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Single-Event Effects (SEE) testing was conducted on the AMD e9173 Graphics Processor Unit (GPU). Testing was conducted at Massachusetts General Hospital's (MGH) Francis H. Burr Proton Therapy Center.
Single-Event Effects (SEE) testing was conducted on the nVidia GTX 1050 Graphics Processor Unit (GPU); herein referred to as device under test (DUT). Testing was conducted at Massachusetts General Hospitals (MGH) Francis H. Burr Proton Therapy Center on April 9th, 2017 using 200-MeV protons. This testing trip was purposed to provide a baseline assessment of the radiation susceptibility of the DUT as no previous testing had been conducted on this component.
Single-Event Effects (SEE) testing was conducted on the nVidia GTX 1050 Graphics Processor Unit (GPU); herein referred to as device under test (DUT). Testing was conducted at Massachusetts General Hospital's (MGH) Francis H. Burr Proton Therapy Center on April 28th, 2018 using 200-MeV protons. This testing trip was purposed to provide additional radiation susceptibility data from payloads compiled in Q3FY18. While not all radiation-induced errors are critical, the effects on the application need to be considered. More so, failure of the device and an inability to reset itself should be considered detrimental to the application. Radiation effects on electronic components are a significant reliability issue for systems intended for space.
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.
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With increased sensor data rates, and limited downlink capability, NASA missions have increased demands for onboard processing for applications ranging from synthetic aperture radar (SAR) data reduction to hyperspectral image processing and recognition, and even artificial intelligence (AI). Graphics Processor Units (GPUs) offer an attractive processing architecture for many of the applications due to their massive parallelism. As no radiation hardened GPU devices currently exist, any near term GPU-based onboard processors must use commercially available devices. To address this need NASA GSFC is collaborating with Cubic Aerospace Incorporated to, (a) characterize the capability of GPUs to meet the demands of a candidate onboard processing application, thereby demonstrating their ability to improve mission performance, reduce spacecraft SWaP, and potentially enable new missions, and (b) evaluate the radiation tolerance of capable COTS GPU devices to determine their suitability for spaceflight applications and understand any mitigations that are needed. A candidate onboard processing image has been prototyped and evaluated on a commercial GPU board and has demonstrated significantly increased processing throughput. Radiation tests for commercial GPU devices are planned for early fiscal year 2019.
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The Goddard Earth Observing System 5 (GEOS-5) is the atmospheric model used by the Global Modeling and Assimilation Office (GMAO) for a variety of applications, from long-term climate prediction at relatively coarse resolution, to data assimilation and numerical weather prediction, to very high-resolution cloud-resolving simulations. GEOS-5 is being ported to a graphics processing unit (GPU) cluster at the NASA Center for Climate Simulation (NCCS). By utilizing GPU co-processor technology, we expect to increase the throughput of GEOS-5 by at least an order of magnitude, and accelerate the process of scientific exploration across all scales of global modeling, including: The large-scale, high-end application of non-hydrostatic, global, cloud-resolving modeling at 10- to I-kilometer (km) global resolutions Intermediate-resolution seasonal climate and weather prediction at 50- to 25-km on small clusters of GPUs Long-range, coarse-resolution climate modeling, enabled on a small box of GPUs for the individual researcher After being ported to the GPU cluster, the primary physics components and the dynamical core of GEOS-5 have demonstrated a potential speedup of 15-40 times over conventional processor cores. Performance improvements of this magnitude reduce the required scalability of 1-km, global, cloud-resolving models from an unfathomable 6 million cores to an attainable 200,000 GPU-enabled cores.
Background Oriented Schlieren (BOS) is a widely used technique that provides density gradient information in flow fields of interest, without imposing stringent optical quality requirements on the facility/experiment windows and/or optics used in the BOS setup. Typically, the BOS reference image is acquired before the test begins (flow off) and then the "live" image data are acquired during the actual testing/experiment (flow on). The raw BOS image data, while displayed in real-time as they are acquired from the camera, unfortunately provide little if any visual indication of the density gradients in the flow. Generally, the "live" images must be processed off-line after the testing is completed, providing no indication of the success of the BOS setup and no feedback on the operational success of the test. Advances in computer processing hardware enables the implementation of real-time processing and display of the BOS image data. Two different approaches to implementing the real-time BOS (RT-BOS) processing capability are described herein. First, a traditional multi-core Central Processing Unit (CPU) based approach using scheduled parallel threads is used to build a RT-BOS processing engine. In the second approach, a Graphical Processing Unit (GPU) approach is used to costruct a RT-BOS processing engine. Generally, high core count CPU processors can provide a useful processing rate for RT-BOS. However, the GPU based approach exceeds the processing capability of the CPU approach, at a fraction of the cost. The GPU approach places no restrictions on the Host PC processing capability, except that it be capable of acquiring the BOS image data from the camera in real-time.
In this work, a novel graphics processing unit (GPU) distributed sharing model for hybrid many-core architectures is introduced and employed in the acceleration of a production-level computational fluid dynamics (CFD) code. The latest generation graphics hardware allows multiple processor cores to simultaneously share a single GPU through concurrent kernel execution. This feature has allowed the NASA FUN3D code to be accelerated in parallel with up to four processor cores sharing a single GPU. For codes to scale and fully use resources on these and the next generation machines, codes will need to employ some type of GPU sharing model, as presented in this work. Findings include the effects of GPU sharing on overall performance. A discussion of the inherent challenges that parallel unstructured CFD codes face in accelerator-based computing environments is included, with considerations for future generation architectures. This work was completed by the author in August 2010, and reflects the analysis and results of the time.
MARLEY is a simulation tool that helps scientists study how low-energy neutrinos interact with matter. To work properly, MARLEY uses random numbers thousands of times in each simulation. These random numbers are important for modeling things like how neutrinos collide with atoms and what particles they produce. Right now, MARLEY runs on a regular computer processor (CPU) and uses a built-in random number generator called the Mersenne Twister. This setup works, but it can be slow, especially when trying to simulate many events. This research focuses on making MARLEY run faster by moving the random number generation and some of the repetitive calculations from the CPU to a graphics processing unit (GPU), which can handle many tasks at the same time. We use CUDA (a tool for programming NVIDIA GPUs) and cuRAND (a GPU-based random number library) to test faster alternatives to the current random number system. We compare different GPU-based generators, like curand_mtgp32, xorwow, and philox, to see which ones are the quickest and still give reliable results. Early tests show that using the GPU can make MARLEY simulations much faster. This project not only helps improve current simulation performance but also moves closer to a full simulation chain where all stages can run on modern GPU hardware.
Computational device families are converging and multiple EEE components are required for a complete HPC subsystem. The components of this “processor enclave” are sensitive to radiation effects and therefore must be characterized for mission assurance. NEPP’s standardized approach to testing the Processor Enclave includes math, graphics, and AI and Machine Learning test vectors and device hardware capable of these applications.
Caused by historical separation and driven by the requirements of the PC gaming industry, Graphics Processing Units (GPUs) have evolved to massive parallel processing systems which entered the area of non-graphic related applications. Although a single processing core on the GPU is much slower and provides less functionality than its counterpart on the CPU, the huge number of these small processing entities outperforms the classical processors when the application can be parallelized. Thus, in recent years various radio astronomical projects have started to make use of this technology either to realize the correlator on this platform or to establish the post-processing pipeline with GPUs. Therefore, the feasibility of GPUs as a choice for a VLBI correlator is being investigated, including pros and cons of this technology. Additionally, a GPU based software correlator will be reviewed with respect to energy consumption/GFlop/sec and cost/GFlop/sec.
On-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. At JPL, a novel, adaptive and predictive technique for lossless compression of hyperspectral data, named the Fast Lossless (FL) algorithm, was recently developed. This technique uses an adaptive filtering method and achieves state-of-the-art performance in both compression effectiveness and low complexity. Because of its outstanding performance and suitability for real-time onboard hardware implementation, the FL compressor is being formalized as the emerging CCSDS Standard for Lossless Multispectral & Hyperspectral image compression. The FL compressor is well-suited for parallel hardware implementation. A GPU hardware implementation was developed for FL targeting the current state-of-the-art GPUs from NVIDIA(Trademark). The GPU implementation on a NVIDIA(Trademark) GeForce(Trademark) GTX 580 achieves a throughput performance of 583.08 Mbits/sec (44.85 MSamples/sec) and an acceleration of at least 6 times a software implementation running on a 3.47 GHz single core Intel(Trademark) Xeon(Trademark) processor. This paper describes the design and implementation of the FL algorithm on the GPU. The massively parallel implementation will provide in the future a fast and practical real-time solution for airborne and space applications.
The TES-8 was jettisoned from the International Space Station on January 31, 2019. As an orbital laboratory and 8th in on-going series, the design makes use of a standard set of interfaces and safety features that permit rapid re-flight. On this flight, an advanced Exo-Brake is flown with de-orbit targeting capability that will engender sample return capability from LEO platforms. A Virtual Reality data recording system uses stereo imaging and efficient data-compression with an NVIDIA GPU (Graphics Processing Unit) to permit compression and transmission of very large data files. An SDR (Software Defined Radio) will download data to the NEN (Near Earth Network) for the first time - demonstrating potential use in cis-lunar space using S-band. For the first time, a comparison will be made regarding the functionality of the Iridium and Globalstar short burst data modems - as essential communication tools for future nano-sat projects. Lastly, the 7 micro-processors and 4 cameras provide an excellent learning platform for university students and NASA young professionals.
The AMD Radeon Embedded e9170 Graphics Processing Unit (GPU), notably the e9173 Peripheral Component Interconnect Express (PCIE) variant, is of interest to Artemis generation programs with requirements for graphics rendering, compute, artificial intelligence (Ai) with a constraints-requiring piece-part procurement and power consumption of less than 50W. In addition to collecting heavy ion data on this device, a secondary purpose of this test campaign was to validate video capture hardware and software workflows used with GPU, microprocessor and system-on-chip device testing. Five (5) test patterns from the NEPP Processor Enclave (NPE) test suite were used with the e9173. The test patterns covered the operating system’s (OS) idle contribution towards the cross section, matrix math using tensorflow-rocm, two artificial intelligence models developed at NASA GSFC, and an industry standard GPU benchmarking application called Mesa GLXGears.
We present a cost-effective scalable real-time wavefront control architecture based on off-the-shelf graphics processing units hosted in an ultra-low latency, high-bandwidth interconnect PC cluster environment composed of modules written in the component-oriented language of nesC. The architecture enables full-matrix reconstruction of the wavefront at up to 2 KHz with latency under 250 us for the PALM-3000 adaptive optics systems, a state-of-the-art upgrade on the 5.1 meter Hale Telescope that consists of a 64 x 64 subaperture Shack-Hartmann wavefront sensor and a 3368 active actuator high order deformable mirror in series with a 241 active actuator tweeter DM. The architecture can easily scale up to support much larger AO systems at higher rates and lower latency.
The data analysis tool of choice for many Sun-Earth Connection missions is the Interactive Data Language (IDL) by ITT VIS. The increasing amount of data produced by these missions and the increasing complexity of image processing algorithms requires access to higher computing power. Parallel computing is a cost-effective way to increase the speed of computation, but algorithms oftentimes have to be modified to take advantage of parallel systems. Enhancing IDL to work on clusters gives scientists access to increased performance in a familiar programming environment. The goal of this project was to enable IDL applications to benefit from both computing clusters as well as graphics processing units (GPUs) for accelerating data analysis tasks. The tool suite developed in this project enables scientists now to solve demanding data analysis problems in IDL that previously required specialized software, and it allows them to be solved orders of magnitude faster than on conventional PCs. The tool suite consists of three components: (1) TaskDL, a software tool that simplifies the creation and management of task farms, collections of tasks that can be processed independently and require only small amounts of data communication; (2) mpiDL, a tool that allows IDL developers to use the Message Passing Interface (MPI) inside IDL for problems that require large amounts of data to be exchanged among multiple processors; and (3) GPULib, a tool that simplifies the use of GPUs as mathematical coprocessors from within IDL. mpiDL is unique in its support for the full MPI standard and its support of a broad range of MPI implementations. GPULib is unique in enabling users to take advantage of an inexpensive piece of hardware, possibly already installed in their computer, and achieve orders of magnitude faster execution time for numerically complex algorithms. TaskDL enables the simple setup and management of task farms on compute clusters. The products developed in this project have the potential to interact, so one can build a cluster of PCs, each equipped with a GPU, and use mpiDL to communicate between the nodes and GPULib to accelerate the computations on each node.