High Performance Computing Software Research Articles

Parallel Pattern Compiler for Automatic Global Optimizations

High-performance computing (HPC) systems enable scientific advances through simulation and data processing. The heterogeneity in HPC hardware and software increases the application complexity and reduces its maintainability and productivity. This work proposes a prototype implementation for a parallel pattern-based source-to-source compiler to address these challenges. The prototype limits the complexity of parallelism and heterogeneous architectures to parallel patterns that are optimized towards a given target architecture. By applying high-level optimizations and a mapping between parallel patterns and execution units during compile time, portability between systems is achieved. The compiler can address architectures with shared memory, distributed memory, and accelerator offloading.The approach shows speedups for seven of the nine supported Rodinia benchmarks, reaching speedups of up to twelve times. Porting LULESH to the Parallel Pattern Language (PPL) shows a compression of code size by 65% (3.4 thousand lines of code) through a more concise expression and a higher level of abstraction. The tool’s limitations include dynamic algorithms that are challenging to analyze statically and overheads during the compile time optimization. This paper is an extended version of a previous PMAM publication (Schmitz et al., 2024).

Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community.

The Community Multiscale Air Quality Model (CMAQ) is a local- to hemispheric-scale numerical air quality modeling system developed by the U.S. Environmental Protection Agency (USEPA) and supported by the Community Modeling and Analysis System (CMAS) center. CMAQ is used for regulatory purposes by the USEPA program offices and state and local air agencies and is also widely used by the broader global research community to simulate and understand complex air quality processes and for computational environmental fate and transport and climate and health impact studies. Leveraging state-of-the-science cloud computing resources for high-performance computing (HPC) applications, CMAQ is now available as a fully tested, publicly available technology stack (HPC cluster and software stack) for two major cloud service providers (CSPs). Specifically, CMAQ configurations and supporting materials have been developed for use on their HPC clusters, including extensive online documentation, tutorials and guidelines to scale and optimize air quality simulations using their services. These resources allow modelers to rapidly bring together CMAQ, cloud-hosted datasets, and visualization and evaluation tools on ephemeral clusters that can be deployed quickly and reliably worldwide. Described here are considerations in CMAQ version 5.3.3 cloud use and the supported resources for each CSP, presented through a benchmark application suite that was developed as an example of a typical simulation for testing and verifying components of the modeling system. The outcomes of this effort are to provide findings from performing CMAQ simulations on the cloud using popular vendor-provided resources, to enable the user community to adapt this for their own needs, and to identify specific areas of potential optimization with respect to storage and compute architectures.

VUStruct: a compute pipeline for high throughput and personalized structural biology.

Effective diagnosis and treatment of rare genetic disorders requires the interpretation of a patient's genetic variants of unknown significance (VUSs). Today, clinical decision-making is primarily guided by gene-phenotype association databases and DNA-based scoring methods. Our web-accessible variant analysis pipeline, VUStruct, supplements these established approaches by deeply analyzing the downstream molecular impact of variation in context of 3D protein structure. VUStruct's growing impact is fueled by the co-proliferation of protein 3D structural models, gene sequencing, compute power, and artificial intelligence. Contextualizing VUSs in protein 3D structural models also illuminates longitudinal genomics studies and biochemical bench research focused on VUS, and we created VUStruct for clinicians and researchers alike. We now introduce VUStruct to the broad scientific community as a mature, web-facing, extensible, High Performance Computing (HPC) software pipeline. VUStruct maps missense variants onto automatically selected protein structures and launches a broad range of analyses. These include energy-based assessments of protein folding and stability, pathogenicity prediction through spatial clustering analysis, and machine learning (ML) predictors of binding surface disruptions and nearby post-translational modification sites. The pipeline also considers the entire input set of VUS and identifies genes potentially involved in digenic disease. VUStruct's utility in clinical rare disease genome interpretation has been demonstrated through its analysis of over 175 Undiagnosed Disease Network (UDN) Patient cases. VUStruct-leveraged hypotheses have often informed clinicians in their consideration of additional patient testing, and we report here details from two cases where VUStruct was key to their solution. We also note successes with academic research collaborators, for whom VUStruct has informed research directions in both computational genomics and wet lab studies.

Editorial for the special issue on programming models and system software for High-Performance Computing (HPC) environments

Editorial for the special issue on programming models and system software for High-Performance Computing (HPC) environments

DIFFERENCE BETWEEN OPENHPC AND HTCONDOR CLUSTER SYSTEMS: IN-DEPTH ANALYSIS

The rapidly developing field of high-performance computing (HPC) requires efficient and scalable solutions to manage extensive computing loads. Although they use different approaches and architectures, OpenHPC and HTCondor are well-known platforms that meet these needs. This article thoroughly analyzes OpenHPC and HTCondor to identify their fundamental differences and work paradigms. OpenHPC is a comprehensive modular structure designed to facilitate the deployment, management, and maintenance of HPC clusters, offering a rich set of pre-integrated HPC software components. Conversely, HTCondor specializes in efficiently planning and managing resource-intensive tasks, using a unique partner selection system for dynamic resource allocation based on job requirements and resource availability. By examining aspects such as system architecture, resource management efficiency, scalability, flexibility, and the user ecosystem, this analysis sheds light on the strengths and weaknesses of each structure. The research aims to provide stakeholders in high-performance computing with the knowledge necessary to make informed decisions regarding the selection and implementation of high-performance computing management systems, ultimately aimed at optimizing the use of computing resources and optimizing research and development workflows.

ABCD Analysis of Industries Using High-Performance Computing

Purpose: High-Performance Computing (HPC) is the technique of increasing the overall processing capacity of the system, and achieving high performance in solving large problems in science, engineering, or business. This research paper investigated the High-Performance Computing industry using exploratory research methodology and a listing of ABCD analysis is done. Along with this, the identification of various applications and grand challenges in HPC and various Software Tools required for industry-specific application areas of HPC is done. Design/Methodology/Approach: Qualitative exploratory research methodology to perform Industry Analysis of High-Performance Computing using ABCD Analysis. This research paper is written with the literature review of High-Performance Computing (HPC) industries and various software tools available to use in those industries. Findings/Results: Finding of this research paper is the identification of various grand challenges in HPC industries like healthcare and medical field, engineering and design, aerospace sector, urban planning, and infrastructure, finance, and business. It also lists different HPC software tools that can be used to solve these challenges, along with the findings of ABCD analysis of HPC industry. Originality/Value: Listing of ABCD Analysis (Advantages, Benefits, Constraints, and Disadvantages) of High-Performance Computing industry. Paper Type: Industry Analysis research paper

Overcoming Challenges to Continuous Integration in HPC

Continuous integration (CI) has become a ubiquitous practice in modern software development, with major code hosting services offering free automation on popular platforms. CI offers major benefits, as it enables detecting bugs in code prior to committing changes. While high-performance computing (HPC) research relies heavily on software, HPC machines are not considered "common" platforms. This presents several challenges that hinder the adoption of CI in HPC environments, making it difficult to maintain bug-free HPC projects, and resulting in adverse effects on the research community. In this article, we explore the challenges that impede HPC CI, such as hardware diversity, security, isolation, administrative policies, and non-standard authentication, environments, and job submission mechanisms. We propose several solutions that could enhance the quality of HPC software and the experience of developers. Implementing these solutions would require significant changes at HPC centers, but if these changes are made, it would ultimately enable faster and better science.

P3 problem and Magnolia language: Specializing array computations for emerging architectures

The problem of producing portable high-performance computing (HPC) software that is cheap to develop and maintain is called the P3 (performance, portability, productivity) problem. Good solutions to the P3 problem have been achieved when the performance profiles of the target machines have been similar. The variety of HPC architectures is, however, large and can be expected to grow larger. Software for HPC therefore needs to be highly adaptable, and there is a pressing need to provide developers with tools to produce software that can target machines with vastly different profiles. Multi-dimensional array manipulation constitutes a core component of numerous numerical methods, such as finite difference solvers of Partial Differential Equations (PDEs). The efficiency of these computations is tightly connected to traversing and distributing array data in a hardware-friendly way. The Mathematics of Arrays (MoA) allows for formally reasoning about array computations and enables systematic transformations of array-based programs, e.g., to use data layouts that fit to a specific architecture. This paper presents a programming methodology aimed for tackling the P3 problem in domains that are well-explored using Magnolia, a language designed to embody generic programming. The Magnolia programmer can restrict the semantic properties of abstract generic types and operations by defining so-called axioms. Axioms can be used to produce tests for concrete implementations of specifications, for formal verification, or to perform semantics-preserving program transformations. We leverage Magnolia's semantic specification facilities to extend the Magnolia compiler with a term rewriting system. We implement MoA's transformation rules in Magnolia, and demonstrate through a case study on a finite difference solver of PDEs how our rewriting system allows exploring the space of possible optimizations.

Accelerating HPC With Quantum Computing: It Is a Software Challenge Too

With quantum computing (QC) maturing, high-performance computing (HPC) centers are already preparing to host early-phase production versions of such systems. Unlike their experimental predecessors in physics laboratories, with a very small and dedicated user community, this next generation of systems needs to serve a wider user community and must work in concert with existing HPC systems and software stacks. This article describes our vision for an integrated ecosystem that combines existing HPC and evolving quantum software stacks into a single system to enable a common and continuous user experience. This integration comes with several major challenges as quantum systems pose significantly different requirements including increased need for compilation at run time, long optimization times, statistical evaluations of results, and the need to work with few centralized resources. To overcome these challenges, new scheduling approaches on the HPC side and new programming approaches on the QC side are required.

Always improving performance

Jack J. Dongarra is the recipient of the 2021 ACM A.M. Turing Award for his pioneering contributions to numerical algorithms and libraries that enabled high-performance computational software to keep pace with exponential hardware improvements for over four decades.

Accelerating HDF5 I/O for Exascale Using DAOS

The Hierarchical Data Format 5 (HDF5) has long been defined as one of the most prominent data models, binary file formats and I/O libraries for storing and managing scientific data. Introduced in the late 90s when POSIX I/O was the standard, the library has since then been continuously improved to respond and adapt to the ever-growing demands of high-performance computing (HPC) software and hardware. Given the limitations of POSIX I/O and with the emergence of new technologies such as object stores, non-volatile memory, and SSDs, the need for an interface that can efficiently store and access data at scale through new paradigms has become more and more pressing. The Distributed Asynchronous Object Storage (DAOS) file system is an emerging file system that aims at responding to those demands by taking disk-based storage out of the loop. We present in this article the research efforts that have been taking place to prepare the HDF5 library for Exascale using DAOS. By enabling and defining a new storage file format, we focus on the benefits that it delivers to the applications in terms of features and performance.

Containers and orchestration of numerical ocean model for computational reproducibility and portability in public and private clouds: Application of ROMS 3.6

Various numerical models have been used to understand and predict ocean dynamics. For this reason, many information technology (IT) resources are required for high-resolution global ocean modeling. The development of cloud-computing technologies has enabled earth scientists to easily use numerical ocean models that require high-performance computing (HPC) and message-passing interface (MPI) software in private and public clouds. Although it is easier today to use computing resources than it was in the past, computational reproducibility and portability in diverse IT environments remain crucial issues. This study proposes a model execution architecture for computational reproducibility, portability, and agility based on container-based virtualization and orchestration technologies. We implement a containerized regional ocean-modeling system (ROMS), an MPI-based numerical ocean model that exists in various public or private cloud environments (e.g., personal computers and multiple-node servers). The preparation time for model setup is greatly reduced using our container-based HPC architecture. Containerization of ROMS is tested for its support of the portability of numerical modeling in a wide range of public-cloud environments. When leveraging an abstraction layer of complex and diverse infrastructure environments, we can run the ocean model more easily while obtaining computational reproducibility using a shareable deployment code. This advancement can be used to guide the containerization of various numerical models and to run them in parallel in public and private cloud-computing environments.

(Invited) Details Matter: Applying Computed Tomography and X-Ray Imaging to Advancing PEFCs

Polymer electrolyte fuel cells (PEFCs) continue to reach new levels of performance and are increasingly being used in passenger vehicles, heavy-duty vehicles, and stationary power supplies. In concert with these advancements, the tools used to identify new opportunities to further advance power density and durability as well as reduce costs must increasingly scrutinize finer details. Such details include the role of heterogeneities in bulk layers, unintentional or intentional gradients in material properties stemming from the manufacturing and film casting processes, the impact of interfacial zones, spatial distributions of the current generation and material degradation, as well as the impact of the three-dimensional (3D) micro/nanostructure. This talk will share recent advances in the application of micro-scale and nano-scale computed tomography (CT), including X-ray and electron CT, and will outline the state-of-art capabilities. Advanced methodologies that will be introduced include <50 nm resolution X-ray nano-CT and plasma focused ion beam-scanning electron microscopy (pFIB-SEM) for high-throughput, large field of view 3D imaging at <5 nm resolution. The talk will highlight recent advances in multi-modal/multi-scale imaging, CT-informed and image-based modeling, and operando X-ray imaging. Some examples include multi-modal X-ray CT imaging of catalyst layers to understand how ink properties and casting affects the structure, including the reorganization of ionomer during casting and drying. Regarding degradation analysis, the use of nano-CT for imaging the 3D redistribution of Pt in MEAs and pore structure evolution with accelerated stress tests will be highlighted. The talk will emphasize the various levels of using CT data to inform simulations, including recent applications of high-performance computing and open-source software to model coupled transport-reaction in large image domains resolving both the true pore and ionomer 3D distributions as well as the use of electron tomography images to simulate nm-scale gas and ion transport in single Pt/C aggregates. Finally, there will be a discussion of emerging operando X-ray imaging methods at sub-micrometer resolution for understanding the temporal evolution of materials, including catalyst layers and membranes.

Supercomputing Pipelines Search for Therapeutics Against COVID-19

The urgent search for drugs to combat SARS-CoV-2 has included the use of supercomputers. The use of general-purpose graphical processing units (GPUs), massive parallelism, and new software for high-performance computing (HPC) has allowed researchers to search the vast chemical space of potential drugs faster than ever before. We developed a new drug discovery pipeline using the Summit supercomputer at Oak Ridge National Laboratory to help pioneer this effort, with new platforms that incorporate GPU-accelerated simulation and allow for the virtual screening of billions of potential drug compounds in days compared to weeks or months for their ability to inhibit SARS-COV-2 proteins. This effort will accelerate the process of developing drugs to combat the current COVID-19 pandemic and other diseases.

Transmission and distribution co‐simulation: a review and propositions

With the growing trend of emerging new technologies in distribution networks, such as wind turbines, solar panels, electric vehicles, and distributed generations, the passive distribution systems may become ‘active’ which requires more study in the area of integrated transmission and distribution systems (ITDSs) and corresponding bilateral interactions. To solve this problem, most of the studies connect distinct simulators to create a novel co-simulation framework for ITDS. In this study, the authors present a literature survey of existing ITDS co-simulation frameworks along co-optimisation in ITDS. These frameworks are categorised on multiple characteristics, such as simulation tools, synchronisation methods, and research topics. Furthermore, they propose a software platform that is focused on the integrated generation, transmission, distribution, and customer systems (IGTDCSs). The proposed framework also comprises several technological dimensions such as stochastic optimisation, high-performance computing, and high-level design software architecture for planning integrated and flexible power networks and optimising their technological trajectories and operational functioning considering uncertainties. By developing a prototype informed with software engineering and complex system design approaches, they will demonstrate the relevance of a unified vision of IGTDCS simulation in a minute-by-minute horizon, a vision that may later benefit electromagnetic transient simulation or stability co-simulation tools, in horizons from the microsecond to the second.

Parametric study of S-CO2 cycles for the DEMO fusion reactor

Effective use of fusion energy goes hand in hand with effective energy recovery of the generated heat. One of the most promising working medium suitable for this purpose is supercritical carbon dioxide (S-CO2) due to its thermal properties. Six S-CO2 cycle layouts were optimized for the effective use of the heat generated in the DEMO fusion reactor. The main investigated parameter is the outlet temperature from the blanket and the first wall. The temperature range is 350–550 °C. The second parameter is the compressor intake temperature at two temperature levels 25 and 31 °C. The analysis of thermodynamic cycles was performed using new custom-built high-performance computing software. The parametric study of each layout shows the suitability of the given cycle for heat recovery and its sensitivity to the main heat source temperature.

The MVAPICH project: Transforming research into high-performance MPI library for HPC community

High-Performance Computing (HPC) research, from hardware and software to the end applications, provides remarkable computing power to help scientists solve complex problems in science, engineering, or even daily business. Over the last decades, Message Passing Interface (MPI) libraries have been powering numerous scientific applications, such as weather forecasting, earthquake simulations, physic, and chemical simulations, to conduct ever-large-scale experiments. However, it is always challenging to practice HPC research. In this article, we use the well-established MVAPICH project to discuss how researchers can transform the HPC research into a high-performance MPI library and translate it to the HPC community.

Scalability of high-performance PDE solvers

Performance tests and analyses are critical to effective high-performance computing software development and are central components in the design and implementation of computational algorithms for achieving faster simulations on existing and future computing architectures for large-scale application problems. In this article, we explore performance and space-time trade-offs for important compute-intensive kernels of large-scale numerical solvers for partial differential equations (PDEs) that govern a wide range of physical applications. We consider a sequence of PDE-motivated bake-off problems designed to establish best practices for efficient high-order simulations across a variety of codes and platforms. We measure peak performance (degrees of freedom per second) on a fixed number of nodes and identify effective code optimization strategies for each architecture. In addition to peak performance, we identify the minimum time to solution at 80% parallel efficiency. The performance analysis is based on spectral and p-type finite elements but is equally applicable to a broad spectrum of numerical PDE discretizations, including finite difference, finite volume, and h-type finite elements.

GeoDRIVE - a high performance computing flexible platform for seismic applications

GeoDRIVE, a high performance computing (HPC) software framework tailored to massive seismic applications and super-computers is presented. The paper discusses the flexibility and modularity of the application along with optimized HPC features. GeoDRIVE’s versatile design, associated to exascale computing capabilities, unlocks new classes of applications that significantly improve geoscientists’ abilities to understand, locate and characterize challenging targets in complex settings. As a result, uncertainties in subsurface models are reduced both quantitatively and qualitatively, along with reduced drilling risks and improved prospect generation.

Optimizing the computational simulations of air-flammable gas explosions using HPC and Ansys software

The purpose of this scientific work is to improve computer simulations of flammable air-gas explosions with HPC systems. Computational Fluid Dynamics (CFD) is increasingly used for obtaining variable values of fluid flow areas, respectively for the manner in which fluids react with limited surfaces. For a separate analysis of liquids and gases is used CFD, and for more realistic results is used the multi-phase method performed by ANSYS Fluent, which improves the calculation scalability and power. For increasing the processing speed for complex analyses, with multiple geometries and fine meshes, ANSYS provides the user HPC (High Performance Computing) tools applicable for structural, thermal, electromagnetic, fluid dynamics and explicit dynamics solvers. HPC configuration is characterized by a good scalability, having the capacity for future extension of cores or processors. For decreasing the computational simulation time for explosions, the proposed solution consists in running complex simulations on the servers of a HPC cluster. In this way is provided the possibility for a parallel or distributed running on one or several calculation systems. Using HPC along ANSYS applications may be activated by GPU acceleration, while other applications are limited to processing using CPUs. INSEMEX develops technical investigations of explosions and fires occurred in the industrial or civilian field, in compliance with Government Decision 1461/2006, based on the verification of scenarios using virtual computational simulations.