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

Fast, scalable, low-cost, and low-power execution of parallel graph algorithms is important for a wide variety of commercial and public sector applications. Breadth First Search (BFS) imposes an extreme burden on memory bandwidth and network communications and has been proposed as a benchmark that may be used to evaluate current and future parallel computers. Hardware […]

August 11, 2014 by hgpu

We present the results obtained by using an evolution of our CUDA-based solution for the exploration, via a Breadth First Search, of large graphs. This latest version exploits at its best the features of the Kepler architecture and relies on a 2D decomposition of the adjacency matrix to reduce the number of communications among the […]

August 9, 2014 by hgpu

Graphs that model social networks, numerical simulations, and the structure of the Internet are enormous and cannot be manually inspected. A popular metric used to analyze these networks is betweenness centrality, which has applications in community detection, power grid contingency analysis, and the study of the human brain. However, these analyses come with a high […]

August 1, 2014 by hgpu

Graphs play a very important role in the field of Science and Technology for finding the shortest distance between any two places. This Paper demonstrate the recent technology named as CUDA (Compute Unified Device Architecture) working for BFS Graph Algorithm. There are some Graph algorithms are fundamental to many disciplines and application areas. Large graphs […]

July 11, 2014 by hgpu

Graph is a widely used data structure and graph algorithms, such as breadth-first search (BFS), are regarded as key components in a great number of applications. Recent studies have attempted to accelerate graph algorithms on highly parallel graphics processing unit (GPU). Although many graph algorithms based on large graphs exhibit abundant parallelism, their performance on […]

July 10, 2014 by hgpu

Many problems in Computer Science can be modelled using graphs. Evaluating node centrality in complex networks, which can be considered equivalent to undirected graphs, provides an useful metric of the relative importance of each node inside the evaluated network. The knowledge on which the most central nodes are, has various applications, such as improving information […]

July 6, 2014 by hgpu

The exponential growth in bioinformatics data generation and the stagnation of processor frequencies in modern processors stress the need for efficient implementations that fully exploit the parallel capabilities offered by modern computers. This thesis focuses on parallel algorithms and implementations for bioinformatics problems. Various types of parallelism are described and exploited. This thesis presents applications […]

July 3, 2014 by hgpu

Medusa is a parallel graph processing system on graphics processors (GPUs). The core design of Medusa is to enable developers to leverage the massive parallelism and other hardware features of GPUs by writing sequential C/C++ code for a small set of APIs. This simplifies the implementation of parallel graph processing on the GPU. The runtime […]

June 19, 2014 by hgpu

Betweenness Centrality is a widely used graph analytic that has applications such as finding influential people in social networks, analyzing power grids, and studying protein interactions. However, its complexity makes its exact computation infeasible for large graphs of interest. Furthermore, networks tend to change over time, invalidating previously calculated results and encouraging new analyses regarding […]

June 3, 2014 by hgpu

Centrality metrics have shown to be highly correlated with the importance and loads of the nodes in a network. Given the scale of today’s social networks, it is essential to use efficient algorithms and high performance computing techniques for their fast computation. In this work, we exploit hardware and software vectorization in combination with fine-grain […]

March 29, 2014 by hgpu