- Tytuł:
- Exploiting multi-core and many-core parallelism for subspace clustering
- Autorzy:
-
Datta, Amitava
Kaur, Amardeep
Lauer, Tobias
Chabbouh, Sami - Powiązania:
- https://bibliotekanauki.pl/articles/331126.pdf
- Data publikacji:
- 2019
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
data mining
subspace clustering
multicore processor
many core processor
GPU computing
eksploracja danych
procesor wielordzeniowy
obliczenia GPU - Opis:
- Finding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of the dataset, where a subspace is a subset of dimensions of the data. But the exponential increase in the number of subspaces with the dimensionality of data renders most of the algorithms inefficient as well as ineffective. Moreover, these algorithms have ingrained data dependency in the clustering process, which means that parallelization becomes difficult and inefficient. SUBSCALE is a recent subspace clustering algorithm which is scalable with the dimensions and contains independent processing steps which can be exploited through parallelism. In this paper, we aim to leverage the computational power of widely available multi-core processors to improve the runtime performance of the SUBSCALE algorithm. The experimental evaluation shows linear speedup. Moreover, we develop an approach using graphics processing units (GPUs) for fine-grained data parallelism to accelerate the computation further. First tests of the GPU implementation show very promising results.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2019, 29, 1; 81-91
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł