Results for “CUDA” — Page 18

NASA NTRS2011

Accelerated Adaptive MGS Phase Retrieval

The Modified Gerchberg-Saxton (MGS) algorithm is an image-based wavefront-sensing method that can turn any science instrument focal plane into a wavefront sensor. MGS characterizes optical systems by estimating the wavefront errors in the exit pupil using only intensity images of a star or other point source of light. This innovative implementation of MGS significantly accelerates the MGS phase retrieval algorithm by using stream-processing hardware on conventional graphics cards. Stream processing is a relatively new, yet powerful, paradigm to allow parallel processing of certain applications that apply single instructions to multiple data (SIMD). These stream processors are designed specifically to support large-scale parallel computing on a single graphics chip. Computationally intensive algorithms, such as the Fast Fourier Transform (FFT), are particularly well suited for this computing environment. This high-speed version of MGS exploits commercially available hardware to accomplish the same objective in a fraction of the original time. The exploit involves performing matrix calculations in nVidia graphic cards. The graphical processor unit (GPU) is hardware that is specialized for computationally intensive, highly parallel computation. From the software perspective, a parallel programming model is used, called CUDA, to transparently scale multicore parallelism in hardware. This technology gives computationally intensive applications access to the processing power of the nVidia GPUs through a C/C++ programming interface. The AAMGS (Accelerated Adaptive MGS) software takes advantage of these advanced technologies, to accelerate the optical phase error characterization. With a single PC that contains four nVidia GTX-280 graphic cards, the new implementation can process four images simultaneously to produce a JWST (James Webb Space Telescope) wavefront measurement 60 times faster than the previous code.

Lam, Raymond K.↗

NASA NTRS2013

A Simple GPU-Accelerated Two-Dimensional MUSCL-Hancock Solver for Ideal Magnetohydrodynamics

We describe our experience using NVIDIA's CUDA (Compute Unified Device Architecture) C programming environment to implement a two-dimensional second-order MUSCL-Hancock ideal magnetohydrodynamics (MHD) solver on a GTX 480 Graphics Processing Unit (GPU). Taking a simple approach in which the MHD variables are stored exclusively in the global memory of the GTX 480 and accessed in a cache-friendly manner (without further optimizing memory access by, for example, staging data in the GPU's faster shared memory), we achieved a maximum speed-up of approx. = 126 for a sq 1024 grid relative to the sequential C code running on a single Intel Nehalem (2.8 GHz) core. This speedup is consistent with simple estimates based on the known floating point performance, memory throughput and parallel processing capacity of the GTX 480.

graphics processing units↗

NASA NTRSDate not supplied

TPSAS-NF1676L-30692-DND

Objectives: transform a branch of the FUN3D mini-app into OpenCL, benchmark OpenCL routines against sequential and CUDA versions of the same routines, use Intel OpenCL SDK to run on FPGA, and compare performance and suggest opportunities for optimization.

Jason Orender↗

NASA NTRSDate not supplied

Implicit Thermochemical Nonequilibrium Compressible Flow Simulations on Unstructured Grids Using GPUs

As next-generation exascale-class systems arrive, existing software must be updated accordingly to effectively utilize these systems. For high concurrency and energy efficiency, many of these systems utilize GPU architectures. In this work, we present a CUDA C++ implementation of FUN3D's thermochemical nonequilibrium capability for turbulent flows. Efficiency is demonstrated at scale using the Summit system at the Oak Ridge Leadership Computing Facility which is representative of future exascale systems. This work enables faster, higher fidelity, and scale-resolving simulations of thermochemical nonequilibrium flows including reentry, hypersonics, and combustion.

CFD, GPU, HPC, Hypersonics, Chemistry↗

NASA NTRSDate not supplied

Implicit Thermochemical Nonequilibrium Flow Simulations on Unstructured Grids using GPUs

Thermochemical nonequilibrium flow simulation capabilities have been previously implemented, verified, and validated for central processing unit (CPU) systems in NASA’s unstructured-grid computational fluid dynamics solver FUN3D. Many exascale-class high-performance computing systems will rely on graphics processing unit (GPU) architectures for high throughput and energy efficiency; thus, CPU-based scientific computing software unable to effectively utilize these systems must be updated. In this work, we present a CUDA C++ implementation of FUN3D’s thermochemical nonequilibrium flow simulation capabilities targeting NVIDIA Tesla GPUs. An overview of the porting and optimization strategy is described and performance comparisons with other recent architectures are presented. Scaling to thousands of GPUs is demonstrated, yielding computational performance equivalent to that of several million CPU cores. The implementation enables efficient, high-fidelity, scale-resolving simulations of thermochemical nonequilibrium flows for many applications including atmospheric entry, hypersonics, and combustion.

GPU↗

NASA NTRSDate not supplied

Ray Tracing Techniques for the Characterization of Lunar Communication Architectures

This paper provides an overview of the computational techniques used to characterize the viability of different lunar architectures and their ability to provide communication services to the lunar surface. This analysis was done with modern ray tracing techniques that allow for the computations to be done on Graphics Processing Unit (GPU) clusters for a high level of parallelism and severe reduction in computation time. The ray tracing computations were done with the GPU platform Compute Unified Device Architecture (CUDA) provided by NVIDIA which utilizes general-purpose computing on graphics processing units (GPGPU). This new method provides the advantage of being able to characterize a much larger portion of the lunar surface due to its computational efficiency as well as providing a more accurate representation of elevation angle limits instead of the typical and often inaccurate elevation angle mask. The Lunar surface can now be characterized with metrics such as contact time, outage time, and received data rate. With these metrics, different proposed Lunar architectures can be rapidly evaluated. This reduction in computation time not only leads to more accurate results but allows these results to be obtained in a time frame that allows for the complete characterization of the trade space. It is expected that these different architecture comparisons will lead to a conclusive determination of the optimal Lunar architecture and will allow for future Lunar missions to operate as close to real time as possible. In addition, this computation method can be used to recreate visibility figures generated by previous methods but with an increased level of accuracy.

Thomas Montano↗

NASA NTRSDate not supplied

Performance and Accuracy Assessment of Line Marching Algorithm Computations Utilizing GPUs Within a Predictive GNSS Quality Service

This paper presents a detailed analysis of the accuracy and performance of line marching algorithms executing on a GPU. In the context of an accurate Global Navigation Satellite System(GNSS) quality of service simulation, horizon sky-plots are a useful tool to determine satellite visibility in the presence of obstructions from objects, such as buildings or dense foliage. In order to accurately model satellite visibility at a point of interest on a map, a horizon plot can identify the viewing angles at which objects are blocking the sky. This computation requires traversing a line starting at the point of interest on a 2D altitude map, moving outward for every azimuth angle. To explore the performance of this computation, we propose a new dynamic stopping condition for the traversal of the line, benefiting from objects close to the point of interest. We compare the accuracy of common line marching algorithms, and consider their parallel performance when developed in CUDA. We find that our proposed stopping condition for line marching provides a significant improvement in performance in urban canyon sky-plots, as compared to previous work. Additionally, these results show that simpler algorithms, such as the digital differential analyzer line algorithm, are better suited for GPUs than more sophisticated schemes such as Bresenham’s algorithm, specifically in the context of sky-plothorizon computations. The trade-off between accuracy and performance is analyzed and providing guidance that depends on the targeted goal of the GNSS application.

GNSS↗

NASA NTRSDate not supplied

Adding GPU Support to the Markov Chain Monte Carlo Code Catmip

In geophysics, we are confronted with many under-determined inverse problems. For example, all of our observations of earthquakes are made at the Earth’s surface. So, when we try to infer how slip during an earthquake evolves in space and time, we find that there are many potential slip histories that are consistent with our limited observations and our understanding of earthquake physics. One way to approach these problems is with Bayesian analysis which allows us to infer the ensemble of all potential slip models that satisfy the observations and our prior knowledge of earthquake physics. In Bayesian analysis, our prior knowledge is known as the prior probability density function or prior PDF, the fit to the data is known as the data likelihood, and the target PDF that satisfies both the prior PDF and data likelihood is known as the posterior PDF. However, simulating the posterior PDF typically requires using Markov Chain Monte Carlo (MCMC) to draw tens of billions of random realizations of earthquake slip models, which may not be computationally feasible. To make this and similar geophysical inversions computationally tractable, we developed the Cascading Adaptive Transitional Metropolis In Parallel (CATMIP) algorithm. CATMIP is an efficient parallel Markov Chain Monte Carlo (MCMC) sampler that is used for model fitting and uncertainty quantification in geophysics. Example use cases are earthquake rupture modeling, determining mineral composition on Mars, reconstructing the history of ocean salinity, and historical earthquake relocation. CATMIP employs many parallel instances of the Metropolis algorithm for sampling in a transitioning framework. Transitioning is a process in which a set of random samples at equilibrium with a known probability density function (PDF) are used as seeds for the Markov chains to sample successive target PDFs that incrementally move the distribution from the starting seeds to the final desired PDF that describes the relative plausibility of potential values for the model parameters. The algorithm is implemented as a Master-Worker model employing MPI for communication. The worker processes are loosely coupled with global parameters periodically optimized by the master process. This provides a very high amount of parallelism with little communication between updates. During the presentation we will discuss the history of the algorithm and elaborate the earthquake rupture modeling use case for the CATMIP package. Our first step toward GPU optimization was to optimize the code for the CPU. CPU profiling revealed that most of the compute time is spent in calls to level 2 BLAS routines and calls to GSL random number generators. We revised the algorithm to employ level 3 BLAS routines instead. In our presentation we will describe how this was accomplished. Adding GPU support to CATMIP consisted mostly of replacing the calls to GSL with calls to GPU vendor-provided library routines. A small number of loops were directly implemented in CUDA. In the presentation will provide implementation details. Finally, we will discuss methods for profiling and opportunities for further optimizing GPU execution. By creating a code with the flexibility to run on either a CPU or GPU architecture, CATMIP can be used on systems ranging from large CPU-based HPC environments to single servers with GPU acceleration and everything in between.

HECC↗

NASA NTRSDate not supplied

A High-Performance Computing GNSS-aware Path Planning Algorithm for Safe Urban Flight Operations

The emergence and development of advanced technologies and vehicle types have created a growing demand for new forms of flight operations. These new and increasingly complex operational paradigms, such as Advanced and Urban Air Mobility (AAM/UAM), present regulatory authorities and the aviation community with several design-and-implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is to develop protocols that can integrate these operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive, autonomous, risk mitigation capability becomes critical to meet this challenge. This paper proposes and evaluates a computationally-efficient path planning approach to perform pre-flight planning and autonomous in-flight re-routing to minimize exposures to selected hazards. In our evaluation, hazards associated with degraded and missing critical GPS navigation data are considered. In this paper, we first present a high-performance computing path planning approach based on an adapted Bellman-Ford algorithm, developed in the CUDA programming language. Using the adapted path planning algorithm, we test this algorithm when encountering issues with GPS quality, and deliver an implementation that can produce flight paths that minimize exposure to risks, while maintaining a low computational burden. In our evaluation, the computation of periodic and aperiodic path updates are evaluated, prioritizing specific events as triggers for updates, based on changes to satellite availability. These critical events can lead to significant exposure to navigational hazards if not dealt with correctly.

GNSS↗

NASA NTRSDate not supplied

A High-Performance Computing GNSS-aware Path Planning Algorithm for Safe Urban Flight Operations

The emergence and development of advanced technologies and vehicle types have created a growing demand for new forms of flight operations. These new and increasingly complex operational paradigms, such as Advanced and Urban Air Mobility (AAM/UAM), present regulatory authorities and the aviation community with several design-and-implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is to develop protocols that can integrate these operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive, autonomous, risk mitigation capability becomes critical to meet this challenge. This paper proposes and evaluates a computationally-efficient path planning approach to perform pre-flight planning and autonomous in-flight re-routing to minimize exposures to selected hazards. In our evaluation, hazards associated with degraded and missing critical GPS navigation data are considered. In this paper, we first present a high-performance computing path planning approach based on an adapted Bellman-Ford algorithm, developed in the CUDA programming language. Using the adapted path planning algorithm, we test this algorithm when encountering issues with GPS quality, and deliver an implementation that can produce flight paths that minimize exposure to risks, while maintaining a low computational burden. In our evaluation, the computation of periodic and aperiodic path updates are evaluated, prioritizing specific events as triggers for updates, based on changes to satellite availability. These critical events can lead to significant exposure to navigational hazards if not dealt with correctly.

GNSS↗

NASA NTRSDate not supplied

Porting OVERFLOW CFD Code to GPUs: To Hackathons and Beyond!

OVERFLOW is an overset, structured computational fluid dynamics (CFD) code written in Fortran which is widely used in the government, industry, and academia. Over the last several years the OVERFLOW developers have been working to port miniapps based on computationally expensive parts of OVERFLOW to run on GPUs, primarily using OpenACC. This effort started at our first hackathon in 2019 and since then the OVERFLOW team has attended two additional hackathons (virtually). These hackathon environments have provided a great place to collaborate with others and learn from experts. These learning experiences enabled porting two miniapps to run effectively on NVIDIA GPUs using OpenACC. The first miniapp focused on motifs found in the solver itself and the final ported version runs three times fast ona single V100 compared to a 40 core, dual-socket Intel Skylake node. The speed up in this solverminiapp required multiple design changes including increasing the amount of parallelism available and the amount of work performed in each kernel. The second miniapp focused on overset MPI communication, also saw significant speedups over the CPU implementation using a CUDA-aware MPI implementation through OpenACC. This presentation will discuss our experience at the hackathons, our process of porting the miniapps to run on the GPUs, and several lessons learned throughout.

OpenACC↗

NASA NTRSDate not supplied

A Multi-Architecture Approach for Implicit Computational Fluid Dynamics on Unstructured Grids

High-performance computing (HPC) architectures are trending toward manycore paradigms such as graphics processing units (GPUs). Approximately half of the top 100 publicly disclosed supercomputers in the world utilize GPU accelerators for performance. This is in contrast to a decade ago, where there were only a few such machines in the top 100. It is not currently possible to compile and run legacy central processing unit (CPU) software efficiently on GPUs without significant refactoring. Though a number of frameworks offering performance portability exist, none offer a standardized specification that is supported by all major hardware vendors. Additionally, experiences show that obtaining a high percentage of peak performance often requires architecture-specific code. This work details a pragmatic multi-architecture computational fluid dynamics library focused on aerospace problems across the speed range from low subsonic to hypersonic flows involving thermochemical nonequilibrium. A thin abstraction layer above NVIDIA CUDA C++ is utilized, which enables primarily single-source software currently capable of running efficiently on multicore CPUs, NVIDIA GPUs, AMD GPUs, and Intel GPUs. Results on various problems of interest across the speed range are presented and performance is compared between various architectures.

GPU↗

NASA NTRSDate not supplied

A Multi-Architecture Approach for Implicit Computational Fluid Dynamics on Unstructured Grids

High-performance computing (HPC) architectures are trending toward manycore paradigms such as graphics processing units (GPUs). Approximately half of the top 100 publicly disclosed supercomputers in the world utilize GPU accelerators for performance. This is in contrast to a decade ago, where there were only a few such machines in the top 100. It is not currently possible to compile and run legacy central processing unit (CPU) software efficiently on GPUs without significant refactoring. Though a number of frameworks offering performance portability exist, none offer a standardized specification that is supported by all major hardware vendors. Additionally, experiences show that obtaining a high percentage of peak performance often requires architecture-specific code. This work details a pragmatic multi-architecture computational fluid dynamics library focused on aerospace problems across the speed range from low subsonic to hypersonic flows involving thermochemical nonequilibrium. A thin abstraction layer above NVIDIA CUDA C++ is utilized, which enables primarily single-source software currently capable of running efficiently on multicore CPUs, NVIDIA GPUs, AMD GPUs, and Intel GPUs. Results on various problems of interest across the speed range are presented and performance is compared between various architectures.

GPU↗

NASA NTRS2023

Framework for Extensible, Asynchronous Task Scheduling (FEATS) in Fortran

Most parallel scientific programs contain compiler directives (pragmas) such as those from OpenMP, explicit calls to runtime library procedures such as those implementing the Message Passing Interface (MPI), or compiler-specific language extensions such as those provided by CUDA. By contrast, the recent Fortran standards empower developers to express parallel algorithms without directly referencing lower-level parallel programming models. Fortran’s parallel features place the language within the Partitioned Global Address Space (PGAS) class of programming models. When writing programs that exploit data-parallelism, application developers often find it straightforward to develop custom parallel algorithms. Problems involving complex, heterogeneous, staged calculations, however, pose much greater challenges. Such applications require careful coordination of tasks in a manner that respects dependencies prescribed by a directed acyclic graph. When rolling one’s own solution proves difficult, extending a customizable framework becomes attractive. The paper presents the design, implementation, and use of the Framework for Extensible Asynchronous Task Scheduling (FEATS), which we believe to be the first task-scheduling tool written in modern Fortran. We describe the benefits and compromises associated with choosing Fortran as the implementation language, and we propose ways in which future Fortran standards can best support the use case in this paper.

Modern Fortran↗

DOE OSTI2012

High-Performance Beam Simulator for the LANSCE Linac

A high performance multiparticle tracking simulator is currently under development at Los Alamos. The heart of the simulator is based upon the beam dynamics simulation algorithms of the PARMILA code, but implemented in C++ on Graphics Processing Unit (GPU) hardware using NVIDIA's CUDA platform. Linac operating set points are provided to the simulator via the EPICS control system so that changes of the real time linac parameters are tracked and the simulation results updated automatically. This simulator will provide valuable insight into the beam dynamics along a linac in pseudo real-time, especially where direct measurements of the beam properties do not exist. Details regarding the approach, benefits and performance are presented.

43 PARTICLE ACCELERATORS↗

DOE OSTI2014

Nanoscale Cluster Detection in Massive Atom Probe Tomography Data

Recent technological advances in atom probe tomography (APT) have led to unprecedented data acquisition capabilities that routinely generate data sets containing hundreds of millions of atoms. Detecting nanoscale clusters of different atom types present in these enormous amounts of data and analyzing their spatial correlations with one another are fundamental to understanding the structural properties of the material from which the data is derived. Extant algorithms for nanoscale cluster detection do not scale to large data sets. Here, a scalable, CUDA-based implementation of an autocorrelation algorithm is presented. It isolates spatial correlations amongst atomic clusters present in massive APT data sets in linear time using a linear amount of storage. Correctness of the algorithm is demonstrated using large synthetically generated data with known spatial distributions. Benefits and limitations of using GPU-acceleration for autocorrelation-based APT data analyses are presented with supporting performance results on data sets with up to billions of atoms. To our knowledge, this is the first nanoscale cluster detection algorithm that scales to massive APT data sets and executes on commodity hardware.

Seal, Sudip K↗

DOE OSTI2013

miniGMG

miniGMG is a compact geometric multigrid (MG benchmark designed to proxy the performance characteristics of the solves found in adaptive mesh refinement multigrid (AMR MG) applications. It solves the equation a*alpha*u - b div beta grad u = f on a large cubical domain using cell-centered values and periodic boundaries. a and b are scalar constants, alpha and beta are space varying constants, f is the right hand side, and u is the solution. The cubical domain is divided into cubical subdomains which are distributed across the supercomputer. The righthand side f is generated from a continuous function for u. Thus, one can solve this linear system of equations and verify the error properties of the discrete solution u by comparing to a sampling of the continuous u. miniGMG implements both v- and f- cycles, as well as two constant coeffficient discretications of the equation above (one 27pt 2nd order, and one 13pt 4th order). In both cases, the (embedded) v-cycles are truncated when a subdomain reaces 4^3, 2^3, or 1^3 cells (a u-cycle). The multigrd solver then swtiches to one of seven coarse grid (bottom) solves. relaxation, BiCGStab, CABiCGStab, TelescopingCABiCGStab, CG, or CACG. The code exploits two forms of communication-avoiding. The first is exemplified by the referenced SC'12 paper in which DRAM data movment is avoided when smoothing. The second, described in the IPDPS'14 paper avoids collectives in a bottom solve (the CA bottom solves). There are a number of implementations of the operators for both CPUs and NVIDIA GPUs. The CPUs have been optimized to exploit a wavefront approach to communication-avoiding. The GPU versions include a number of optimized and tunable implementations. The code has been demonstrated to scale to 46K nodes on the BGQ Mira (750K cores), 9K nodes on Edison (111K cores), and 14K GPUs on Titan (37M cuda cores). It thus acts as a scalable testbed for a wide range of computer science and applied math research. Nominally, one invokes the benchmark as ./run b x y z alpha beta gamma Each subdomain is (2^b)^3 cells Each MPI process receives a x-by-y-by-z collection of subdomains. There are alpha*beta*gamma total MPI processes For correctness (a cubical domain), *alpha==y*beta==z*gamma.

Williams, Sameul↗

DOE OSTI2015

Evaluating Performance Portability of OpenACC

Accelerator-based heterogeneous computing is gaining momentum in High Performance Computing arena. However, the increased complexity of the accelerator architectures demands more generic, high-level programming models. OpenACC is one such attempt to tackle the problem. While the abstraction endowed by OpenACC offers productivity, it raises questions on its portability. This paper evaluates the performance portability obtained by OpenACC on twelve OpenACC programs on NVIDIA CUDA, AMD GCN, and Intel MIC architectures. We study the effects of various compiler optimizations and OpenACC program settings on these architectures to provide insights into the achieved performance portability.

Sabne, Amit J↗