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Wyszukujesz frazę "CUDA" wg kryterium: Temat


Wyświetlanie 1-4 z 4
Tytuł:
Accelerating SELECT WHERE and SELECT JOIN queries on a GPU
Autorzy:
Pietroń, M.
Russek, P.
Wiatr, K.
Powiązania:
https://bibliotekanauki.pl/articles/305797.pdf
Data publikacji:
2013
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
SQL
CUDA
relational databases
GPU
Opis:
This paper presents implementations of a few selected SQL operations using the CUDA programming framework on the GPU platform. Nowadays, the GPU’s parallel architectures give a high speed-up on certain problems. Therefore, the number of non-graphical problems that can be run and sped-up on the GPU still increases. Especially, there has been a lot of research in data mining on GPUs. In many cases it proves the advantage of offloading processing from the CPU to the GPU. At the beginning of our project we chose the set of SELECT WHERE and SELECT JOIN instructions as the most common operations used in databases. We parallelized these SQL operations using three main mechanisms in CUDA: thread group hierarchy, shared memories, and barrier synchronization. Our results show that the implemented highly parallel SELECT WHERE and SELECT JOIN operations on the GPU platform can be significantly faster than the sequential one in a database system run on the CPU.
Źródło:
Computer Science; 2013, 14 (2); 243-252
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The comparison of parallel sorting algorithms implemented on different hardware platforms
Autorzy:
Żurek, D.
Pietroń, M.
Wielgosz, M.
Wiatr, K.
Powiązania:
https://bibliotekanauki.pl/articles/305317.pdf
Data publikacji:
2013
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
parallel algorithms
GPU
OpenMP
CUDA
sorting networks
merge-sort
Opis:
Sorting is a common problem in computer science. There are a lot of well-known sorting algorithms created for sequential execution on a single processor. Recently, many-core and multi-core platforms have enabled the creation of wide parallel algorithms. We have standard processors that consist of multiple cores and hardware accelerators, like the GPU. Graphic cards, with their parallel architecture, provide new opportunities to speed up many algorithms. In this paper, we describe the results from the implementation of a few different parallel sorting algorithms on GPU cards and multi-core processors. Then, a hybrid algorithm will be presented, consisting of parts executed on both platforms (a standard CPU and GPU). In recent literature about the implementation of sorting algorithms in the GPU, a fair comparison between many core and multi-core platforms is lacking. In most cases, these describe the resulting time of sorting algorithm executions on the GPU platform and a single CPU core.
Źródło:
Computer Science; 2013, 14 (4); 679-691
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Grammar based multi-frontal solver for isogeometric analysis in 1d
Autorzy:
Kuźnik, K.
Paszyński, M
Calo, V.
Powiązania:
https://bibliotekanauki.pl/articles/305531.pdf
Data publikacji:
2013
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
graph grammar
direct solver
isogeometric finite element method
NVIDIA CUDA GPU
Opis:
In this paper, we present a multi-frontal direct solver for one-dimensional iso-geometric finite element method. The solver implementation is based on the graph grammar (GG) model. The GG model allows us to express the entire solver algorithm, including generation of frontal matrices, merging, and eliminations as a set of basic undividable tasks called graph grammar productions. Having the solver algorithm expressed as GG productions, we can find the partial order of execution and create a dependency graph, allowing for scheduling of tasks into shared memory parallel machine. We focus on the implementation of the solver with NVIDIA CUDA on the graphic processing unit (GPU). The solver has been tested for linear, quadratic, cubic, and higher-order B-splines, resulting in logarithmic scalability.
Źródło:
Computer Science; 2013, 14 (4); 589-613
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hypergrammar-based parallel multi-frontal solver for grids with point singularities
Autorzy:
Gurgul, P.
Paszyński, M.
Paszyńska, A.
Powiązania:
https://bibliotekanauki.pl/articles/305343.pdf
Data publikacji:
2015
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
hypergraph grammar
direct solver
h-adaptive finite element method
NVIDIA CUDA GPU
Opis:
This paper describes the application of hypergraph grammars to drive a linear computational cost solver for grids with point singularities. Such graph grammar productions are the first mathematical formalisms used to describe solver algorithms, and each indicates the smallest atomic task that can be executed in parallel, which is very useful in the case of parallel execution. In particular,the partial order of execution of graph grammar productions can be found, and the sets of independent graph grammar productions can be localized. They can be scheduled set by set into a shared memory parallel machine. The graph-grammar-based solver has been implemented with NVIDIA CUDA for GPU. Graph grammar productions are accompanied by numerical results for a 2D case. We show that our graph-grammar-based solver with a GPU accelerator is, by order of magnitude, faster than the state-of-the-art MUMPS solver.
Źródło:
Computer Science; 2015, 16 (1); 75-102
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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