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

For large-scale graph analytics on the GPU, the irregularity of data access and control flow and the complexity of programming GPUs have been two significant challenges for developing a programmable high-performance graph library. "Gunrock", our graph-processing system, uses a high-level bulk-synchronous abstraction with traversal and computation steps, designed specifically for the GPU. Gunrock couples high […]

January 23, 2015 by hgpu

The availability of Graphics Processing Units (GPUs) with multicore architecture have enabled parallel computations using extensive multi-threading. Recent advancements in computer hardware have led to the usage of graphics processors for solving general purpose problems. Using GPUs for computation is a highly efficient and low-cost alternative as compared to currently available multicore Central Processing Units […]

January 16, 2015 by hgpu

Graph mining and data management has become a significant area because more and more new applications to various data mining problems in social networking, computational biology, chemical data analysis and drug discovery are emerging recently. Although traditional mining methods have been extended to process graphs, many graph applications still confront huge challenges due to continuous […]

December 14, 2014 by hgpu

Finding chordless cycles is an important theoretical problem in the Graph Theory area. It also can be applied to practical problems such as discover which predators compete for the same food in ecological networks. Motivated by the problem of theoretical interest and also by its significant practical importance, we present in this paper a parallel […]

October 22, 2014 by hgpu

Range searching is a primal problem in computational geometry with applications to database systems, mobile computing, geographical information systems, and the like. Defined simply, the problem is to preprocess a given a set of points in a d-dimensional space so that the points that lie inside an orthogonal query rectangle can be efficiently reported. Many […]

August 19, 2014 by hgpu

Frequent graph mining is an important though computationally hard problem because it requires enumerating possibly an exponential number of candidate subgraph patterns, and checking their presence in a database of graphs. In this paper, we propose a novel approach for parallel graph mining on GPUs, which have emerged as a relatively cheap but powerful architecture […]

August 19, 2014 by hgpu