Polynomial Preconditioned GMRES in Trilinos: Practical Considerations for High Performance Computing.
Abstract not provided.
Loe, Jennifer Ann↗
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Performance Evaluation of a Two-Dimensional Flood Model on Heterogeneous High-Performance Computing Architectures
This paper describes the implementation of a two-dimensional hydrodynamic flood model with two different numerical schemes on heterogeneous high-performance computing architectures. Both schemes were able to solve the nonlinear hyperbolic shallow water equations using an explicit upwind first-order approach on finite differences and finite volumes, respectively, and were conducted using MPI and CUDA. Four different test cases were simulated on the Summit supercomputer at Oak Ridge National Laboratory. Both numerical schemes scaled up to 128 nodes (768 GPUs) with a maximum 98.2x speedup of over 1 GPU. The lowest run time for the 10 day Hurricane Harvey event simulation at 5 meter resolution (272 million grid cells) was 50 minutes. GPUDirect communication proved to be more convenient than the standard communication strategy. Both strong and weak scaling are shown.
Sharif, Md Bulbul↗
Mass storage: The key to success in high performance computing
There are numerous High Performance Computing & Communications Initiatives in the world today. All are determined to help solve some 'Grand Challenges' type of problem, but each appears to be dominated by the pursuit of higher and higher levels of CPU performance and interconnection bandwidth as the approach to success, without any regard to the impact of Mass Storage. My colleagues and I at Data Storage Technologies believe that all will have their performance against their goals ultimately measured by their ability to efficiently store and retrieve the 'deluge of data' created by end-users who will be using these systems to solve Scientific Grand Challenges problems, and that the issue of Mass Storage will become then the determinant of success or failure in achieving each projects goals. In today's world of High Performance Computing and Communications (HPCC), the critical path to success in solving problems can only be traveled by designing and implementing Mass Storage Systems capable of storing and manipulating the truly 'massive' amounts of data associated with solving these challenges. Within my presentation I will explore this critical issue and hypothesize solutions to this problem.
Lee, Richard R.↗
PLEXUS: A Pattern-Oriented Runtime System Architecture for Resilient Extreme-Scale High-Performance Computing Systems
For high-performance computing (HPC) system designers and users, meeting the myriad challenges of next-generation exascale supercomputing systems requires rethinking their approach to application and system software design. Among these challenges, providing resiliency and stability to the scientific applications in the presence of high fault rates requires new approaches to software architecture and design. As HPC systems become increasingly complex, they require intricate solutions for detection and mitigation for various modes of faults and errors that occur in these large-scale systems, as well as solutions for failure recovery. These resiliency solutions often interact with and affect other system properties, including application scalability, power and energy efficiency. Therefore, resilience solutions for HPC systems must be thoughtfully engineered and deployed.In previous work, we developed the concept of resilience design patterns, which consist of templated solutions based on well-established techniques for detection, mitigation and recovery. In this paper, we use these patterns as the foundation to propose new approaches to designing runtime systems for HPC systems. The instantiation of these patterns within a runtime system enables flexible and adaptable end-to-end resiliency solutions for HPC environments. The paper describes the architecture of the runtime system, named Plexus, and the strategies for dynamically composing and adapting pattern instances under runtime control. This runtime-based approach enables actively balancing the cost-benefit trade-off between performance overhead and protection coverage of the resilience solutions. Based on a prototype implementation of PLEXUS, we demonstrate the resiliency and performance gains achieved by the pattern-based runtime system for a parallel linear solver application.
Hukerikar, Saurabh↗
Defense strategies and expected capacity of high performance computing infrastructures
We consider high performance computing infrastructures consisting of multiple sites connected over a wide-area network. These sites house heterogeneous computing systems, network elements and local-area connections, and the wide-area network plays a critical, asymmetric role of providing the vital connectivity between them. We model this infrastructure as a recursive system of systems, wherein the basic systems at the finest levels are composed of discrete cyber and physical components. These components may be disabled by cyber and physical disruptions, and their reinforcements protect against them albeit at a cost. We utilize results from a game formulation based on (i) characterizing the disruptions between systems at each level of recursion using aggregate failure correlation functions, and within basic systems using the multiplier functions, and (ii) using utility functions expressed in terms of the number of basic system components attacked and reinforced. At Nash Equilibrium, we derive expressions for the expected capacity of the infrastructure given by the number of computing nodes that are operational and connected to the network.
Rao, Nageswara↗
Converged Computing: A Best of Both Worlds of High-Performance Computing and Cloud
Collaboration between Cloud and High Performance Computing (HPC) communities has accelerated in the last half decade. A common goal to run batch workloads combined with a desire for reproducibility, automation, and optimization has led to successful projects that range from container technologies to workload management and security. This span of current and future work defines a novel “Converged Computing” paradigm that aims to combine the best of both worlds, both from a technological and cultural standpoint. Furthermore, in this Special Issue, we review common themes in the space, showcasing current work and encouraging a continued effort toward innovative ideas that will enable the next generations of scientific discovery.
97 MATHEMATICS AND COMPUTING↗
NASA SERC 1990 Symposium on VLSI Design
This document contains papers presented at the first annual NASA Symposium on VLSI Design. NASA's involvement in this event demonstrates a need for research and development in high performance computing. High performance computing addresses problems faced by the scientific and industrial communities. High performance computing is needed in: (1) real-time manipulation of large data sets; (2) advanced systems control of spacecraft; (3) digital data transmission, error correction, and image compression; and (4) expert system control of spacecraft. Clearly, a valuable technology in meeting these needs is Very Large Scale Integration (VLSI). This conference addresses the following issues in VLSI design: (1) system architectures; (2) electronics; (3) algorithms; and (4) CAD tools.
Maki, Gary K.↗
The High Performance Computing Initiative
The paper discusses NASA High Performance Computing Initiative (HPCI), an essential component of the Federal High Performance Computing Program. The HPCI program is designed to provide a thousandfold increase in computing performance, and apply the technologies to NASA 'Grand Challenges'. The Grand Challenges chosen include integrated multidisciplinary simulations and design optimizations of aerospace vehicles throughout the mission profiles; the multidisciplinary modeling and data analysis of the earth and space science physical phenomena; and the spaceborne control of automated systems, handling, and analysis of sensor data and real-time response to sensor stimuli.
Holcomb, Lee B.↗
Reaching for the Right Portfolio Strategy: Multicriteria R&D Planning Problems and High-Performance Computing
An application of high-performance computing tothe analysis and solution of an R&D portfolio strategy problem is described. A study conducted ten years earlier is revisited and used as a benchmark.
R and D R & D research and development high perfor↗
High performance computing and communications program
A review of the High Performance Computing and Communications (HPCC) program is provided in vugraph format. The goals and objectives of this federal program are as follows: extend U.S. leadership in high performance computing and computer communications; disseminate the technologies to speed innovation and to serve national goals; and spur gains in industrial competitiveness by making high performance computing integral to design and production.
Holcomb, Lee↗
Data Center Immersion Cooling: A Case Study and Summary of High-Performance Computing Cooling Technologies
The future of High Performance Computing (HPC) is carved out for all high-performance facilities. As computer processing increases exponentially, especially with the demand for AI training, more speed will equate to more computing density and ultimately more power draw and resource demand. Sandia National Laboratories has not seen or known of anything that would lead us to think this might change in the next 5 to 10 years.
97 MATHEMATICS AND COMPUTING↗
Demonstration of Cost-Effective, High-Performance Computing at Performance and Reliability Levels Equivalent to a 1994 Vector Supercomputer
The Affordable High Performance Computing (AHPC) project demonstrated that high-performance computing based on a distributed network of computer workstations is a cost-effective alternative to vector supercomputers for running CPU and memory intensive design and analysis tools. The AHPC project created an integrated system called a Network Supercomputer. By connecting computer work-stations through a network and utilizing the workstations when they are idle, the resulting distributed-workstation environment has the same performance and reliability levels as the Cray C90 vector Supercomputer at less than 25 percent of the C90 cost. In fact, the cost comparison between a Cray C90 Supercomputer and Sun workstations showed that the number of distributed networked workstations equivalent to a C90 costs approximately 8 percent of the C90.
Babrauckas, Theresa↗
DRAS: Deep Reinforcement Learning for Cluster Scheduling in High Performance Computing
Cluster schedulers are crucial in high-performance computing (HPC). They determine when and which user jobs should be allocated to available system resources. Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads. However, the increasing complexity of computing systems and the highly dynamic nature of application workloads have placed tremendous burden on manually designed and tuned scheduling heuristics. More aggressive optimization and automation are needed for cluster scheduling in HPC. In this work, we present an automated HPC scheduling agent named DRAS (Deep Reinforcement Agent for Scheduling) by leveraging deep reinforcement learning. DRAS is built on a hierarchical neural network incorporating special HPC scheduling features such as resource reservation and backfilling. An efficient training strategy is presented to enable DRAS to rapidly learn the target environment. Once being provided a specific scheduling objective given by the system manager, DRAS automatically learns to improve its policy through interaction with the scheduling environment and dynamically adjusts its policy as workload changes. We implement DRAS into a HPC scheduling platform called CQGym. CQGym provides a common platform allowing users to flexibly evaluate DRAS and other scheduling methods such as heuristic and optimization methods. Furthermore, the experiments using CQGym with different production workloads demonstrate that DRAS outperforms the existing heuristic and optimization approaches by up to 50%.
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Quantifying the Impact of Advanced Web Platforms on High Performance Computing Usage
The deployment of Science Gateways for High Performance Computing (HPC) systems can alter long-accepted usage patterns on supercomputing systems in positive ways as an ever-increasing number of users migrate their workflows to HPC systems. Idaho National Laboratory (INL) has deployed two separate advanced web platforms, Open OnDemand and NICE DCV, for integration with HPC resources to improve web accessibility for HPC users. Researchers conducted a multi-year study on how HPC usage pat- terns changed in the presence of these platforms. This work reports the results of that study and quantifies the observed impacts, including adoption by visualization and Jupyter Notebook/Lab users, decreased job submission friction, rapid uptake of HPC by Windows users, and increased overall system utilization. The most significant impacts were observed from the deployment of Open OnDemand, and this work also identifies some best practices for Open OnDemand deployment for HPC datacenters.
97 MATHEMATICS AND COMPUTING↗
Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation
The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Furthermore, our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning.
47 OTHER INSTRUMENTATION↗
The role of quantum computing in advancing scientific high-performance computing: A perspective from the ADAC institute
Quantum computing (QC) has gained significant attention over the past two decades due to its potential for speeding up classically demanding tasks. This transition from an academic focus to a thriving commercial sector is reflected in substantial global investments. While advancements in qubit counts and functionalities continue at a rapid pace, current quantum systems still lack the scalability for practical applications, facing challenges such as too high error rates and limited coherence times. Here, this perspective paper examines the relationship between QC and high-performance computing (HPC), highlighting their complementary roles in enhancing computational efficiency. It is widely acknowledged that even fully error-corrected QC will not be suited for all computational tasks. Rather, future compute infrastructures are anticipated to employ quantum acceleration within hybrid systems that integrate HPC and QC. While QC can enhance classical computing, traditional HPC remains essential for maximizing quantum acceleration. This integration is a priority for supercomputing centers and companies, sparking innovation to address the challenges of merging these technologies. The novelty of this work lies in its unique perspective, reflecting the collective insights of the Accelerated Data Analytics and Computing (ADAC) Institute, a global consortium of over 20 leading HPC centers. Recognizing the growing importance of QC, ADAC established a Quantum Computing Working Group in 2023 to foster collaboration and knowledge-sharing among its members. This paper synthesizes insights from the group’s collaborative efforts and incorporates findings from a member survey that captures shared experiences, ongoing projects, and strategic directions. By outlining the current landscape and challenges of QC integration into HPC ecosystems, this work offers HPC specialists practical and forward-looking guidance on the opportunities and implications of QC in computationally intensive endeavors.
Accelerated Data Analytics and↗
Domain-specific virtual processors as a portable programming and execution model for parallel computational workloads on modern heterogeneous high-performance computing architectures
We advocate domain-specific virtual processors (DSVP) as a portability layer for expressing and executing domain-specific computational workloads on modern heterogeneous HPC architectures, with applications in quantum chemistry. Specifically, in this article we extend, generalize and better formalize the concept of a domain-specific virtual processor as applied to scientific high-performance computing. In particular, we introduce a system-wide recursive (hierarchical) hardware encapsulation mechanism into the DSVP architecture and specify a concrete microarchitectural design of an abstract DSVP from which specialized DSVP implementations can be derived for specific scientific domains. Furthermore, we demonstrate, an example of a domain-specific virtual processor specialized to numerical tensor algebra workloads, which is implemented in the ExaTENSOR library developed by the author with a primary focus on the quantum many-body computational workloads on large-scale GPU-accelerated HPC platforms.
97 MATHEMATICS AND COMPUTING↗
Development of a High Performance Computing Accounts Administration Web Application
INL's High Performance Computing (HPC) accounts administration tool is a web application that that allows an administrator to change user information, create an HPC account, create or modify an LDAP group or project, and modify the members in a group. An administrator can search for a user, obtain the relevant information about the user, and update their account information. To create or modify an LDAP group or project, the administrator can filter a list of groups or projects according to the name, owner, or administrator that created the project or group, choose a name accordingly, and specify the group or project owner.
97 MATHEMATICS AND COMPUTING↗