Autor: Pietroń, M. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: The Java profiler based on byte code analysis and instrumentation for many-core hardware accelerators
Autorzy:: Pietroń, M.
Karwatowski, M.
Wiatr, K.
Powiązania:: https://bibliotekanauki.pl/articles/114614.pdf
Data publikacji:: 2015
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:: virtual machine
CUDA
GPU
profiling
parallel computing
Opis:: One of the most challenging issues in the case of many and multi-core architectures is how to exploit their potential computing power in legacy systems without a deep knowledge of their architecture. The analysis of static dependence and dynamic data dependences of a program run, can help to identify independent paths that could have been computed by individual parallel threads. The statistics of reusing the data and its size is also crucial in adapting the application in GPU many-core hardware architecture because of specific memory hierarchies. The proposed profiling system accomplishes static data analysis and computes dynamic dependencies for Java programs as well as recommends parts of source code with the highest potential for parallelization in GPU. Such an analysis can also provide starting point for automatic parallelization.
Źródło:: Measurement Automation Monitoring; 2015, 61, 7; 385-387
2450-2855
Pojawia się w:: Measurement Automation Monitoring
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 2.

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

Zmień widok

na półce

Skocz do pozycji: 3.

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

Zmień widok

na półce

Informacja

Wyszukujesz frazę "Pietroń, M." wg kryterium: Autor

Źródło danych

Dostawca treści

Kolekcja

Rok wydania

Wydawca

Temat

Autor

Typ dokumentu

Język