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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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Cited by 11 (7 self)
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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 spaceefficient parallel algorithm for computing betweenness centrality in distributed memory
 In Proc. Int’l. Conf. on High Performance Computing (HiPC 2010
, 2010
"... Abstract—Betweenness centrality is a measure based on shortest paths that attempts to quantify the relative importance of nodes in a network. As computation of betweenness centrality becomes increasingly important in areas such as social network analysis, networks of interest are becoming too large ..."
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Cited by 5 (0 self)
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Abstract—Betweenness centrality is a measure based on shortest paths that attempts to quantify the relative importance of nodes in a network. As computation of betweenness centrality becomes increasingly important in areas such as social network analysis, networks of interest are becoming too large to fit in the memory of a single processing unit, making parallel execution a necessity. Parallelization over the vertex set of the standard algorithm, with a final reduction of the centrality for each vertex, is straightforward but requires Ω(V  2) storage. In this paper we present a new parallelizable algorithm with low spatial complexity that is based on the best known sequential algorithm. Our algorithm requires O(V  + E) storage and enables efficient parallel execution. Our algorithm is especially well suited to distributed memory processing because it can be implemented using coarsegrained parallelism. The presented time bounds for parallel execution of our algorithm on CRCW PRAM and on distributed memory systems both show good asymptotic performance. Experimental results with a distributed memory computer show the practical applicability of our algorithm. I.
Advanced Shortest Paths Algorithms on a MassivelyMultithreaded Architecture
"... We present a study of multithreaded implementations of Thorup’s algorithm for solving the Single Source Shortest Path (SSSP) problem for undirected graphs. Our implementations leverage the fledgling MultiThreaded Graph Library (MTGL) to perform operations such as finding connected components and ext ..."
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Cited by 3 (0 self)
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We present a study of multithreaded implementations of Thorup’s algorithm for solving the Single Source Shortest Path (SSSP) problem for undirected graphs. Our implementations leverage the fledgling MultiThreaded Graph Library (MTGL) to perform operations such as finding connected components and extracting induced subgraphs. To achieve good parallel performance from this algorithm, we deviate from several theoretically optimal algorithmic steps. In this paper, we present simplifications that perform better in practice, and we describe details of the multithreaded implementation that were necessary for scalability. We study synthetic graphs that model unstructured networks, such as social networks and economic transaction networks. Most of the recent progress in shortest path algorithms relies on structure that these networks do not have. In this work, we take a step back and explore the synergy between an elegant theoretical algorithm and an elegant computer architecture. Finally, we conclude with a prediction that this work will become relevant to shortest path computation on structured networks. 1.
Compact Graph Representations and Parallel Connectivity Algorithms for Massive Dynamic Network Analysis
"... Graphtheoretic abstractions are extensively used to analyze massive data sets. Temporal data streams from socioeconomic interactions, social networking web sites, communication traffic, and scientific computing can be intuitively modeled as graphs. We present the first study of novel highperformanc ..."
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Graphtheoretic abstractions are extensively used to analyze massive data sets. Temporal data streams from socioeconomic interactions, social networking web sites, communication traffic, and scientific computing can be intuitively modeled as graphs. We present the first study of novel highperformance combinatorial techniques for analyzing largescale information networks, encapsulating dynamic interaction data in the order of billions of entities. We present new data structures to represent dynamic interaction networks, and discuss algorithms for processing parallel insertions and deletions of edges in smallworld networks. With these new approaches, we achieve an average performance rate of 25 million structural updates per second and a parallel speedup of nearly 28 on a 64way Sun UltraSPARC T2 multicore processor, for insertions and deletions to a smallworld network of 33.5 million vertices and 268 million edges. We also design parallel implementations of fundamental dynamic graph kernels related to connectivity and centrality queries. Our implementations are freely distributed as part of the opensource SNAP (Smallworld Network Analysis and Partitioning) complex network analysis framework. 1.
Experimental Comparison of Emulated Lock‐free vs. Fine‐grain Locked Data Structures on the Cray XMT
, 2009
"... Three implementations of a concurrently‐updateable linked list were compared, one that emulates a lock‐free approach based on a compare‐and‐swap instruction, one that makes direct use of the Cray XMT’s full‐empty synchronization bits on every word of memory, and a third that uses the XMT’s atomic in ..."
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Three implementations of a concurrently‐updateable linked list were compared, one that emulates a lock‐free approach based on a compare‐and‐swap instruction, one that makes direct use of the Cray XMT’s full‐empty synchronization bits on every word of memory, and a third that uses the XMT’s atomic int_fetch_add instruction. The relative performance of the three implementations was experimentally compared on a 512‐processor XMT. The direct implementation approach performed up to twice as fast as the other two approaches under conditions of low contention, but the three implementations performed about the same when the amount of contention was high. The Cray XMT The Cray XMT is a heavily multithreaded architecture, with 128 hardware threads per
Customized Architectures for Faster Route Finding in GPSBased Navigation Systems
"... GPS based navigation systems became popular in dedicated handheld devices, and are now also found in modern cell phones, and other small personal devices. A key element of any navigation system is fast and effective route finding, and this depends heavily on Dijkstra’s shortest path algorithm. Dijks ..."
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GPS based navigation systems became popular in dedicated handheld devices, and are now also found in modern cell phones, and other small personal devices. A key element of any navigation system is fast and effective route finding, and this depends heavily on Dijkstra’s shortest path algorithm. Dijkstra’s algorithm is serial in nature; prior efforts to accelerate it through parallel processing have had almost no success. In this paper, we present a practical approach to extract smallscale parallelism by shifting priority queue operations to a secondary tightlycoupled processor. We obtain a substantial speedup on realworld graphs (in particular, road maps), allowing the development of navigation systems that are more responsive, and also lower in total power consumption. I.
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.
Parallel Computation of . . . Public Transportation Networks
, 2009
"... We present a novel algorithm for the socalled onetoall profile search problem in public transportation networks. It answers the question for all fastest connections between a given station S and any other station at any time of the day in a single query. Our approach exploits the facts that fir ..."
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We present a novel algorithm for the socalled onetoall profile search problem in public transportation networks. It answers the question for all fastest connections between a given station S and any other station at any time of the day in a single query. Our approach exploits the facts that first, timedependent traveltime functions in such networks can be represented as a special class of piecewise linear functions, and that second, only few connections from S are useful to travel far away. Introducing the connectionsetting property, we are able to extend Dijkstra’s algorithm in a sound manner. Moreover, we are able to parallelize our algorithm in a very natural way, yielding excellent speedups on standard multicore servers. By preprocessing important connections within the public transportation network, we also accelerate stationtostation queries. As a result, we are able to compute all relevant connections between two random stations in a complete public transportation network of a big city (Los Angeles) in less than 120 ms on average. This value is achieved on a standard multicore server.
Distributed SociaLite: A DatalogBased Language for LargeScale Graph Analysis
"... Largescale graph analysis is becoming important with the rise of worldwide social network services. Recently in SociaLite, we proposed extensions to Datalog to efficiently and succinctly implement graph analysis programs on sequential machines. This paper describes novel extensions and optimizatio ..."
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Largescale graph analysis is becoming important with the rise of worldwide social network services. Recently in SociaLite, we proposed extensions to Datalog to efficiently and succinctly implement graph analysis programs on sequential machines. This paper describes novel extensions and optimizations of SociaLite for parallel and distributed executions to support largescale graph analysis. With distributed SociaLite, programmers simply annotate how data are to be distributed, then the necessary communication is automatically inferred to generate parallel code for cluster of multicore machines. It optimizes the evaluation of recursive monotone aggregate functions using a delta stepping technique. In addition, approximate computation is supported in SociaLite, allowing programmers to trade off accuracy for less time and space. We evaluated SociaLite with six core graph algorithms used in many social network analyses. Our experiment with 64 Amazon EC2 8core instances shows that SociaLite programs performed within a factor of two with respect to ideal weak scaling. Compared to optimized Giraph, an opensource alternative of Pregel, SociaLite programs are 4 to 12 times faster across benchmark algorithms, and 22 times more succinct on average. As a declarative query language, SociaLite, with the help of a compiler that generates efficient parallel and approximate code, can be used easily to create many social apps that operate on largescale distributed graphs. 1.
Triangle Finding: How Graph Theory can Help the Semantic Web
"... Abstract. RDF data can be thought of as a graph where the subject and objects are vertices and the predicates joining them are edge attributes. Despite decades of research in graph theory, very little of this work has been applied to RDF data sets and it has been largely ignored by the Semantic Web ..."
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Abstract. RDF data can be thought of as a graph where the subject and objects are vertices and the predicates joining them are edge attributes. Despite decades of research in graph theory, very little of this work has been applied to RDF data sets and it has been largely ignored by the Semantic Web research community. We present a case study of triangle finding, where existing algorithms from graph theory provide excellent complexity bounds, growing at a significantly slower rate than algorithms used within existing RDF triple stores. In order to scale to large volumes of data, the Semantic Web community should look to the many existing graph algorithms. 1