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The use of accelerators in high-performance computing is increasing. The most commonly used accelerator is the graphics processing unit (GPU) because of its low cost and massively parallel performance. The two most common programming environments for GPU accelerators are CUDA and OpenCL. While CUDA runs natively only on NVIDIA GPUs, OpenCL is an open(More)
—The proliferation of heterogeneous computing systems has led to increased interest in parallel architectures and their associated programming models. One of the most promising models for heterogeneous computing is the accelerator model, and one of the most cost-effective, high-performance accelerators currently available is the general-purpose, graphics(More)
The use of hardware accelerators in high-performance computing has grown increasingly prevalent, particularly due to the growth of graphics processing units (GPUs) as general-purpose (GPGPU) accelerators. Much of this growth has been driven by NVIDIA's CUDA ecosystem for developing GPGPU applications on NVIDIA hardware. However, with the increasing(More)
The proliferation of heterogeneous computing systems has led to increased interest in parallel architectures and their associated programming models. One of the most promising models for heterogeneous computing is the accelerator model, and one of the most cost-e↵ective, high-performance accelerators currently available is the general-purpose, graphics(More)
In this paper we discuss a GPU implementation of a hybrid deterministic/Monte Carlo method for the solution of the neutron transport equation. The key feature is using GPUs to perform a Monte Carlo transport sweep as part of the evaluation of the nonlinear residual and Jacobian-vector product. We describe the algorithm and present some preliminary numerical(More)
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