GPU-accelerated subgraph enumeration on partitioned graphs

Ministry of Education, Singapore under its Academic Research Funding Tier

Algorithms for Extracting Frequent Episodes in the Process of Temporal Data Mining

An important aspect in the data mining process is the discovery of patterns having a great influence on the studied problem. The purpose of this paper is to study the frequent episodes data mining through the use of parallel pattern discovery algorithms. Parallel pattern discovery algorithms offer better performance and scalability, so they are of a great interest for the data mining research community. In the following, there will be highlighted some parallel and distributed frequent pattern mining algorithms on various platforms and it will also be presented a comparative study of their main features. The study takes into account the new possibilities that arise along with the emerging novel Compute Unified Device Architecture from the latest generation of graphics processing units. Based on their high performance, low cost and the increasing number of features offered, GPU processors are viable solutions for an optimal implementation of frequent pattern mining algorithmsFrequent Pattern Mining, Parallel Computing, Dynamic Load Balancing, Temporal Data Mining, CUDA, GPU, Fermi, Thread

DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives

We present a new parallel algorithm for probabilistic graphical model optimization. The algorithm relies on data-parallel primitives (DPPs), which provide portable performance over hardware architecture. We evaluate results on CPUs and GPUs for an image segmentation problem. Compared to a serial baseline, we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare our performance to a reference, OpenMP-based algorithm, and find speedups of up to 7X (CPU).Comment: LDAV 2018, October 201

Efficient sampling algorithms for approximate temporal motif counting

Ministry of Education, Singapore under its Academic Research Funding Tier

Graph Processing on GPU

Ph.DDOCTOR OF PHILOSOPH

A Systematic Survey of General Sparse Matrix-Matrix Multiplication

SpGEMM (General Sparse Matrix-Matrix Multiplication) has attracted much attention from researchers in fields of multigrid methods and graph analysis. Many optimization techniques have been developed for certain application fields and computing architecture over the decades. The objective of this paper is to provide a structured and comprehensive overview of the research on SpGEMM. Existing optimization techniques have been grouped into different categories based on their target problems and architectures. Covered topics include SpGEMM applications, size prediction of result matrix, matrix partitioning and load balancing, result accumulating, and target architecture-oriented optimization. The rationales of different algorithms in each category are analyzed, and a wide range of SpGEMM algorithms are summarized. This survey sufficiently reveals the latest progress and research status of SpGEMM optimization from 1977 to 2019. More specifically, an experimentally comparative study of existing implementations on CPU and GPU is presented. Based on our findings, we highlight future research directions and how future studies can leverage our findings to encourage better design and implementation.Comment: 19 pages, 11 figures, 2 tables, 4 algorithm