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        <title>Frontiers in High Performance Computing | Architecture and Systems section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/high-performance-computing/sections/architecture-and-systems</link>
        <description>RSS Feed for Architecture and Systems section in the Frontiers in High Performance Computing journal | New and Recent Articles</description>
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        <pubDate>2026-05-02T22:22:08.440+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2026.1664774</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2026.1664774</link>
        <title><![CDATA[Toward energy-efficiency: CNTD_MERIC approach for energy-aware MPI applications]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kashaf Ad Dooja</author><author>Osman Yasal</author><author>Ondrej Vysocky</author><author>Lubomir Riha</author><author>Daniele Cesarini</author><author>Andrea Bartolini</author>
        <description><![CDATA[Energy efficiency is a major challenge in High-Performance Computing (HPC) systems, impairing their scale, performance, and sustainability. Despite technological and research progress, there is still a lack of software methods to measure and assess the energy efficiency of computing codes at scale. This is also exacerbated by the emergence of newer ISAs in the HPC computing spectrum with non-unified interfaces for power and energy monitoring. In this work, we present CNTD_MERIC, which integrates two state-of-the-art energy monitoring and optimization libraries for HPC systems, COUNTDOWN and MERIC. COUNTDOWN is an energy-aware runtime system for MPI applications. MERIC is a platform-agnostic runtime system and energy measurement library that optimizes energy efficiency by adjusting hardware configurations. CNTD_MERIC combines the benefits of these two approaches with low overhead, resulting in a portable power management runtime system for MPI applications. We evaluated the integrated library on both ARM and x86 compute nodes in the production environment of the IT4Innovations supercomputing center (IT4I). The results show that CNTD_MERIC achieves similar performance to the original COUNTDOWN and MERIC implementations in terms of energy optimization and power/energy measurement, with negligible overheads within −5% to +3% compared to the original COUNTDOWN configurations. We also implemented CNTD_MERIC for multi-architecture (x86 and ARM) comparison between Intel Sapphire Rapids and A64FX processors. The results indicate that A64FX achieves significantly lower execution time, reduced energy-to-solution, and lower average power consumption (110–132 vs. 400–590 W), confirming its efficiency for energy-efficient HPC systems.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1709051</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1709051</link>
        <title><![CDATA[The Score-P performance tools ecosystem]]></title>
        <pubdate>2026-03-24T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Christian Feld</author><author>Alexandru Calotoiu</author><author>Gregor Corbin</author><author>Markus Geimer</author><author>Marc-André Hermanns</author><author>Maximilian Knespel</author><author>Bernd Mohr</author><author>Jan André Reuter</author><author>Maximilian Sander</author><author>Pavel Saviankou</author><author>Marc Schlütter</author><author>Robert Schöne</author><author>Sameer S. Shende</author><author>Anke Visser</author><author>Bert Wesarg</author><author>William R. Williams</author><author>Felix Wolf</author><author>Brian J. N. Wylie</author><author>Mikhail Zarubin</author>
        <description><![CDATA[With the first exascale computing systems in production, tuning and scaling HPC applications to fully utilize the available hardware resources has become more important than ever. Thus, there is a strong need for software tools that assist application developers with this task. The Score-P instrumentation and measurement infrastructure plays a major role in filling this gap. Score-P is a community-driven, highly scalable tool suite for profiling and event tracing of massively parallel HPC application codes, and aimed to be easy to use. It provides measurement data via common data formats and runtime interfaces for a variety of complementary analysis tools developed by multiple institutions and companies, allowing users to gain insights into the communication, synchronization, input/output, and scaling behavior of their applications, pinpointing performance bottlenecks and their causes. In this article, we provide an overview of the current state of the Score-P infrastructure and its related tools ecosystem Cube, Extra-P, TAU, Scalasca, and Vampir. In particular, we detail Score-P's current design and architecture, both of which are highly flexible and extensible. Moreover, we describe how Score-P interacts with the analysis tools mentioned above and highlight the major extensions implemented over the past 10+ years to keep pace with the rapidly changing landscape of HPC hardware and parallel application programming interfaces. Furthermore, we discuss emerging challenges, particularly with respect to the ever-growing heterogeneity in both hardware and software, for collecting and analyzing performance data from applications running on future top-tier computing systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2026.1771927</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2026.1771927</link>
        <title><![CDATA[OpenMP-annotated code dataset for large language model fine-tuning on parallel programming tasks]]></title>
        <pubdate>2026-02-23T00:00:00Z</pubdate>
        <category>Data Report</category>
        <author>Nichole Etienne</author><author>Simon Garcia de Gonzalo</author><author>Dorian Arnold</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1763887</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1763887</link>
        <title><![CDATA[Correction: Processor simulation as a tool for performance engineering]]></title>
        <pubdate>2026-01-08T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Frontiers Production Office </author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1669101</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1669101</link>
        <title><![CDATA[Processor simulation as a tool for performance engineering]]></title>
        <pubdate>2025-12-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Carlos Falquez</author><author>Shiting Long</author><author>Nam Ho</author><author>Estela Suarez</author><author>Dirk Pleiter</author>
        <description><![CDATA[The diversity of processor architectures used for High-Performance Computing (HPC) applications has increased significantly over the last few years. This trend is expected to continue for different reasons, including the emergence of various instruction set extensions. Examples are the renewed interest in vector instructions like Arm's Scalable Vector Extension (SVE) or RISC-V's RVV. For application developers, research software developers, and performance engineers, the increased diversity and complexity of architectures have led to the following challenges: Limited access to these different processor architectures and more difficult root cause analysis in case of performance issues. To address these challenges, we propose leveraging the much-improved capabilities of processor simulators such as gem5. We enhanced this simulator with a performance analysis framework. We extend available performance counters and introduce new analysis capabilities to track the temporal behaviour of running applications. An algorithm has been implemented to link these statistics to specific regions. The resulting performance profiles allow for the identification of code regions with the potential for optimization. The focus is on observables to monitor quantities that are usually not directly accessible on real hardware. Different algorithms have been implemented to identify potential performance bottlenecks. The framework is evaluated for different types of HPC applications like the molecular-dynamics application GROMACS, Ligra, which implements the breadth-first search (BFS) algorithm, and a kernel from the Lattice QCD solver DD-αAMG.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1393936</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1393936</link>
        <title><![CDATA[An analysis of the I/O semantic gaps of HPC storage stacks]]></title>
        <pubdate>2025-08-11T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Sebastian Oeste</author><author>Patrick Höhn</author><author>Michael Kluge</author><author>Julian Kunkel</author>
        <description><![CDATA[Modern high-performance computing (HPC) Input/Output (I/O) systems consist of stacked hard- and software layers that provide interfaces for data access. Depending on application needs, developers usually choose higher layers with richer semantics for the ease of use or lower layers for performance. Each I/O interface on a given stack consists of a set of operations and their syntactic definition, as well as a set of semantic properties. To properly function, high-level libraries such as Hierarchical Data Format version 5 (HDF5) need to map their semantics to lower-level Application Programming Interface (API) such as Portable Operating System Interface (POSIX). Lower-level storage backends provide different I/O semantics than the layers in the stack above while sometimes implementing the same interface. However, most I/O interfaces do not transport semantic information through their APIs. Ideally, no semantics of an I/O operation should be lost while passing through the I/O stack, allowing lower layers to optimize performance. Unfortunately, there is a lack of general definition and unified taxonomy of I/O semantics. Similarly, system-level APIs offer little support for passing semantics to underlying layers. Thus, passing semantic information between layers is currently not feasible. In this article, we systematically compare I/O interfaces by examining their semantics across the HPC I/O stack. Our primary goal is to provide a taxonomy and comparative analysis, not to propose a new I/O interface or implementation. We propose a general definition of I/O semantics and present a unified classification of I/O semantics based on the categories of concurrent access, persistency, consistency, spatiality, temporality, and mutability. This allows us to compare I/O interfaces in terms of their I/O semantics. We show that semantic information is lost while traveling through the storage stack, which often prevents the underlying storage backends from making the proper performance and consistency decisions. In other words, each layer acts like a semantic filter for the lower layers. We discuss how higher-level abstractions could propagate their semantics and assumptions down through the lower-levels of the I/O stack. As a possible mitigation, we discuss the conceptual design of semantics-aware interfaces, to illustrate how such interfaces might address semantic loss—though we do not propose a concrete new implementation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1570210</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1570210</link>
        <title><![CDATA[FlexNPU: a dataflow-aware flexible deep learning accelerator for energy-efficient edge devices]]></title>
        <pubdate>2025-06-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Arnab Raha</author><author>Deepak A. Mathaikutty</author><author>Shamik Kundu</author><author>Soumendu K. Ghosh</author>
        <description><![CDATA[This paper introduces FlexNPU, a Flexible Neural Processing Unit, which adopts agile design principles to enable versatile dataflows, enhancing energy efficiency. Unlike conventional convolutional neural network accelerator architectures that adhere to fixed dataflows (such as input, weight, output, or row stationary) to transfer activations and weights between storage and compute units, our design revolutionizes by enabling adaptable dataflows of any type through configurable software descriptors. Considering that data movement costs considerably outweigh compute costs from an energy perspective, the flexibility in dataflow allows us to optimize the movement per layer for minimal data transfer and energy consumption, a capability unattainable in fixed dataflow architectures. To further enhance throughput and reduce energy consumption in the FlexNPU architecture, we propose a novel sparsity-based acceleration logic that utilizes fine-grained sparsity in both the activation and weight tensors to bypass redundant computations, thus optimizing the convolution engine within the hardware accelerator. Extensive experimental results underscore a significant improvement in the performance and energy efficiency of FlexNPU compared to existing DNN accelerators.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1611997</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1611997</link>
        <title><![CDATA[Editorial: Scientific workflows at extreme scales]]></title>
        <pubdate>2025-05-26T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Anshu Dubey</author><author>Erik Draeger</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1303358</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1303358</link>
        <title><![CDATA[Nek5000/RS performance on advanced GPU architectures]]></title>
        <pubdate>2025-02-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Misun Min</author><author>Yu-Hsiang Lan</author><author>Paul Fischer</author><author>Thilina Rathnayake</author><author>John Holmen</author>
        <description><![CDATA[The authors explore performance scalability of the open-source thermal-fluids code, NekRS, on the U.S. Department of Energy's leadership computers, Crusher, Frontier, Summit, Perlmutter, and Polaris. Particular attention is given to analyzing performance and time-to-solution at the strong-scale limit for a target efficiency of 80%, which is typical for production runs on the DOE's high-performance computing systems. Several examples of anomalous behavior are also discussed and analyzed.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1472719</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1472719</link>
        <title><![CDATA[Wilkins: HPC in situ workflows made easy]]></title>
        <pubdate>2024-11-20T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Orcun Yildiz</author><author>Dmitriy Morozov</author><author>Arnur Nigmetov</author><author>Bogdan Nicolae</author><author>Tom Peterka</author>
        <description><![CDATA[In situ approaches can accelerate the pace of scientific discoveries by allowing scientists to perform data analysis at simulation time. Current in situ workflow systems, however, face challenges in handling the growing complexity and diverse computational requirements of scientific tasks. In this work, we present Wilkins, an in situ workflow system that is designed for ease-of-use while providing scalable and efficient execution of workflow tasks. Wilkins provides a flexible workflow description interface, employs a high-performance data transport layer based on HDF5, and supports tasks with disparate data rates by providing a flow control mechanism. Wilkins seamlessly couples scientific tasks that already use HDF5, without requiring task code modifications. We demonstrate the above features using both synthetic benchmarks and two science use cases in materials science and cosmology.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1394615</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1394615</link>
        <title><![CDATA[ExaWorks software development kit: a robust and scalable collection of interoperable workflows technologies]]></title>
        <pubdate>2024-10-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Matteo Turilli</author><author>Mihael Hategan-Marandiuc</author><author>Mikhail Titov</author><author>Ketan Maheshwari</author><author>Aymen Alsaadi</author><author>Andre Merzky</author><author>Ramon Arambula</author><author>Mikhail Zakharchanka</author><author>Matt Cowan</author><author>Justin M. Wozniak</author><author>Andreas Wilke</author><author>Ozgur Ozan Kilic</author><author>Kyle Chard</author><author>Rafael Ferreira da Silva</author><author>Shantenu Jha</author><author>Daniel Laney</author>
        <description><![CDATA[Scientific discovery increasingly requires executing heterogeneous scientific workflows on high-performance computing (HPC) platforms. Heterogeneous workflows contain different types of tasks (e.g., simulation, analysis, and learning) that need to be mapped, scheduled, and launched on different computing. That requires a software stack that enables users to code their workflows and automate resource management and workflow execution. Currently, there are many workflow technologies with diverse levels of robustness and capabilities, and users face difficult choices of software that can effectively and efficiently support their use cases on HPC machines, especially when considering the latest exascale platforms. We contributed to addressing this issue by developing the ExaWorks Software Development Kit (SDK). The SDK is a curated collection of workflow technologies engineered following current best practices and specifically designed to work on HPC platforms. We present our experience with (1) curating those technologies, (2) integrating them to provide users with new capabilities, (3) developing a continuous integration platform to test the SDK on DOE HPC platforms, (4) designing a dashboard to publish the results of those tests, and (5) devising an innovative documentation platform to help users to use those technologies. Our experience details the requirements and the best practices needed to curate workflow technologies, and it also serves as a blueprint for the capabilities and services that DOE will have to offer to support a variety of scientific heterogeneous workflows on the newly available exascale HPC platforms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1414569</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1414569</link>
        <title><![CDATA[ExaFEL: extreme-scale real-time data processing for X-ray free electron laser science]]></title>
        <pubdate>2024-10-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Johannes P. Blaschke</author><author>Robert Bolotovsky</author><author>Aaron S. Brewster</author><author>Jeffrey Donatelli</author><author>Antoine DuJardin</author><author>Wu-chun Feng</author><author>Vidya Ganapati</author><author>Wilko Kroeger</author><author>Derek Mendez</author><author>Peter McCorquodale</author><author>Seema Mirchandaney</author><author>Christopher P. O'Grady</author><author>Daniel W. Paley</author><author>Amedeo Perazzo</author><author>Frederic P. Poitevin</author><author>Billy K. Poon</author><author>Vinay B. Ramakrishnaiah</author><author>Nicholas K. Sauter</author><author>Niteya Shah</author><author>Elliott Slaughter</author><author>Christine Sweeney</author><author>Daniel Tchoń</author><author>Monarin Uervirojnangkoorn</author><author>Felix Wittwer</author><author>Michael E. Wall</author><author>Chun Hong Yoon</author><author>Iris D. Young</author>
        <description><![CDATA[ExaFEL is an HPC-capable X-ray Free Electron Laser (XFEL) data analysis software suite for both Serial Femtosecond Crystallography (SFX) and Single Particle Imaging (SPI) developed in collaboration with the Linac Coherent Lightsource (LCLS), Lawrence Berkeley National Laboratory (LBNL) and Los Alamos National Laboratory. ExaFEL supports real-time data analysis via a cross-facility workflow spanning LCLS and HPC centers such as NERSC and OLCF. Our work therefore constitutes initial path-finding for the US Department of Energy's (DOE) Integrated Research Infrastructure (IRI) program. We present the ExaFEL team's 7 years of experience in developing real-time XFEL data analysis software for the DOE's exascale supercomputers. We present our experiences and lessons learned with the Perlmutter and Frontier supercomputers. Furthermore we outline essential data center services (and the implications for institutional policy) required for real-time data analysis. Finally we summarize our software and performance engineering approaches and our experiences with NERSC's Perlmutter and OLCF's Frontier systems. This work is intended to be a practical blueprint for similar efforts in integrating exascale compute resources into other cross-facility workflows.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1497384</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1497384</link>
        <title><![CDATA[Corrigendum: Using open-science workflow tools to produce SCEC CyberShake physics-based probabilistic seismic hazard models]]></title>
        <pubdate>2024-09-26T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Scott Callaghan</author><author>Philip J. Maechling</author><author>Fabio Silva</author><author>Mei-Hui Su</author><author>Kevin R. Milner</author><author>Robert W. Graves</author><author>Kim B. Olsen</author><author>Yifeng Cui</author><author>Karan Vahi</author><author>Albert Kottke</author><author>Christine A. Goulet</author><author>Ewa Deelman</author><author>Thomas H. Jordan</author><author>Yehuda Ben-Zion</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1390709</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1390709</link>
        <title><![CDATA[A galactic approach to neutron scattering science]]></title>
        <pubdate>2024-08-07T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Gregory R. Watson</author><author>Thomas A. Maier</author><author>Sergey Yakubov</author><author>Peter W. Doak</author>
        <description><![CDATA[Neutron scattering science is leading to significant advances in our understanding of materials and will be key to solving many of the challenges that society is facing today. Improvements in scientific instruments are actually making it more difficult to analyze and interpret the results of experiments due to the vast increases in the volume and complexity of data being produced and the associated computational requirements for processing that data. New approaches to enable scientists to leverage computational resources are required, and Oak Ridge National Laboratory (ORNL) has been at the forefront of developing these technologies. We recently completed the design and initial implementation of a neutrons data interpretation platform that allows seamless access to the computational resources provided by ORNL. For the first time, we have demonstrated that this platform can be used for advanced data analysis of correlated quantum materials by utilizing the world's most powerful computer system, Frontier. In particular, we have shown the end-to-end execution of the DCA++ code to determine the dynamic magnetic spin susceptibility χ(q, ω) for a single-band Hubbard model with Coulomb repulsion U/t = 8 in units of the nearest-neighbor hopping amplitude t and an electron density of n = 0.65. The following work describes the architecture, design, and implementation of the platform and how we constructed a correlated quantum materials analysis workflow to demonstrate the viability of this system to produce scientific results.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1416727</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1416727</link>
        <title><![CDATA[A multiphysics coupling framework for exascale simulation of fracture evolution in subsurface energy applications]]></title>
        <pubdate>2024-07-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>David Trebotich</author><author>Randolph R. Settgast</author><author>Terry Ligocki</author><author>William Tobin</author><author>Gregory H. Miller</author><author>Sergi Molins</author><author>Carl I. Steefel</author>
        <description><![CDATA[Predicting the evolution of fractured media is challenging due to coupled thermal, hydrological, chemical and mechanical processes that occur over a broad range of spatial scales, from the microscopic pore scale to field scale. We present a software framework and scientific workflow that couples the pore scale flow and reactive transport simulator Chombo-Crunch with the field scale geomechanics solver in GEOS to simulate fracture evolution in subsurface fluid-rock systems. This new multiphysics coupling capability comprises several novel features. An HDF5 data schema for coupling fracture positions between the two codes is employed and leverages the coarse resolution of the GEOS mechanics solver which limits the size of data coupled, and is, thus, not taxed by data resulting from the high resolution pore scale Chombo-Crunch solver. The coupling framework requires tracking of both before and after coarse nodal positions in GEOS as well as the resolved embedded boundary in Chombo-Crunch. We accomplished this by developing an approach to geometry generation that tracks the fracture interface between the two different methodologies. The GEOS quadrilateral mesh is converted to triangles which are organized into bins and an accessible tree structure; the nodes are then mapped to the Chombo representation using a continuous signed distance function that determines locations inside, on and outside of the fracture boundary. The GEOS positions are retained in memory on the Chombo-Crunch side of the coupling. The time stepping cadence for coupled multiphysics processes of flow, transport, reactions and mechanics is stable and demonstrates temporal reach to experimental time scales. The approach is validated by demonstration of 9 days of simulated time of a core flood experiment with fracture aperture evolution due to invasion of carbonated brine in wellbore-cement and sandstone. We also demonstrate usage of exascale computing resources by simulating a high resolution version of the validation problem on OLCF Frontier.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1360720</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1360720</link>
        <title><![CDATA[Using open-science workflow tools to produce SCEC CyberShake physics-based probabilistic seismic hazard models]]></title>
        <pubdate>2024-05-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Scott Callaghan</author><author>Philip J. Maechling</author><author>Fabio Silva</author><author>Mei-Hui Su</author><author>Kevin R. Milner</author><author>Robert W. Graves</author><author>Kim B. Olsen</author><author>Yifeng Cui</author><author>Karan Vahi</author><author>Albert Kottke</author><author>Christine A. Goulet</author><author>Ewa Deelman</author><author>Thomas H. Jordan</author><author>Yehuda Ben-Zion</author>
        <description><![CDATA[The Statewide (formerly Southern) California Earthquake Center (SCEC) conducts multidisciplinary earthquake system science research that aims to develop predictive models of earthquake processes, and to produce accurate seismic hazard information that can improve societal preparedness and resiliency to earthquake hazards. As part of this program, SCEC has developed the CyberShake platform, which calculates physics-based probabilistic seismic hazard analysis (PSHA) models for regions with high-quality seismic velocity and fault models. The CyberShake platform implements a sophisticated computational workflow that includes over 15 individual codes written by 6 developers. These codes are heterogeneous, ranging from short-running high-throughput serial CPU codes to large, long-running, parallel GPU codes. Additionally, CyberShake simulation campaigns are computationally extensive, typically producing tens of terabytes of meaningful scientific data and metadata over several months of around-the-clock execution on leadership-class supercomputers. To meet the needs of the CyberShake platform, we have developed an extreme-scale workflow stack, including the Pegasus Workflow Management System, HTCondor, Globus, and custom tools. We present this workflow software stack and identify how the CyberShake platform and supporting tools enable us to meet a variety of challenges that come with large-scale simulations, such as automated remote job submission, data management, and verification and validation. This platform enabled us to perform our most recent simulation campaign, CyberShake Study 22.12, from December 2022 to April 2023. During this time, our workflow tools executed approximately 32,000 jobs, and used up to 73% of the Summit system at Oak Ridge Leadership Computing Facility. Our workflow tools managed about 2.5 PB of total temporary and output data, and automatically staged 19 million output files totaling 74 TB back to archival storage on the University of Southern California's Center for Advanced Research Computing systems, including file-based relational data and large binary files to efficiently store millions of simulated seismograms. CyberShake extreme-scale workflows have generated simulation-based probabilistic seismic hazard models that are being used by seismological, engineering, and governmental communities.]]></description>
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