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Results for “Accelerated”
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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Petawatt Laser Guiding and Electron Beam Acceleration to 8 GeV in a Laser-Heated Capillary Discharge Waveguide.
Guiding of relativistically intense laser pulses with peak power of 0.85 PW over 15 diffraction lengths was demonstrated by increasing the focusing strength of a capillary discharge waveguide using laser inverse bremsstrahlung heating. This allowed for the production of electron beams with quasimonoenergetic peaks up to 7.8 GeV, double the energy that was previously demonstrated. Charge was 5 pC at 7.8 GeV and up to 62 pC in 6 GeV peaks, and typical beam divergence was 0.2 mrad.
Exploring Spatial Indexing for Accelerated Feature Retrieval in HPC.
Abstract not provided.
Resistive Memory Device Requirements for a Neural Algorithm Accelerator.
Abstract not provided.
Portable Acceleration of Materials Modeling Software: CASTEP, GPUs, and OpenACC
Not provided.
Accelerating Genomic Data Analytics With Composable Hardware Acceleration Framework
Not provided.
Using a Robust Metadata Management System to Accelerate Scientific Discovery at Extreme Scales.
Abstract not provided.
Accelerated Distributed Energy Management for Microgrids
This paper considers the optimal energy management for a microgrid. The goal of optimal energy management is to minimize the total generation cost while meeting total demand and satisfying individual generator output limits. To achieve this goal, we develop a distributed algorithm based on the consensus theory and gradient estimation technique. Compared to the existing algorithms with diminishing step-sizes, the proposed algorithm enjoys a faster convergence speed due to the fixed step-size, and thus increases the computation speed, and reduces the computation and communication burden. The proposed distributed algorithm is applicable to both fixed and time-varying directed communication networks. We show that the convergence of the proposed distributed algorithm is achieved if the fixed communication network is strongly connected, or if the time-varying communication network is uniformly jointly strongly connected. Numerical simulation results are used to illustrate and demonstrate the effectiveness of the proposed algorithm.
Automatic Data Processing and Data Display System for the HERMES III Accelerator.
Abstract not provided.
MODERNIZATION OF THE MARX AND RIMFIRE TRIGGERING SYSTEMS FOR THE HERMES-III ACCELERATOR.
Abstract not provided.
Co-located Accelerated Testing of Module Level Power Electronics and Associated PV Panels.
Abstract not provided.
FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters
In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer paral- lelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation.