Exploiting GPUs to accelerate clustering algorithms

• Main Authors: Al-Ayyoub, Mahmoud, Yaseen, Qussai, Shehab, Moahmmed A., Jararweh, Yaser, Albalas, Firas, Benkhelifa, Elhadj
• Format: Conference Proceeding
• Language: English
• Subjects: Big Data; Clustering algorithms; Graphics processing units; Linear programming; Mathematical model; Parallel processing; Servers
• ISSN: 2161-5330
• DOI: 10.1109/AICCSA.2016.7945796

Big data is a main problem for data mining methods. Fortunately, the rapid advances in affordable high performance computing platforms such as the Graphics Processing Unit (GPU) have helped researchers in reducing the execution time of many algorithms including data mining algorithms. This paper discusses the utilization of the parallelism capabilities of the GPU to improve the the performance of two common clustering algorithms, which are K-Means (KM) and Fuzzy C-Means (FCM) algorithms. Two main parallelism approaches are presented: pure and hybrid. These different versions are tested under different settings including two different GPU-equipped machines (a laptop and a server). The results show excellent improvement gains of the hybrid implementations compared with the pure parallel and sequential ones. On the laptop, the best gains of the hybrid implementations compared with the sequential ones are 11.3X for KM and 10.9X for FCM. As for the server, the best gains are 13.5X for KM and 16.3X for FCM. Moreover, the paper explores the usage of a recent memory management technique for GPU called Unified Memory (UM). The results show a decrease in the performance gain of the hybrid implementations that is equal to 44% for hybrid version of KM and 61% for FCM. On the other hand, the use of UM does introduce a small advantage for the pure parallel implementation.