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Relativistic numerical hydrodynamics is an important tool in high energy nuclear science. However, such simulations are extremely demanding in terms of computing power. This paper focuses on improving the speed of solving the Riemann problem with the MUSTA-FORCE algorithm by employing the CUDA parallel programming model. We also propose a new approach to 3D finite difference algorithms, which employ...
The newest GPU Kepler architecture offers a reconfigurable L1 cache per Streaming Multiprocessor with different cache size and cache associativity. Both these cache parameters affect the overall performance of cache intensive algorithms, i.e. the algorithms which intensively reuse the data. In this paper, we analyze the impact of different configurations of L1 cache on execution of matrix multiplication...
The aim of the paper is to show how to design and implement fast parallel algorithms for Linear Congruential, Lagged Fibonacci and Wichmann-Hill pseudorandom number generators. The new algorithms employ the divide-and-conquer approach for solving linear recurrence systems. They are implemented on multi GPU-accelerated systems using CUDA. Numerical experiments performed on a computer system with two...
In this paper we describe a parallel implicit method based on radial basis functions (RBF) for surface reconstruction. Practical applicability of RBF methods is hindered by their high computational demand, that requires the solution of linear systems of size equal to the number of data points. The implementation of our method relies on parallel scientific libraries and is designed for exploiting Graphic...
Non-Local Means (NLM) algorithm is widely considered as a state-of-the-art denoising filter in many research fields. High computational complexity led to implementations on Graphic Processor Unit (GPU) architectures, which achieve reasonable running times by filtering, slice-by-slice, 3D datasets with a 2D NLM approach. Here we present a fully 3D NLM implementation on a multi-GPU architecture and...
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