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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Study on efficiency of time computation in x-ray imaging simulation base on Monte Carlo algorithm using graphics processing unit

Monte Carlo (MC) is one of the powerful techniques for simulation in x-ray imaging. MC method can simulate the radiation transport within matter with high accuracy and provides a natural way to simulate radiation transport in complex systems. One of the codes based on MC algorithm that are widely used for radiographic images simulation is MC-GPU, a codes developed by Andrea Basal. This study was aimed to investigate the time computation of x-ray imaging simulation in GPU (Graphics Processing Unit) compared to a standard CPU (Central Processing Unit). Furthermore, the effect of physical parameters to the quality of radiographic images and the comparison of image quality resulted from simulation in the GPU and CPU are evaluated in this paper. The simulations were run in CPU which was simulated in serial condition, and in two GPU with 384 cores and 2304 cores. In simulation using GPU, each cores calculates one photon, so, a large number of photon were calculated simultaneously. Results show that the time simulations on GPU were significantly accelerated compared to CPU. The simulations on the 2304 core of GPU were performed about 64 -114 times faster than on CPU, while the simulation on the 384 core of GPU were performed about 20 – 31 times faster than in a single core of CPU. Another result shows that optimum quality of images from the simulation was gained at the history start from 10{sup 8} and the energy from 60 Kev to 90 Kev. Analyzed by statistical approach, the quality of GPU and CPU images are relatively the same.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Developing extensible lattice-Boltzmann simulators for general-purpose graphics-processing units

Lattice-Boltzmann methods are versatile numerical modeling techniques capable of reproducing a wide variety of fluid-mechanical behavior. These methods are well suited to parallel implementation, particularly on the single-instruction multiple data (SIMD) parallel processing environments found in computer graphics processing units (GPUs). Although more recent programming tools dramatically improve the ease with which GPU programs can be written, the programming environment still lacks the flexibility available to more traditional CPU programs. In particular, it may be difficult to develop modular and extensible programs that require variable on-device functionality with current GPU architectures. This paper describes a process of automatic code generation that overcomes these difficulties for lattice-Boltzmann simulations. It details the development of GPU-based modules for an extensible lattice-Boltzmann simulation package - LBHydra. The performance of the automatically generated code is compared to equivalent purpose written codes for both single-phase, multiple-phase, and multiple-component flows. The flexibility of the new method is demonstrated by simulating a rising, dissolving droplet in a porous medium with user generated lattice-Boltzmann models and subroutines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

General Purpose Graphics Processing Unit Based High-Rate Rice Decompression and Reed-Solomon Decoding

As the volume of data acquired by space-based sensors increases, mission data compression/decompression and forward error correction code processing performance must likewise scale. This competency development effort was explored using the General Purpose Graphics Processing Unit (GPGPU) to accomplish high-rate Rice Decompression and high-rate Reed-Solomon (RS) decoding at the satellite mission ground station. Each algorithm was implemented and benchmarked on a single GPGPU. Distributed processing across one to four GPGPUs was also investigated. The results show that the GPGPU has considerable potential for performing satellite communication Data Signal Processing, with three times or better performance improvements and up to ten times reduction in cost over custom hardware, at least in the case of Rice Decompression and Reed-Solomon Decoding.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Discrete element simulation of Pebble Bed Reactors on graphics processing units

Prediction of pebble positions in a Pebble Bed Reactor (PBR) is necessary for both reactor physics and thermal hydraulics simulations as the arrangement of pebbles has a significant impact on the resulting core power, coolant flow, and fuel temperature. Knowledge of pebble movement as the fuel is cycled through the core is also critical for predicting the fuel residence time and subsequently, the fuel burnup. Simulation with the Discrete Element Method (DEM) can provide knowledge of both the fuel packing and the fuel movement during cycling. Previous works that have performed 3D full-core DEM simulation of PBRs have used simplified models that neglect reflector wall features. This work employs a graphics processing unit (GPU)-enabled DEM code, Project Chrono, to analyze the differences in pebble packing and pebble velocities between a simplified smooth PBR reflector and a more realistic reflector that includes circular wall features. Additionally, a sensitivity study is performed on the depth of the wall features to ensure that crystallization is prevented. Project Chrono is also validated for PBR cycling applications using experimental data. It is found that wall features with a depth of at least 0.5 pebble diameters significantly reduce crystallization in the near-wall region, leading to discrepancies in both packing fraction and pebble velocity in this region compared to the simplified reflector models. These discrepancies are found to lead to roughly a 5–10% difference in the prediction of the near-wall porosity and a 10% difference in the prediction of the velocity of pebbles near the wall. As a result of these discrepancies, it is suggested that future DEM simulations of PBRs include wall features to reduce modeling errors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Full Monte Carlo–Based Biologic Treatment Plan Optimization System for Intensity Modulated Carbon Ion Therapy on Graphics Processing Unit

One of the major benefits of carbon ion therapy is enhanced biological effectiveness at the Bragg peak region. For intensity modulated carbon ion therapy (IMCT), it is desirable to use Monte Carlo (MC) methods to compute the properties of each pencil beam spot for treatment planning, because of their accuracy in modeling physics processes and estimating biological effects. We previously developed goCMC, a graphics processing unit (GPU)-oriented MC engine for carbon ion therapy. The purpose of the present study was to build a biological treatment plan optimization system using goCMC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Fast, multi-channel real-time processing of signals with microsecond latency using graphics processing units

Fast, digital signal processing (DSP) has many applications. Typical hardware options for performing DSP are field-programmable gate arrays (FPGAs), application-specific integrated DSP chips, or general purpose personal computer systems. This paper presents a novel DSP platform that has been developed for feedback control on the HBT-EP tokamak device. The system runs all signal processing exclusively on a Graphics Processing Unit (GPU) to achieve real-time performance with latencies below 8 μs. Signals are transferred into and out of the GPU using PCI Express peer-to-peer direct-memory-access transfers without involvement of the central processing unit or host memory. Tests were performed on the feedback control system of the HBT-EP tokamak using forty 16-bit floating point inputs and outputs each and a sampling rate of up to 250 kHz. Signals were digitized by a D-TACQ ACQ196 module, processing done on an NVIDIA GTX 580 GPU programmed in CUDA, and analog output was generated by D-TACQ AO32CPCI modules.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Brookhaven Lab Hosts Hackathon Focused on Graphics Processing Units (GPUs)

For five days in June 2017, Brookhaven Lab’s Computational Science Initiative hosted “Brookathon”—a programming workshop where teams of researchers, students, and software developers worked with mentors to accelerate scientific applications on GPUs for high-performance computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Ground-referenced single-ended signaling connected graphics processing unit multi-chip module

A system of interconnected chips comprising a multi-chip module (MCM) includes a first processor chip, a graphics processing cluster (GPC) chip, and an MCM package configured to include the first processor chip, the GPC chip, and an interconnect circuit. The first processor chip is configured to include a first ground-referenced single-ended signaling interface circuit. A first set of electrical traces fabricated within the MCM package and configured to couple the first single-ended signaling interface circuit to the interconnect circuit. The GPC chip is configured to include a second single-ended signaling interface circuit and to execute shader programs. A second set of electrical traces fabricated within the MCM package and configured to couple the second single-ended signaling interface circuit to the interconnect circuit. In one embodiment, each single-ended signaling interface advantageously implements ground-referenced single-ended signaling.

Dally, William J.↗
DOE OSTI2014

The Metropolis Monte Carlo method with CUDA enabled Graphic Processing Units

We present a CPU–GPU system for runtime acceleration of large molecular simulations using GPU computation and memory swaps. The memory architecture of the GPU can be used both as container for simulation data stored on the graphics card and as floating-point code target, providing an effective means for the manipulation of atomistic or molecular data on the GPU. To fully take advantage of this mechanism, efficient GPU realizations of algorithms used to perform atomistic and molecular simulations are essential. Our system implements a versatile molecular engine, including inter-molecule interactions and orientational variables for performing the Metropolis Monte Carlo (MMC) algorithm, which is one type of Markov chain Monte Carlo. By combining memory objects with floating-point code fragments we have implemented an MMC parallel engine that entirely avoids the communication time of molecular data at runtime. Our runtime acceleration system is a forerunner of a new class of CPU–GPU algorithms exploiting memory concepts combined with threading for avoiding bus bandwidth and communication. The testbed molecular system used here is a condensed phase system of oligopyrrole chains. A benchmark shows a size scaling speedup of 60 for systems with 210,000 pyrrole monomers. Our implementation can easily be combined with MPI to connect in parallel several CPU–GPU duets. -- Highlights: •We parallelize the Metropolis Monte Carlo (MMC) algorithm on one CPU—GPU duet. •The Adaptive Tempering Monte Carlo employs MMC and profits from this CPU—GPU implementation. •Our benchmark shows a size scaling-up speedup of 62 for systems with 225,000 particles. •The testbed involves a polymeric system of oligopyrroles in the condensed phase. •The CPU—GPU parallelization includes dipole—dipole and Mie—Jones classic potentials.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

High-Performance Semiempirical Excited-State Molecular Dynamics Powered by Graphics Processing Units

Here, this Letter introduces excited-state molecular dynamics in PYSEQM, a GPU-accelerated semiempirical quantum chemistry engine implemented in PyTorch. The new module enables Born–Oppenheimer molecular dynamics (BOMD) using configuration-interaction singles and random phase approximation for excited states, allowing long trajectories and large statistical ensembles to be simulated efficiently on a single GPU. We also implement an extended Lagrangian excited-state BOMD (XL-ESMD) scheme that propagates auxiliary electronic variables, enabling relaxed ground and excited-state convergence thresholds without compromising energy conservation. The excited-state BOMD implementation scales smoothly from small chromophores to a nearly 900-atom dendrimer (taking 6.5 s per MD step). PYSEQM also supports batched execution, allowing many geometries or trajectories to be evaluated in a single GPU launch, substantially increasing throughput and making ensemble-based protocols routine. As a demonstration, we compute absorption, emission, and infrared spectra from trajectories propagated on the ground and first excited states. The XL-ESMD scheme yields identical spectra at significantly lower computational cost, establishing the role of extended Lagrangian based dynamics for efficient excited-state BOMD simulations. Beyond raw performance, PYSEQM’s PyTorch foundation provides automatic differentiation for forces, efficient GPU batching, and seamless interfacing with machine learning models. These capabilities position PYSEQM as a practical platform for machine learning-augmented excited-state dynamics and lay the foundation for future data-driven nonadiabatic excited-state dynamics modeling of ultrafast spectroscopic probes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Acceleration of the Parameterization of Unified Microphysics Across Scales (PUMAS) on the Graphics Processing Unit (GPU) With Directive-Based Methods

Cloud microphysics is one of the most time-consuming components in a climate model. In this study, we port the cloud microphysics parameterization in the Community Atmosphere Model (CAM), known as Parameterization of Unified Microphysics Across Scales (PUMAS), from CPU to GPU to seek a computational speedup. The directive-based methods (OpenACC and OpenMP target offload) are determined as the best fit specifically for our development practices, which enable a single version of source code to run either on the CPU or GPU, and yield a better portability and maintainability. Their performance is first examined in a PUMAS stand-alone kernel and the directive-based methods can outperform a CPU node as long as there is enough computational burden on the GPU. A consistent behavior is observed when we run PUMAS on the GPU in a practical CAM simulation. A 3.6× speedup of the PUMAS execution time, including data movement between CPU and GPU, is achieved at a coarse horizontal resolution (8 NVIDIA V100 GPUs against 36 Intel Skylake CPU cores). This speedup further increases up to 5.4× at a high resolution (24 NVIDIA V100 GPUs against 108 Intel Skylake CPU cores), which highlights the fact that GPU favors larger problem size. This study demonstrates that using GPU in a CAM simulation can save noticeable computational costs even with a small portion of code being GPU-enabled. Therefore, we are encouraged to port more parameterizations to GPU to take advantage of its computational benefit.

54 ENVIRONMENTAL SCIENCES↗