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At least 19 records

$\mathrm{RADICAL}$-Pilot and $\mathrm{PMIx}$/$\mathrm{PRRTE}$: Executing Heterogeneous Workloads at Large Scale on Partitioned $\mathrm{HPC}$ Resources

Execution of heterogeneous workflows on high-performance computing (HPC) platforms present unprecedented resource management and execution coordination challenges for runtime systems. Task heterogeneity increases the complexity of resource and execution management, limiting the scalability and efficiency of workflow execution. Re-source partitioning and distribution of tasks execution over portioned re-sources promises to address those problems but we lack an experimental evaluation of its performance at scale. Here this paper provides a performance evaluation of the Process Management Interface for Exascale (PMIx) and its reference implementation PRRTE on the leadership-class HPC plat-form Summit, when integrated into a pilot-based runtime system called RADICAL-Pilot. We partition resources across multiple PRRTE Distributed Virtual Machine (DVM) environments, responsible for launching tasks via the PMIx interface. We experimentally measure the work-load execution performance in terms of task scheduling/launching rate and distribution of DVM task placement times, DVM startup and termination overheads on the Summit leadership-class HPC platform. Integrated solution with PMIx/PRRTE enables using an abstracted, standardized set of interfaces for orchestrating the launch process, dynamic process management and monitoring capabilities. It extends scaling capabilities allowing to overcome a limitation of other launching mechanisms (e.g., JSM/LSF). Explored different DVM setup configurations provide insights on DVM performance and a layout to leverage it. Our experimental results show that heterogeneous workload of 65,500 tasks on 2048 nodes, and partitioned across 32 DVMs, runs steady with resource utilization not lower than 52%. While having less concurrently executed tasks resource utilization is able to reach up to 85%, based on results of heterogeneous workload of 8200 tasks on 256 nodes and 2 DVMs.

97 MATHEMATICS AND COMPUTING↗

Executing a composite VLIW instruction having a scalar atom that indicates an iteration of execution

A processor core includes a storage device which stores a composite very large instruction word (VLIW) instruction, an instruction unit which obtains the composite VLIW instruction from the storage device and decodes the composite VLIW instruction to determine an operation to perform, and a composite VLIW instruction execution unit which executes the decoded composite VLIW instruction to perform the operation.

97 MATHEMATICS AND COMPUTING↗

Asynchronous Execution of Heterogeneous Tasks in ML-Driven HPC Workflows

Heterogeneous scientific workflows consist of numerous types of tasks that require execution on heterogeneous resources. Asynchronous execution of those tasks is crucial to improve resource utilization, task throughput and reduce workflows' makespan. Therefore, middleware capable of scheduling and executing different task types across heterogeneous resources must enable asynchronous execution of tasks. In this paper, we investigate the requirements and properties of the asynchronous task execution of machine learning (ML)-driven high-performance computing (HPC) workflows. We model the degree of asynchronicity permitted for arbitrary workflows and propose key metrics that can be used to determine qualitative benefits when employing asynchronous execution. Our experiments represent relevant scientific drivers, we perform them at scale on Summit, and we show that the performance enhancements due to asynchronous execution are consistent with our model.

97 MATHEMATICS AND COMPUTING↗

A cross-platform execution engine for the quantum intermediate representation

Hybrid languages like the quantum intermediate representation (QIR) are essential for programming systems that mix quantum and conventional computing models, while execution of these programs is often deferred to a system-specific implementation. Here, we develop the QIR Execution Engine (QIR-EE) for parsing, interpreting, and executing QIR across multiple hardware platforms. QIR-EE uses LLVM to execute hybrid instructions specifying quantum programs and, by design, presents extension points that support customized runtime and hardware environments. We demonstrate an implementation that uses the XACC quantum hardware-accelerator library to dispatch prototypical quantum programs on different commercial quantum platforms and numerical simulators, and we validate execution of QIR-EE on IonQ, Quantinuum, and IBM hardware. Our results highlight the efficiency of hybrid executable architectures for handling mixed instructions, managing mixed data, and integrating with quantum computing frameworks to realize cross-platform execution.

LLVM↗

On-the fly scheduling of execution of dynamic hardware behaviors

Methods for dynamically executing computer code across multiple disparate processing unit architectures are disclosed. During execution of a first portion of computer code on a first processing unit, it is determined that a first dynamic hardware behavior of a plurality of dynamic hardware behaviors will occur at a subsequent point in time, based on a second dynamic hardware behavior that is occurring. The methods include determining to execute code corresponding to the first dynamic hardware behavior on a second processing unit, rather than the first processing unit, and scheduling computer program code corresponding to the first dynamic hardware behavior to execute on the second processing unit rather than the first processing unit. Upon completion of execution of the computer code corresponding to the first dynamic hardware behavior, a remaining portion of the computer code is scheduled to execute on the first processing unit.

97 MATHEMATICS AND COMPUTING↗

Performance Analysis of Scientific Computing Workloads on Trusted Execution Environments

Scientific computing sometimes involves computation on sensitive data. Depending on the data and the execution environment, the HPC (high-performance computing) user or data provider may require confidentiality and/or integrity guarantees. To study the applicability of hardware-based trusted execution environments (TEEs) to enable secure scientific computing, we deeply analyze the performance impact of AMD SEV and Intel SGX for diverse HPC benchmarks including traditional scientific computing, machine learning, graph analytics, and emerging scientific computing workloads. We observe three main findings: 1) SEV requires careful memory placement on large scale NUMA machines (1x -3.4x slowdown without and 1x -1.15x slowdown with NUMA aware placement), 2) virtualization - a prerequisite for SEV - results in performance degradation for workloads with irregular memory accesses and large working sets (1x -4x slowdown compared to native execution for graph applications) and 3) SGX is inappropriate for HPC given its limited secure memory size and inflexible programming model (1.2x -126x slowdown over unsecure execution). Finally, we discuss forthcoming new TEE designs and their potential impact on scientific computing.

97 MATHEMATICS AND COMPUTING↗

Advanced Fuels Campaign Execution Plan

The Advanced Fuels Campaign (AFC) Execution Plan details the strategy, mission, scope, and goals—both near-term and long-term—along with the structure and organization of nuclear fuels and materials research, development, and demonstration (RD&D) activities within the Fuel Cycle Technologies (FCT) program. The FCT program, tasked by the U.S. Department of Energy (DOE), employs a science-based approach to advance fuel technologies. This approach integrates theory, experiments, and multi-scale modeling and simulation (M&S) to develop a predictive understanding of fuel fabrication processes and fuel/cladding performance under irradiation, moving beyond traditional empirical methods. The long-term goals of the AFC are guided by the AFC Strategic Plan and align with the DOE Office of Nuclear Energy (NE) Roadmap [1], which outlines a multi-decade vision for demonstrating and qualifying advanced fuel forms to support diverse fuel cycle options. Near-term goals focus on enhancing accident tolerant fuels (ATF) for Light Water Reactors (LWR), a significant challenge that demands balancing immediate objectives with ongoing progress toward advanced reactor missions. Accelerating the traditional fuel qualification process to meet ATF objectives is another critical challenge. A detailed set of 5-year goals, summarized below, has been developed in line with the overarching science-based fuel development approach: • Advanced LWR Fuel Technologies: By 2027, support the development of advanced LWR fuel technologies with improved performance and enhanced accident tolerance. This includes high burnup (HBu), low enriched uranium (LEU)+, coated cladding, and doped fuel, aimed at complementing industry-led significant LWR uprates and plant refurbishments. • Tristructural Isotropic (TRISO) Fuel: Achieve qualification by 2028 and develop improved designs for emerging markets. • Metal Fuel: Achieve qualification by 2028 and develop improved designs for emerging markets. • Molten Salt Fuel: By 2027, deploy a robust program that enables fuel salt qualification technologies needed to support fuel salt research and development (R&D), focusing on emergent needs to derisk fuel salt production and utilization in advanced reactors. • Long-Term ATF: Develop fuel technologies that enable significant power uprates (~50%) in refurbished or new LWRs while optimizing fissile material utilization and waste disposal. The 5-year milestones in the AFC Execution Plan are contingent on an assumed budget. This Execution Plan will be updated annually to reflect actual funding profiles as budget guidance becomes available, ensuring milestones are adjusted accordingly. In summary, the AFC Execution Plan presents a comprehensive strategy to advance nuclear fuel technologies through a science-based approach, addressing both near-term and long-term goals while adapting to funding realities.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

The executive disruption model of tinnitus distress: Model validation in two independent datasets using factor score regression

This study presents the executive disruption model (EDM) of tinnitus distress and subsequently validates it statistically using two independent datasets (the Construction Dataset: n = 96 and the Validation Dataset: n = 200). The conceptual EDM was first operationalised as a structural causal model (construction phase). Then multiple regression was used to examine the effect of executive functioning on tinnitus-related distress (validation phase), adjusting for the additional contributions of hearing threshold and psychological distress. For both datasets, executive functioning negatively predicted tinnitus distress score by a similar amount (the Construction Dataset: β = −3.50, p = 0.13 and the Validation Dataset: β = −3.71, p = 0.02). Theoretical implications and applications of the EDM are subsequently discussed; these include the predictive nature of executive functioning in the development of distressing tinnitus, and the clinical utility of the EDM.

Clarke, Nathan A.↗

Characterizing the Performance of Executing Many-tasks on Summit

Many scientific workloads are comprised of many tasks, where each task is an independent simulation or analysis of data. The execution of millions of tasks on heterogeneous HPC platforms requires scalable dynamic resource management and multi-level scheduling. RADICAL-Pilot (RP) -- an implementation of the Pilot abstraction, addresses these challenges and serves as an effective runtime system to execute workloads comprised of many tasks. In this paper, we characterize the performance of executing many tasks using RP when interfaced with JSM and PRRTE on Summit: RP is responsible for resource management and task scheduling on acquired resource; JSM or PRRTE enact the placement of launching of scheduled tasks. Our experiments provide lower bounds on the performance of RP when integrated with JSM and PRRTE. Specifically, for workloads comprised of homogeneous single-core, 15 minutes-long tasks we find that: PRRTE scales better than JSM for > O(1000) tasks; PRRTE overheads are negligible; and PRRTE supports optimizations that lower the impact of overheads and enable resource utilization of 63% when executing O(16K), 1-core tasks over 404 compute nodes.

Matteo, Turilli↗

Design and execution of a verification, validation, and uncertainty quantification plan for a numerical model of left ventricular flow after LVAD implantation

Left ventricular assist devices (LVADs) are implantable pumps that act as a life support therapy for patients with severe heart failure. Despite improving the survival rate, LVAD therapy can carry major complications. Particularly, the flow distortion introduced by the LVAD in the left ventricle (LV) may induce thrombus formation. While previous works have used numerical models to study the impact of multiple variables in the intra-LV stagnation regions, a comprehensive validation analysis has never been executed. The main goal of this work is to present a model of the LV-LVAD system and to design and follow a verification, validation and uncertainty quantification (VVUQ) plan based on the ASME V&V40 and V&V20 standards to ensure credible predictions. The experiment used to validate the simulation is the SDSU cardiac simulator, a bench mock-up of the cardiovascular system that allows mimicking multiple operation conditions for the heart-LVAD system. The numerical model is based on Alya, the BSC’s in-house platform for numerical modelling. Alya solves the Navier-Stokes equation with an Arbitrary Lagrangian-Eulerian (ALE) formulation in a deformable ventricle and includes pressure-driven valves, a 0D Windkessel model for the arterial output and a LVAD boundary condition modeled through a dynamic pressure-flow performance curve. The designed VVUQ plan involves: (a) a risk analysis and the associated credibility goals; (b) a verification stage to ensure correctness in the numerical solution procedure; (c) a sensitivity analysis to quantify the impact of the inputs on the four quantities of interest (QoIs) (average aortic root flow $Q^{avg}_{Ao}$, maximum aortic root flow $Q^{max}_{Ao}$, average LVAD flow $Q^{avg}_{VAD}$, and maximum LVAD flow $Q^{max}_{VAD}$; (d) an uncertainty quantification using six validation experiments that include extreme operating conditions. Numerical code verification tests ensured correctness of the solution procedure and numerical calculation verification showed a grid convergence index (GCI)95% <3.3%. The total Sobol indices obtained during the sensitivity analysis demonstrated that the ejection fraction, the heart rate, and the pump performance curve coefficients are the most impactful inputs for the analysed QoIs. The Minkowski norm is used as validation metric for the uncertainty quantification. It shows that the midpoint cases have more accurate results when compared to the extreme cases. The total computational cost of the simulations was above 100 [core-years] executed in around three weeks time span in Marenostrum IV supercomputer. This work details a novel numerical model for the LV-LVAD system, that is supported by the design and execution of a VVUQ plan created following recognised international standards. We present a methodology demonstrating that stringent VVUQ according to ASME standards is feasible but computationally expensive.

59 BASIC BIOLOGICAL SCIENCES↗

Predicting Execution Times for Disk-based and In-Situ Parallel Data Analytics (Final Technical Report)

In recent years, there has been a significant amount of interests in in-situ analytics on simulation programs. For a variety of reasons, it is desirable to be able to predict the execution time of an analytics program. At the same time, frameworks such as MapReduce have become popular for scientific data analytics. This paper focuses on developing performance models for predicting execution time of parallel data analytics, with a special emphasis on in-situ analytics. We take two distinct approach towards performance prediction. We first expand SKOPE (a SKeleton framewOrk for Performance Exploration) with performance models for disk data read, cache performance, and page fault penalty. Second, an analytical performance model is also developed. We have evaluated our performance prediction framework as well as the analytical model on three hardware setups with well-known data mining algorithms implemented in three programming paradigms, MapReduce, MATE (a MapReduce-like parallel system with an alternate API for multi-core environments) and Smart (a MapReduce-like framework for in-situ analytics). Results show that our performance prediction framework along with the incorporated performance models are capable of accurately predicting execution times for parallel scientific analytics on different hardware setups.

97 MATHEMATICS AND COMPUTING↗

2020 Exascale Computing Project Annual Meeting (Executive Summary Report)

The Exascale Computing Project (ECP) delivers specific applications, software products, and outcomes on DOE computing facilities. Integration across these elements for specific hardware technologies for exascale system instantiations is fundamental to ECP success. The outcome of the ECP is the delivery of a capable exascale computing ecosystem to provide breakthrough solutions addressing our most critical challenges in scientific discovery, energy assurance, economic competitiveness, and national security. This outcome is not a matter of ensuring more powerful computing systems. The ECP is designed to create more valuable and rapid insights from a wide variety of applications (“capable”), which requires a much higher level of inherent efficacy in all methods, software tools, and ECP-enabled computing technologies to be acquired by DOE laboratories (“ecosystem”). The ECP annual meeting provides a unique opportunity for the core technical expertise in the United States focused on achieving this next plateau of computational science and computing performance to engage in direct discussions on project execution. Face-to-face gatherings in technical communities like this are common and needed for the exchange of scientific ideas and technical performance. The ECP annual meeting stands apart from other technical conferences and meetings in the computing community as it is uniquely and solely focused on the execution of the ECP and the integration of technical activities leading to the creation of the exascale computing ecosystem for the future. The direct interaction of key critical technical staff, who are leaders in their respective fields, and the resulting give-and-take between software, applications, and hardware and the technical co-design therein, is unique and essential to the effective execution of the ECP. The first annual meeting was held in Knoxville, Tennessee, January 31 – February 2, 2017 and brought together, for the first time, a diverse collection of researchers from 16 DOE national laboratories as well as university computer and computational science researchers to discuss shared problems and joint solutions for the development of a capable exascale computing ecosystem. These interactions resulted in focused technical plans and an energized community centered on advances for ECP. The second annual meeting was held in Knoxville, Tennessee, February 5–9, 2018. It included 643 individual thought leaders and performers in application development, software research and deployment, and hardware research and integrators, all of whom are part of the multifaceted, billion dollar HPC community. This meeting provided a platform to discuss and disseminate numerous examples where researchers with common goals and synergistic solutions came together for the first time to deliver tangible results. Additionally, at the 2018 meeting, ECP researchers had the opportunity to digest all US HPC vendor R&D product roadmaps pointing to exascale – not only to learn how their research can play a role, but, more importantly, to influence those roadmaps to ensure successful delivery on DOE applications that will contribute to (if not solve) problems of national interest in national security, science, energy, and health, as well as growing security threats. The third annual meeting was held in Houston, Texas, January 14–17, 2019. With a 19% increase in the number of registrations (768 people), and the change in location, the third annual meeting was considered the most impactful of the three at the time. The new website provided a better platform for the dissemination of the content, the new venue as a meeting hotel instead of a conference center facilitated interactions and discussions after event hours, and the addition of an award-winning mobile event conference app (Whova) transformed dramatically the attendee experience at the event. This fourth annual meeting was held in Houston, Texas, February 3-7, 2020. This meeting had an increase in the number of attendees for a total of 824 people registered (782 attendees) and included numerous enhancements based on feedback and lessons learned from previous meetings, some of which are listed here: Improved quality of the sessions, their material and the whole program.; Had more industry participation and addition of external collaborators from overseas.; Published the full agenda earlier to better accommodate attendance and travel plans based on schedule.; Centralized all sessions in one venue.; Provided additional hotels and room blocks for the attendees.; Improved communication with the audience (links, material, directions, notifications, etc.) to go paperless.; Enhanced side meeting scheduling, management and user experience.; Made available additional space and tables for impromptu meetings and side discussions.; Improved IT and A/V solutions for speakers. In addition, our final survey captured the following points as opportunities for improvement in future meetings: consider a different meeting location that is more pedestrian friendly, reduce talks during working meals to allow more collaboration and informal time, adapt the agenda to acknowledge attendees from different timezones, consider recording some of the tutorials and/or sessions to share broadly with the HPC community, have a larger poster room, provide additional power strips, and improve the WiFi.

97 MATHEMATICS AND COMPUTING↗

G3P3 Phase 3 Project Execution Plan. Issue 1

The National Solar Thermal Test Facility (NSTTF) at Sandia National Laboratories New Mexico (SNL/NM) developed this Project Execution Plan (PEP) to document its process for executing, monitoring, controlling and closing-out Phase 3 of the Gen 3 Particle Pilot Plant G3P3. This plan serves as a resource for stakeholders who wish to be knowledgeable of project objectives and how they will be accomplished. The plan is intended to be used by the development partners, principal investigator, and the federal project director. Project objectives are derived from the mission needs statement, and an integrated project team assists in development of the PEP. This plan is a living document and will be updated throughout the project to describe current and future processes and procedures. The scope of the PEP covers: Cost, schedule, and scope Project reporting Staffing plan Quality assurance plan Environment, safety, security, and health This document is a tailored approach for the Facilities Management and Operations Center (FMOC) to meet the project management principles of DOE Order 413.3B, Program and Project Management for the Acquisition of Capital Assets , and DOE G 413.3-15, DOE Guide for Project Execution Plans. This document will elaborate on content as knowledge of the project is gained or refined.

14 SOLAR ENERGY↗

Production Agency Quality Assurance Management Execution Strategy [Thesis]

There are multiple federal directives that Los Alamos National Laboratory (LANL) must follow for the proper implementation of quality assurance, strategic planning, and execution of manufacturing and surveillance operations. Recent assessments identified that manufacturing in all processing areas is dynamic due to influencing scope changes, design modifications, funding adjustments, and staff attrition. In response, this project was started to develop a comprehensive PAQ execution strategy to support the success of LANL’s manufacturing mission. This project’s method for developing an updated architecture was to create an integrated, flexible, reliable, and agile strategic process. The execution plan has five deliverables: 1) an integrated schedule view of PAQ work scope, 2) a resource management plan that aligns with the required scope, 3) a metrics monitoring dashboard, 4) a project management plan for sustaining NAP 401.1A implementation, and 5) a risk management plan. The research design is a mixed-method approach focused on data collection for each deliverable. The methodology entails qualitative and quantitative research, metrics monitoring, data analysis, and the creation of a dashboard. The qualitative methods used include literature reviews and interviews. The quantitative methods include resource, financial, schedule and survey data analysis. Preliminary and final results were peer reviewed by subject matter experts both within the ALDWP organization and deployed support.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Enabling Parallel Execution of System-level Simulations in SAM

This report summarizes the recent code updates related to “element ghosting” in SAM to enable the parallel execution of system-level simulations using multiple processors/cores. Unlike typical MOOSE-based applications, for system-level simulations, SAM mostly deals with a collection of discrete small pieces of meshes, and the connection of physics on these meshes are realized by using “connector” types of components/code structures, such as conjugate heat transfer and flow junctions. The required code implementation is to correctly mark the necessary ghost elements for each type of such components/code structures; thus, the lower-level libraries can correctly perform the necessary data transfer between processors (CPUs) when executed in parallel mode. After the code updates, SAM can now run system-level simulations in the parallel mode. The parallel execution capability was then tested with an ABTR input model with 23k DOFs. Significant speedup was demonstrated when the optimal number of CPUs were used in parallel mode. Future systematic studies on parallelization performance using additional test cases covering different physics/scenarios will be needed to provide additional insights into the scalability of SAM parallelization.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Heterogeneous graphics processing unit for scheduling thread groups for execution on variable width SIMD units

A compute unit configured to execute multiple threads in parallel is presented. The compute unit includes one or more single instruction multiple data (SIMD) units and a fetch and decode logic. The SIMD units have differing numbers of arithmetic logic units (ALUs), such that each SIMD unit can execute a different number of threads. The fetch and decode logic is in communication with each of the SIMD units, and is configured to assign the threads to the SIMD units for execution based on such differing numbers of ALUs.

97 MATHEMATICS AND COMPUTING↗

Techniques for recovering from errors when executing software applications on parallel processors

In various embodiments, a software program uses hardware features of a parallel processor to checkpoint a context associated with an execution of a software application on the parallel processor. The software program uses a preemption feature of the parallel processor to cause the parallel processor to stop executing instructions in accordance with the context. The software program then causes the parallel processor to collect state data associated with the context. After generating a checkpoint based on the state data, the software program causes the parallel processor to resume executing instructions in accordance with the context.

Hukerikar, Saurabh↗

An end-to-end workflow for executing a classically bootstrapped variational quantum algorithm on an academic quantum computer

Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.

Clifford↗