In this work I learned more about of the language CUDA to evaluate the possibility to implement the parallel computing on GPU in some FLUKA's routine. The project focused on the generation of random numbers by analyzing how this was currently done by FLUKA's routine and how it was possible to reproduce and / or optimize by implementing the use of a GPU. During the work it was necessary to study the properties of the RNG algorithms, the best strategies for allocating memory in CUDA and benchmarking techniques
The future of high-performance computing is aligning itself towards the efficient use of highly para...
Context. Machine Learning is a complex and resource consuming process that requires a lot of computi...
GPU-accelerated computing drives current scientific research. Writing fast numeric algorithms for GP...
The future of computation is the GPU, i.e. the Graphical Processing Unit. The graphics cards have sh...
Abstract EFFICIENT RANDOM NUMBER GENERATION FOR FERMI CLASS GPUs by NIRODHA ABEYWARDANA JAN 2012 Adv...
The implementation of Monte Carlo simulation on the CUDA Fortran requires a fast random number gener...
Niniejsza praca dotyczy problemu uwspółbieżnienia procesu generacji liczb pseudolosowychz użyciem te...
International audienceWe examine the requirements and the available methods and software to provide ...
Programming Massively Parallel Processors discusses basic concepts about parallel programming and GP...
This research study is based on the growing interest towards graphical processing unit usability for...
Monte Carlo methods provide approximate numerical solutions to problems that would be difficult or i...
Computers almost always contain one or more central processing units (CPU), each of which processes ...
GPU-accelerated computing drives current scientific research. Writing fast numeric algorithms for GP...
This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. Firs...
High-performance streams of (pseudo) random numbers are crucial for the efficient implementation of ...
The future of high-performance computing is aligning itself towards the efficient use of highly para...
Context. Machine Learning is a complex and resource consuming process that requires a lot of computi...
GPU-accelerated computing drives current scientific research. Writing fast numeric algorithms for GP...
The future of computation is the GPU, i.e. the Graphical Processing Unit. The graphics cards have sh...
Abstract EFFICIENT RANDOM NUMBER GENERATION FOR FERMI CLASS GPUs by NIRODHA ABEYWARDANA JAN 2012 Adv...
The implementation of Monte Carlo simulation on the CUDA Fortran requires a fast random number gener...
Niniejsza praca dotyczy problemu uwspółbieżnienia procesu generacji liczb pseudolosowychz użyciem te...
International audienceWe examine the requirements and the available methods and software to provide ...
Programming Massively Parallel Processors discusses basic concepts about parallel programming and GP...
This research study is based on the growing interest towards graphical processing unit usability for...
Monte Carlo methods provide approximate numerical solutions to problems that would be difficult or i...
Computers almost always contain one or more central processing units (CPU), each of which processes ...
GPU-accelerated computing drives current scientific research. Writing fast numeric algorithms for GP...
This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. Firs...
High-performance streams of (pseudo) random numbers are crucial for the efficient implementation of ...
The future of high-performance computing is aligning itself towards the efficient use of highly para...
Context. Machine Learning is a complex and resource consuming process that requires a lot of computi...
GPU-accelerated computing drives current scientific research. Writing fast numeric algorithms for GP...