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At least 685 records · Page 38

All sources NASA NTRS DOE OSTI

DOE OSTI2018

Lightweight Detection of Cache Conflicts

In memory hierarchies, caches perform an important role in reducing average memory access latency. Minimizing cache misses can yield significant performance gains. As set-associative caches are widely used in modern architectures, capacity and conflict cache misses co-exist. These two types of cache misses require different optimization strategies. While cache misses are commonly studied using cache simulators, state-of-the-art simulators usually incur hundreds to thousands of times a program’s execution runtime. Moreover, a simulator has difficulty in simulating complex real hardware. To overcome these limitations, measurement methods are proposed to directly monitor program execution on real hardware via performance monitoring units. However, existing measurement-based tools either focus on capacity cache misses or do not distinguish capacity and conflict cache misses. In this paper, we design and implement CCProf, a lightweight measurement-based profiler that identifies conflict cache misses and associates them with program source code and data structures. CCProf incurs moderate runtime overhead that is at least an order of magnitude lower than simulators. With the evaluation on a number of representative programs, CCProf is able to guide optimizations on cache conflict misses and obtain nontrivial speedups.

Roy, Probir↗

DOE OSTI2018

Juggler: a dependence-aware task-based execution framework for GPUs

Scientific applications with single instruction, multiple data (SIMD) computations show considerable performance improvements when run on today's graphics processing units (GPUs). However, the existence of data dependences across thread blocks may significantly impact the speedup by requiring global synchronization across multiprocessors (SMs) inside the GPU. To efficiently run applications with interblock data dependences, we need fine-granular task-based execution models that will treat SMs inside a GPU as stand-alone parallel processing units. Such a scheme will enable faster execution by utilizing all internal computation elements inside the GPU and eliminating unnecessary waits during device-wide global barriers.In this paper, we propose Juggler, a task-based execution scheme for GPU workloads with data dependences. The Juggler framework takes applications embedding OpenMP 4.5 tasks as input and executes them on the GPU via an efficient in-device runtime, hence eliminating the need for kernel-wide global synchronization. Juggler requires no or little modification to the source code, and once launched, the runtime entirely runs on the GPU without relying on the host through the entire execution. We have evaluated Juggler on an NVIDIA Tesla P100 GPU and obtained up to 31% performance improvement against global barrier based implementation, with minimal runtime overhead.

Belviranli, Mehmet E.↗

DOE OSTI2019

Evaluating the effectiveness of program data features for guiding memory management

Recent trends have led to the adoption of larger and more complex memory systems, often with multiple tiers of memory performance within the same platform. To utilize complex memory systems efficiently, current data management strategies must be altered to map usage demands to the underlying hardware. Applications, as the generators of memory accesses, are well-suited to guide this process, but building and maintaining separate source code versions for different memory systems is not feasible in most cases. One potential solution is to employ automated program profiling and analysis to facilitate the production of application-based guidance. By attaching memory usage information to static or lightweight program features, compilers and runtime systems can generate fine-grained guidance without additional efforts from users or developers. Recent works have employed this approach with some success, but it is not clear which program features are most useful for guiding data management.This work evaluates the effectiveness of using different program data features to predict memory usage and guide memory management. It employs a custom set of simulation tools, based in the Intel® Pin framework, to collect and analyze the usage characteristics of application data associated with common program data features, such as allocation sites, types, and context. The results show that even relatively simple features, such as object size, are useful for selecting data with similar usage properties, but finer-grained features, such as the instructions that access a particular object, are often much more effective. Additionally, this work evaluates the performance of using different data features and different program inputs to guide data placement on a heterogeneous memory platform with a limited amount of high performance memory.

Effler, T. Chad↗

DOE OSTI2023

MatRIS: Multi-level Math Library Abstraction for Heterogeneity and Performance Portability using IRIS Runtime

Vendor libraries are tuned for a specific architecture and are not portable to others. Moreover, they lack support for heterogeneity and multi-device orchestration, which is required for efficient use of contemporary HPC and cloud resources. To address these challenges, we introduce MatRIS—a multilevel math library abstraction for scalable and performance-portable sparse/dense BLAS/LAPACK operations using IRIS runtime. The MatRIS-IRIS co-design introduces three levels of abstraction to make the implementation completely architecture agnostic and provide highly productive programming. We demonstrate that MatRIS is portable without any change in source code and can fully utilize multi-device heterogeneous systems by achieving high performance and scalability on Summit, Frontier, and a CADES cloud node equipped with four NVIDIA A100 GPUs and four AMD MI100 GPUs. A detailed performance study is presented in which MatRIS demonstrates multi-device scalability. When compared, MatRIS provides competitive and even better performance than libraries from vendors and other third parties.

Monil, M. A. H.↗

DOE OSTI2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

KiT-RT: An Extendable Framework for Radiative Transfer and Therapy

Here, in this article, we present Kinetic Transport Solver for Radiation Therapy (KiT-RT), an open-source C++-based framework for solving kinetic equations in therapy applications available at https://github.com/CSMMLab/KiT-RT . This software framework aims to provide a collection of classical deterministic solvers for unstructured meshes that allow for easy extendability. Therefore, KiT-RT is a convenient base to test new numerical methods in various applications and compare them against conventional solvers. The implementation includes spherical harmonics, minimal entropy, neural minimal entropy, and discrete ordinates methods. Solution characteristics and efficiency are presented through several test cases ranging from radiation transport to electron radiation therapy. Due to the variety of included numerical methods and easy extendability, the presented open-source code is attractive for both developers, who want a basis to build their numerical solvers, and users or application engineers, who want to gain experimental insights without directly interfering with the codebase.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Streaming Data in HPC Workflows Using ADIOS

The “IO Wall” problem, in which the gap between computation rate and data access rate grows continuously, poses significant problems to scientific workflows which have traditionally relied upon using the filesystem for intermediate storage between workflow stages. One way to avoid this problem in scientific workflows is to stream data directly from producers to consumers and avoiding storage entirely. However, the manner in which this is accomplished is key to both performance and usability. This paper presents the Sustainable Staging Transport, an approach which allows direct streaming between traditional file writers and readers with few application changes. SST is an ADIOS “engine”, accessible via standard ADIOS APIs, and because ADIOS allows engines to be chosen at run-time, many existing file-oriented ADIOS workflows can utilize SST for direct application-to-application communication without any source code changes. This paper describes the design of SST and presents performance results from various applications that use SST, for feeding model training with simulation data with substantially higher bandwidth than the theoretical limits of Frontier’s file system, for strong coupling of separately developed applications for multiphysics multiscale simulation, or for in situ analysis and visualization of data to complete all data processing shortly after the simulation finishes.

Podhorszki, Norbert [ORNL] (ORCID:000000019647542X↗

DOE OSTI2019

Sierra Center of Excellence: Lessons Learned

The introduction of heterogeneous computing via GPUs from the Sierra architecture represented a significant shift in direction for computational science at Lawrence Livermore National Laboratory (LLNL), and therefore required significant preparation. Over the last five years, the Sierra Center of Excellence (CoE) has brought employees with specific expertise from IBM and NVIDIA together with LLNL in a concentrated effort to prepare applications, system software, and tools for the Sierra supercomputer. This article shares the process we applied for the CoE and documents lessons learned during the collaboration, with the hope that others will be able to learn from both our success and intermediate setbacks. We describe what we have found to work for the management of such a collaboration and best practices for algorithms and source code, system configuration and software stack, tools, and application performance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

ARM Data Integrator

The Atmospheric Radiation Measurement (ARM) Data Integrator (ADI) streamlines the development of scientific algorithms and analysis of time-series NetCDF data, and improves the content and consistency of the output data products produced by these algorithms. The framework automates the process of retrieving and preparing data for analysis, and allows users to design output data products through a graphical interface. It also provides a modular, flexible software development architecture that scientists can use to generate C, Python, and IDL source code templates that embed the pre and post processing logic allowing the scientist to focus on only their science. The input data, preprocessing, and output data specifications of algorithms are defined through a graphical interface and stored in a database. ADI implements workflow for data integration and supports user access to data through a library of software modules. Data preprocess capabilities supported include automated retrieval of data from input files, merging the retrieved data into appropriately sized chunks, and transformation of the data onto a common coordinate system grid. Through the graphical interface, users can view the details of both their data products and those in the ARM catalog and allows developers to use existing data product to build new data products. Views of the output data products include an overlay of how the design meets ARM archive's data standards providing the user with a visual cue indicating where their output violates an archive standard. The ADI libraries access the information provided through the GUI via a Postgres database. The ADI framework and its supporting components can significantly decrease the time and cost of implementing scientific algorithms while improving the ability of scientists to disseminate their results.

Source record↗

DOE OSTI2018

RMA-MT Benchmarks

The RMA-MT benchmarks are a suite of performance tests for multi-threaded Message Passing Interface (MPI) Remote Memory Access (RMA) implementations. The suite includes tests for bandwidth and latency with any number of threads. This suite was originally developed and release by Sandia National Laboratory. LANL modifications to the suite include a complete re-write of much of the source code, bi-directional bandwidth, and message rate tests.

Hjelm, Nathan↗

DOE OSTI2018

SolarPILOT™

SolarPILOT™advances the analytical flux image Hermite series approximation used in DELSOL3 by applying it to individual heliostat images rather than "zones", allowing characterization of a wide variety of heliostat field layouts. SolarPILOT also integrates the SolTrace Monte-Carlo ray tracing engine, providing improved receiver optical modeling capability, a user-friendly front end for geometry definition, and side-by-side validation of the analytical algorithms. The tool and source code are publicly available and are designed for third-party modification and extendability. The software is intended as a replacement for the more antiquated DELSOL3 algorithm originally developed at Sandia National Laboratory. The software will be used at NREL for power tower component design analysis and may be used at other laboratories, in academia, or as a third-party validation tool by some industry groups.

Wagner, Michael↗

DOE OSTI2018

Sandia Cloud Forensics and Incident Response Platform v 1.0

As systems and devices become virtualized and deployed in the cloud, the hypervisor becomes an increasingly appropriate place to collect performance data, system state, system landscape, function calls, transaction traces, and other characteristics. We developed a method by which an introspection application may be coupled with a hypervisor to “reach into” the VM with minimal intrusiveness to collect data critical to the reconstruction of events, files, and operations. Such a capability is required to take advantage of the hypervisor as an instrumentation platform and to integrate that data with more traditional collection mechanisms. The concept of a VM serviced by a lightweight hypervisor is a relatively new paradigm for forensic practitioners. Traditional forensic techniques, based on assumptions that the filesystem was directly interacting with the hardware through an abstraction, afforded the forensic practitioner the assumption that there was nothing controlling the application below the filesystem. This is not the case when using virtualized technologies. Hypervisors can covertly monitor, introspect and interact with the guest in a transparent fashion. The problems of storage and collection of actionable data are exhausting. The current challenge is most hypervisors do not expose a useful application programming interface (API) at a sufficient level to do transparent, fine-grained and customizable introspection. Scalable VM instrumentation and introspection at an in-depth level requires fast handling of events, as well as direct access to VM state. Furthermore, deep introspection benefits greatly from the ability to gather data from the hardware during the VM’s exit to the hypervisor. All of this requires identical access to the system as the hypervisor itself; improper use of this ability could easily cause system instability. It is for this reason we believe that the hypervisor developers have been hesitant to grant this much control through their APIs. However, our approach leverages other means to collect and monitor the guests in a targeted fashion. Virtual machine introspection (VMI) is a technique used to monitor the runtime state of a system-level virtual machine. The runtime state can be may include processor registers, memory, disk, network, and any other hardware-level events A review of research literature and current VMI technologies exposed several limitations and tradeoffs in VMI approaches, including: the use of in-guest agents; kernel to user space transitions (dramatically slowing down processing); VMI tool pre-configuration requirements; hypervisor version lock-in or source code patching; reliance on operating system symbols; limited processor features due to hypervisor (even if the hardware could do more). To address these constraints, a VMI tool was envisioned to provide the cloud forensic capabilities while having as few of these limitations as possible. The Kernel-based Virtual Machine Introspection (KVMi) tool was developed for hypervisors on Intel’s x86-64 architecture. To meet performance, scoping and use-case demands, the follow criteria were applied to KVMi to support live, forensic data collection: • Shall not require in-guest agents. • Shall work with any recent version of modern hypervisors. • Shall introspect upon any guest (VM) running modern versions of Windows, Linux and OS X. • Shall have the ability to find and track operating system artifacts • Shall not require operating system symbol files. • Shall be able to fully handle VM-exits, bypassing execution of the hypervisor if necessary, to facilitate new features the hypervisor may not support. • Shall be compilable/loadable on a running system with standard build tools.

Stout, WilliamM.S.↗

DOE OSTI2019

ALFABET™ and nfp (Neural Fingerprint) [SWR-19-13]

Keras layers for end-to-end learning on molecular structure. Based on Keras, Tensorflow, and RDKit. Source code used in the study Message-passing neural networks for high-throughput polymer screening. SEE ALSO https://github.com/NREL/alfabet

St. John, Peter↗

DOE OSTI2019

Ompparser

ompparser is a standalone and unified OpenMP parser, which is motivated by the facts that: 1) The differences in terms of syntax and semantics of OpenMP constructs between C/C++ and Fortran are minor. 2) Current OpenMP compilers often develop their own parsers, which represent redundant work. The features of ompparser include: 1) ompparser can be used standalone for static source code analysis, e.g. tools for semantics checking or similarity analysis between C/C++ and Fortran programs 2) omppaser can be integrated into an OpenMP compiler implementation can reduce the development efforts, and there is no need to create and maintain two separate parsers for C/C++ and Fortran. 3) ompparser provides a complete reference OpenMP grammar in the Backus-Naur Form that formally describes all the latest OpenMP language constructs. This will help users understand the rules and restriction of the OpenMP standard, which no longer contains a reference grammar in its recent versions.

Wang, Anjia↗

DOE OSTI2020

Parameter Optimization Toolbox for NS-3 network optimization, NS-3 Parameter Optimization Framework [SWR-18-60]

This simulation-based parameter optimization framework is proposed to tune parameters of different types of communication networks using ns-3 to achieve the optimal network performance. It consists of three main components: an ns-3 packet reporting module; a sampler running simulations with all possible parameter sets for the input parameter variables by using a parallel executor at each generation; and a hybrid optimization algorithm for tuning configurable parameters of hybrid designs and application parameter variables. The proposed hybrid metaheuristic optimization algorithm combines an evolutionary algorithm with a gradient descent function to quickly achieve an approximate globally optimum solution. This software is designed to be used in a multi-core processing Linux environment and run over a long duration of time. The execution time varies depending mainly upon the nature of the ns-3 configuration being simulated. This software includes a custom ns-3 QoS measurement application which must be included with the ns-3 source code during installation of the software.

Hasandka, Adarsh↗

DOE OSTI2017

Sunlamp

The purpose of this model was to facilitate the design of a control system that uses fine grained control of residential and small commercial HVAC loads to counterbalance voltage swings caused by intermittent solar power sources (e.g., rooftop panels) installed in that distribution circuit. Included is the source code and pre-compiled 64 bit dll for adding building HVAC loads to an OpenDSS distribution circuit. As written, the Makefile assumes you are using the Microsoft C++ development tools.

Nutaro, JamesJ↗

DOE OSTI2018

DataXray

Lab experiments involving digital signal processing tend to capture heterogeneous data that is binned into individual files. The structure of the whole experiment tends to get lost in this process, generating a "mess" of data that is difficult to summarize, and even more difficult to analyze in a consistent way. DataXray is a set of python routines that wraps publicly available open-source codes to create a consistent workflow for data and experiments, involving imaging, spectral and hyperspectral data. Routines are provided for data translation, slicing, visualization, multivariate statistics, integral transforms, feature extraction and reporting in the form of publication-grade figures.

Maksymovych, Petro↗

DOE OSTI2015

GnuForPlot Graphics

Gnuforplot Graphics is a Fortran90 program designed to generate two and three dimensional plots of data on a personal computer. The program uses calls to the open source code Gnuplot to generate the plots. Two Fortran90 programs have been written to use the Gnuplot graphics capabilities. The first program, named Plotsetup.f90 reads data from output files created by either the Stadium or LeachXS/Orchestra modeling codes and saves the data in arrays for plotting. This program then calls Gnuforplot which takes the data array along with user specified parameters to set plot specifications and issues Gnuplot commands that generate the screen plots. The user can view the plots and optionally save copies in jpeg format.

Flach, GregoryP.↗