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Pregel: A system for largescale graph processing
 IN SIGMOD
, 2010
"... Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs—in some cases billions of vertices, trillions of edges—poses challenges to their efficient processing. In this paper we present a computational model ..."
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Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs—in some cases billions of vertices, trillions of edges—poses challenges to their efficient processing. In this paper we present a computational model suitable for this task. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges or mutate graph topology. This vertexcentric approach is flexible enough to express a broad set of algorithms. The model has been designed for efficient, scalable and faulttolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier. Distributionrelated details are hidden behind an abstract API. The result is a framework for processing large graphs that is expressive and easy to program.
Experimental Study on SpeedUp Techniques for Timetable Information Systems
 PROCEEDINGS OF THE 7TH WORKSHOP ON ALGORITHMIC APPROACHES FOR TRANSPORTATION MODELING, OPTIMIZATION, AND SYSTEMS (ATMOS 2007
, 2007
"... During the last years, impressive speedup techniques for DIJKSTRA’s algorithm have been developed. Unfortunately, recent research mainly focused on road networks. However, fast algorithms are also needed for other applications like timetable information systems. Even worse, the adaption of recentl ..."
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During the last years, impressive speedup techniques for DIJKSTRA’s algorithm have been developed. Unfortunately, recent research mainly focused on road networks. However, fast algorithms are also needed for other applications like timetable information systems. Even worse, the adaption of recently developed techniques to timetable information is more complicated than expected. In this work, we check whether results from road networks are transferable to timetable information. To this end, we present an extensive experimental study of the most prominent speedup techniques on different types of inputs. It turns out that recently developed techniques are much slower on graphs derived from timetable information than on road networks. In addition, we gain amazing insights into the behavior of speedup techniques in general.
A New GPUbased Approach to the Shortest Path Problem
 In High Performance Computing and Simulation (HPCS), 2013 International Conference on
, 2013
"... Abstract—The SingleSource Shortest Path (SSSP) problem arises in many different fields. In this paper we present a GPUbased version of the Crauser et al. SSSP algorithm. Our work significantly speeds up the computation of the SSSP, not only with respect to the CPUbased version, but also to other ..."
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Abstract—The SingleSource Shortest Path (SSSP) problem arises in many different fields. In this paper we present a GPUbased version of the Crauser et al. SSSP algorithm. Our work significantly speeds up the computation of the SSSP, not only with respect to the CPUbased version, but also to other stateoftheart GPU implementation based on Dijkstra, due to Martı́n et al. Both GPU implementations have been evaluated using the last Nvidia architecture (Kepler). Our experimental results show that the new GPUCrauser algorithm leads to speedups from 13 × to 220 × with respect to the CPU version and a performance gain of up to 17 % with respect the GPUMartı́n algorithm. Keywords—Dijkstra; GPU; Kepler; NSSP; Parallel Algorithms; SSSP
A Study of Different Parallel Implementations of Single Source Shortest Path Algorithms
"... We present a study of parallel implementations of single source shortest path (SSSP) algorithms. In the last three decades number of parallel SSSP algorithms have been developed and implemented on the different type of machines. We have divided some of these implementations into two groups, first ar ..."
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We present a study of parallel implementations of single source shortest path (SSSP) algorithms. In the last three decades number of parallel SSSP algorithms have been developed and implemented on the different type of machines. We have divided some of these implementations into two groups, first are those where parallelization is achieved in the internal operations of sequential SSSP algorithm and second are where an actual graph is divided into subgraphs, and serial SSSP algorithm executes parallel on separate processing units for each subgraph. These parallel implementations have used PRAM, CRAY supercomputer, dynamically reconfigurable processor and Graphics processing unit as platform to run them.