Results for “computer system performance” — Page 24

DOE OSTI2020

The Numerical Computation of Clear-Sky Transmittance and Reflectance of Solar Radiation for the Modeling of PV System Performance

Modeling of photovoltaics (PV) system performance requires numerical computation of plane-of-array (POA) irradiances that are influenced by atmospheric absorption and scattering. Clear-sky transmittance and reflectance of diffuse irradiance are essential for the understanding of solar radiation related to multiple reflection between land surface and clouds. This study analytically solves the radiative transfer equation for clear-sky conditions and numerically computes the transmittance and reflectance of diffuse irradiance as functions of the optical thickness of the atmosphere. The computation will be implemented in all-sky radiative transfer models to improve their computational efficiency.

14 SOLAR ENERGY↗

DOE OSTI2013

An Analysis Framework for Investigating the Trade-offs Between System Performance and Energy Consumption in a Heterogeneous Computing Environment

Rising costs of energy consumption and an ongoing effort for increases in computing performance are leading to a significant need for energy-efficient computing. Before systems such as supercomputers, servers, and datacenters can begin operating in an energy-efficient manner, the energy consumption and performance characteristics of the system must be analyzed. In this paper, we provide an analysis framework that will allow a system administrator to investigate the tradeoffs between system energy consumption and utility earned by a system (as a measure of system performance). We model these trade-offs as a bi-objective resource allocation problem. We use a popular multi-objective genetic algorithm to construct Pareto fronts to illustrate how different resource allocations can cause a system to consume significantly different amounts of energy and earn different amounts of utility. We demonstrate our analysis framework using real data collected from online benchmarks, and further provide a method to create larger data sets that exhibit similar heterogeneity characteristics to real data sets. This analysis framework can provide system administrators with insight to make intelligent scheduling decisions based on the energy and utility needs of their systems.

Friese, Ryan↗

DOE OSTI2021

It’s Time to Talk About HPC Storage: Perspectives on the Past and Future

High-performance computing (HPC) storage systems are a key component of the success of HPC to date. Recently, we have seen major developments in storage-related technologies, as well as changes to how HPC platforms are used, especially in relation to artificial intelligence and experimental data analysis workloads. Additionally, these developments merit a revisit of HPC storage system architectural designs. In this article, we discuss the drivers, identify key challenges to status quo posed by these developments, and discuss directions future research might take to unlock the potential of new technologies for the breadth of HPC applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Simulation of Wave Interactions with Magnetohydrodynamics (SWIM) SciDAC (Scientific Discovery through Advanced Computing) Project

The Center for Simulation of Wave Interactions with Magnetohydrodynamics (SWIM) was begun in 2005 with as a partnership between the DOE Office of Fusion Energy (FES) and the Office of Advanced Supercomputing Research (ASCR) to serve as a prototype for a comprehensive Fusion Simulation Project (FSP). The purpose was to begin the process of multi-physics, multi-scale coupling, at a reduced scope from a full FSP, while addressing problems of programmatic importance, and to develop approaches to large-scale code integration as required for the full FSP. The scientific objectives of SWIM were to improve our understanding of interactions that both RF wave and particle sources have on extended-MHD phenomena, to improve our capability for predicting and optimizing the performance of burning plasmas, to develop an integrated computational system for treating multi-physics phenomena with the required flexibility and extensibility to serve as a prototype for the Fusion Simulation Project, to address mathematics issues related to the multi-scale, coupled physics of RF waves and extended MHD, and to optimize the integrated system on high performance computers. The SWIM project ended in 2012 and the FSP, for which SWIM was to serve as a prototype, was not begun.

Batchelor, Donald↗

DOE OSTI2013

Opcode counting for performance measurement

Methods, systems and computer program products are disclosed for measuring a performance of a program running on a processing unit of a processing system. In one embodiment, the method comprises informing a logic unit of each instruction in the program that is executed by the processing unit, assigning a weight to each instruction, assigning the instructions to a plurality of groups, and analyzing the plurality of groups to measure one or more metrics. In one embodiment, each instruction includes an operating code portion, and the assigning includes assigning the instructions to the groups based on the operating code portions of the instructions. In an embodiment, each type of instruction is assigned to a respective one of the plurality of groups. These groups may be combined into a plurality of sets of the groups.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Opcode counting for performance measurement

Methods, systems and computer program products are disclosed for measuring a performance of a program running on a processing unit of a processing system. In one embodiment, the method comprises informing a logic unit of each instruction in the program that is executed by the processing unit, assigning a weight to each instruction, assigning the instructions to a plurality of groups, and analyzing the plurality of groups to measure one or more metrics. In one embodiment, each instruction includes an operating code portion, and the assigning includes assigning the instructions to the groups based on the operating code portions of the instructions. In an embodiment, each type of instruction is assigned to a respective one of the plurality of groups. These groups may be combined into a plurality of sets of the groups.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Opcode counting for performance measurement

Methods, systems and computer program products are disclosed for measuring a performance of a program running on a processing unit of a processing system. In one embodiment, the method comprises informing a logic unit of each instruction in the program that is executed by the processing unit, assigning a weight to each instruction, assigning the instructions to a plurality of groups, and analyzing the plurality of groups to measure one or more metrics. In one embodiment, each instruction includes an operating code portion, and the assigning includes assigning the instructions to the groups based on the operating code portions of the instructions. In an embodiment, each type of instruction is assigned to a respective one of the plurality of groups. These groups may be combined into a plurality of sets of the groups.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Opcode counting for performance measurement

Methods, systems and computer program products are disclosed for measuring a performance of a program running on a processing unit of a processing system. In one embodiment, the method comprises informing a logic unit of each instruction in the program that is executed by the processing unit, assigning a weight to each instruction, assigning the instructions to a plurality of groups, and analyzing the plurality of groups to measure one or more metrics. In one embodiment, each instruction includes an operating code portion, and the assigning includes assigning the instructions to the groups based on the operating code portions of the instructions. In an embodiment, each type of instruction is assigned to a respective one of the plurality of groups. These groups may be combined into a plurality of sets of the groups.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Opcode counting for performance measurement

Methods, systems and computer program products are disclosed for measuring a performance of a program running on a processing unit of a processing system. In one embodiment, the method comprises informing a logic unit of each instruction in the program that is executed by the processing unit, assigning a weight to each instruction, assigning the instructions to a plurality of groups, and analyzing the plurality of groups to measure one or more metrics. In one embodiment, each instruction includes an operating code portion, and the assigning includes assigning the instructions to the groups based on the operating code portions of the instructions. In an embodiment, each type of instruction is assigned to a respective one of the plurality of groups. These groups may be combined into a plurality of sets of the groups.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Structural Simluation Toolkit (SST) v.11.0

The SST provides a parallel framework to perform system simulation of computer architectures to determine their performance and power consumption. Additionally, the SST contains basic models of a computer processor, and interconnect and can connect to an external memory simulator (DRAMSim II). The SST framework provides a simple interface by which other computer simulation models can be combined under a common parallel discrete event-based simulation environment. This allows design exploration of future architectures, analysis of how current computer programs will function on future architectures. The SST provides a parallel discrete event simulation framework, including partitioning and object distribution over MPI. It also provides a mechanism by which components can report their power consumption for analysis.

Rodrigues, ArunF.↗

DOE OSTI2021

FY2021 Status Report on the Computing Systems for the Yucca Mountain Project TSPA-LA Models and Testing of Selected Process Models

Sandia National Laboratories continued evaluation of the total system performance assessment (TSPA) for License Application (LA) computing systems for the previously considered Yucca Mountain Project (YMP). This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. The FY21 task included continued operation of the cluster; maintenance of the TSPA-LA models (with GoldSim 9.60.300); continued assessment of the status of the Infiltration Model; (a process model that feeds the TSP -LA) and preliminary assessments of the Unsaturated Zone Flow Model and the Saturated Zone Flow and Transport Model Abstraction (process models that feed the TSPA-LA). The 2014 cluster and supporting software systems are currently fully operational to support TSPA-LA type analyses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Energy-efficient scientific computing using chemical reservoirs

The rapid growth of computing demands driven by scientific computing, data analytics, and artificial intelligence (AI) advancements has exposed the limitations of traditional digital processing systems. These systems are nearing physical energy barriers, making significant gains in energy efficiency increasingly unattainable. As we advance toward post-exascale computing, disruptive approaches are critical to overcoming these limitations. Among emerging analog solutions, biochemical computing offers a transformative path for achieving orders-of-magnitude improvements in energy efficiency. By leveraging the natural optimization capabilities of chemical reaction networks (CRNs), biochemical systems have the potential to meet high-performance computing needs through natural scalability. However, numerous challenges remain, including theoretical limitations in mapping computational problems to CRNs and practical barriers in implementing biochemical computing devices. In this paper, we present a framework for chemical computation using biochemical systems and introduce key components of our approach for energy-efficient scientific computing. We showcase the feasibility of this framework by solving a system of ordinary differential equations by emulating a chemical reservoir device, demonstrating its potential for addressing modern computing challenges. This work lays a foundational step toward harnessing the computational power of chemistry to design energy-efficient, scalable, high-performance next-generation computing systems.

Johnson, Connah G. M. [Pacific Northwest National ↗

DOE OSTI2017

Faster than Real-Time Dynamic Simulation for Large-Size Power System with Detailed Dynamic Models using High-Performance Computing Platform

This paper presents a faster-than-real-time dynamic simulation software package that is designed for large-size power system dynamic simulation. It was developed on the GridPACKTM high-performance computing (HPC) framework. The key features of the developed software package include (1) faster-than-real-time dynamic simulation for a WECC system (17,000 buses) with different types of detailed generator, controller, and relay dynamic models, (2) a decoupled parallel dynamic simulation algorithm with optimized computation architecture to better leverage HPC resources and technologies, (3) options for HPC-based linear and iterative solvers, (4) hidden HPC details, such as data communication and distribution, to enable development centered on mathematical models and algorithms rather than on computational details for power system researchers, and (5) easy integration of new dynamic models and related algorithms into the software package.

Huang, Renke↗

DOE OSTI2023

Automatic Code Generation for High-Performance Graph Algorithms

Graph problems are common across fields of scientific computing and social sciences. However, despite their importance, implementing graph algorithms effectively on modern computing systems is a challenging task that requires significant programming effort and generally results in customized implementations. Current computing and memory hierarchies are not architected for irregular computations resulting in challenges for graph algorithms to achieve high performance on those architectures. In this paper, we present GraphX, a novel compiler framework and DSL designed to simplify the development of efficient graph algorithms and achieve high performance on modern computing systems. GraphX consists of a DSL for efficient implementation of graph algorithms, various optimizations, such as support for sparse linear algebra and workspace transformations, optimized graph primitives, including semiring and masking, and a high-performance code generation engine. Using GraphX, users can implement graph algorithms using a semantically-rich language with graph-oriented operators. GraphX uses these semantics to automatically generate efficient code for target architectures, increasing performance and portability across architectures. The composable nature of GraphX makes it possible to extend the set of optimizations and architectures without modifying the source code. We demonstrate GraphX outperforms state-of-the-art graph libraries, such as LAGraph, up to $3.7 speedup in semiring operations, $2.19 speedup in an important sparse computational kernel, and $9.05 speedup in graph processing algorithms.

compiler, graph algorithms, semiring, masking, wor↗

DOE OSTI2018

System Administration Tools

High Performance Computing clusters and supercomputers are expensive and complex systems to manage. The software tools to manage these systems are also aging to the point where they are no longer actively developed and their methodology is antiquated. There has been a surge of new methodologies and software tools developed by the cloud providers to maximize availability of services and scalability of server deployment. The authors would like to utilize these new software tools to distribute our proof of concept, named Kraken, that will be responsible for booting and managing LANL's HPC systems in the future

Peltz, Paul↗

DOE OSTI2023

Frequency Recovery in Power Grids Using High-Performance Computing

Maintaining electric power system stability is paramount, especially in extreme contingencies involving unexpected outages of multiple generators or transmission lines that are typical during severe weather events. Such outages often lead to large supply-demand mismatches followed by subsequent system frequency deviations from their nominal value. The extent of frequency deviations is an important metric of system resilience, and its timely mitigation is a central goal of power system operation and control. This paper develops a novel nonlinear model predictive control (NMPC) method to minimize frequency deviations when the grid is affected by an unforeseen loss of multiple components. Our method is based on a novel multi-period alternating current optimal power flow (ACOPF) formulation that accurately models both nonlinear electric power flow physics and the primary and secondary frequency response of generator control mechanisms. We develop a distributed parallel Julia package for solving the large-scale nonlinear optimization problems that result from our NMPC method and thereby address realistic test instances on existing high-performance computing architectures. Our method demonstrates superior performance in terms of frequency recovery over existing industry practices, where generator levels are set based on the solution of single-period classical ACOPF models.

nonlinear model predictive control↗

DOE OSTI2017

SharP: Towards Programming Extreme-Scale Systems with Hierarchical Heterogeneous Memory

The pre-exascale systems are expected to have a significant amount of hierarchical and heterogeneous on-node memory, and this trend of system architecture in extreme-scale systems is expected to continue into the exascale era. Along with hierarchical-heterogeneous memory, the system typically has a high-performing network and a compute accelerator. This system architecture is not only effective for running traditional High Performance Computing (HPC) applications (Big-Compute), but also running data-intensive HPC applications and Big-Data applications. As a consequence, there is a growing desire to have a single system serve the needs of both Big-Compute and Big-Data applications. Though the system architecture supports the convergence of the Big-Compute and Big-Data, the programming models have yet to evolve to support either hierarchical-heterogeneous memory systems or the convergence. In this work, we propose and develop the programming abstraction called SHARed data-structure centric Programming abstraction (SharP) to address both of these goals, i.e., provide (1) a simple, usable, and portable abstraction for hierarchical-heterogeneous memory and (2) a unified programming abstraction for Big-Compute and Big-Data applications. To evaluate SharP, we implement a Stencil benchmark using SharP, port QMCPack, a petascale-capable application, and adapt Memcached ecosystem, a popular Big-Data framework, to use SharP, and quantify the performance and productivity advantages. Additionally, we demonstrate the simplicity of using SharP on different memories including DRAM, High-bandwidth Memory (HBM), and non-volatile random access memory (NVRAM).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Method and apparatus for managing access to a memory

A method and apparatus for managing access to a memory of a computing system. A controller transforms a plurality of operations that represent a computing job into an operational memory layout that reduces a size of a selected portion of the memory that needs to be accessed to perform the computing job. The controller stores the operational memory layout in a plurality of memory cells within the selected portion of the memory. The controller controls a sequence by which a processor in the computing system accesses the memory to perform the computing job using the operational memory layout. The operational memory layout reduces an amount of energy consumed by the processor to perform the computing job.

97 MATHEMATICS AND COMPUTING↗