with both an ever-present heavy workload on the file system and frequent failures of server components at exascale. As one of the primary reasons for such a degraded performance, file system clients frequently fail to look up metadata from the metadata server cluster during the period of metadata rebalance; thus, metadata operations cannot proceed at their normal speed. We propose a metadata rebalance model that minimizes failures of metadata operations during the metadata rebalance period and validate the proposed model through a cost analysis. The analysis results demonstrate that our model supports the feasibility of online metadata rebalance without the normal operation obstruction and increases the chances of maintaining balance in a huge cluster of metadata servers. [ABSTRACT FROM AUTHOR]

Petascale data management and analysis remain one of the main unresolved challenges in today's computing. The 6th Extremely Large Databases workshop was convened alongside the XLDB conference to discuss the challenges in the health care, biology, and natural resources communities. The role of cloud computing, the dominance of file-based solutions in science applications, in-situ and predictive analysis, and commercial software use in academic environments were discussed in depth as well. This paper summarizes the discussions of this workshop.

The 5th XLDB workshop brought together scientific and industrial users, developers, and researchers of extremely large data and focused on emerging challenges in the healthcare and genomics communities, spreadsheet-based large scale analysis, and challenges in applying statistics to large scale analysis, including machine learning. Major problems discussed were the lack of scalable applications, the lack of expertise in developing solutions, the lack of respect for or attention to big data problems, data volume growth exceeding Moore's Law, poorly scaling algorithms, and poor data quality and integration. More communication between users, developers, and researchers is sorely needed. A variety of future work to help all three groups was discussed, ranging from collecting challenge problems to connecting with particular industrial or academic sectors.