Betweenness Centrality (BC) is steadily growing in popularity as a metrics of the influence of a vertex in a graph. The BC score of a vertex is proportional to the number of all-pairs-shortest-paths passing through it. However, complete and exact BC computation for a large-scale graph is an extraordinary challenge that requires high performance computing […]

February 3, 2016 by hgpu

High performance graph analytics are critical for a long list of application domains. In recent years, the rapid advancement of many-core processors, in particular graphical processing units (GPUs), has sparked a broad interest in developing high performance parallel graph programs on these architectures. However, the SIMT architecture used in GPUs places particular constraints on both […]

December 10, 2015 by hgpu

Modern Graphics Processing Units (GPUs) have high computation power and low cost. Recently, many applications in various fields have been computed powerfully on the GPU using CUDA. In this paper, we propose an efficient parallel algorithm for graph isomorphism which runs on the GPU using CUDA for matching large graphs. Parallelization of a sequential graph […]

December 4, 2015 by hgpu

Complex networks have received interest in a wide area of applications, ranging from road networks over hyperlink connections in the world wide web to interactions between people. Advanced algorithms are required for the generation as well as visualization of such graphs. In this work two graph algorithms, one for graph generation, the other for graph […]

October 6, 2015 by hgpu

We present a simple parallel algorithm to test chordality of graphs which is based on the parallel Lexicographical Breadth-First Search algorithm. In total, the algorithm takes time O(N) on N-threads machine and it performs work O(N^2), where N is the number of vertices in a graph. Our implementation of the algorithm uses a GPU environment […]

August 28, 2015 by hgpu

Graph algorithms are used in several domains such as social networking, biological sciences, computational geometry, and compilers, to name a few. It has been shown that they possess enough parallelism to keep several computing resources busy – even hundreds of cores on a GPU. Unfortunately, tuning their implementation for efficient execution on a particular hardware […]

June 16, 2015 by hgpu

We present a multi-GPU graph processing library that allows programmers to easily extend single-GPU graph algorithms to achieve scalable performance on large graph datasets with billions of edges. Our design only requires users to specify a few algorithm-dependent blocks, hiding most multi-GPU related implementation details. Our design effectively overlaps computation and data transfer and implements […]

April 23, 2015 by hgpu

Large scale-free graphs are famously difficult to process efficiently: the highly skewed vertex degree distribution makes it difficult to obtain balanced workload partitions for parallel processing. Our research instead aims to take advantage of vertex degree heterogeneity by partitioning the workload to match the strength of the individual computing elements in a hybrid architecture. This […]

March 18, 2015 by hgpu

The clustering coefficient and the transitivity ratio are concepts often used in network analysis, which creates a need for fast practical algorithms for counting triangles in large graphs. Previous research in this area focused on sequential algorithms, MapReduce parallelization, and fast approximations. In this paper we propose a parallel triangle counting algorithm for CUDA GPU. […]

March 3, 2015 by hgpu

Two well-known bipartite graph matching algorithms, the Gale-Shapley algorithm and the Hungarian (Kuhn-Munkres) algorithm, has been ported to run on General-Purpose Graphics Processing Units (GPGPU) using kernels written with the CUDA programming model. This was done with the goal of characterising and assessing the performance and behaviour of these matching algorithms on the GPU, and […]

February 23, 2015 by hgpu

Subgraph matching is the task of finding all matches of a query graph in a large data graph, which is known as an NP-complete problem. Many algorithms are proposed to solve this problem using CPUs. In recent years, Graphics Processing Units (GPUs) have been adopted to accelerate fundamental graph operations such as breadth-first search and […]

February 9, 2015 by hgpu

Due to increasingly large datasets, graph analytics – traversals, all-pairs shortest path computations, centrality measures, etc. – are becoming the focus of high-performance computing (HPC). Because HPC is currently dominated by many-core architectures (both CPUs and GPUs), new graph processing solutions have to be defined to efficiently use such computing resources. Prior work focuses on […]

January 23, 2015 by hgpu