Oded Green
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View article: Scalable Katz Ranking Computation in Large Static and Dynamic Graphs
Scalable Katz Ranking Computation in Large Static and Dynamic Graphs Open
Network analysis defines a number of centrality measures to identify the most central nodes in a network. Fast computation of those measures is a major challenge in algorithmic network analysis. Aside from closeness and betweenness, Katz c…
View article: HashGraph—Scalable Hash Tables Using a Sparse Graph Data Structure
HashGraph—Scalable Hash Tables Using a Sparse Graph Data Structure Open
In this article, we introduce HashGraph, a new scalable approach for building hash tables that uses concepts taken from sparse graph representations—hence, the name HashGraph. HashGraph introduces a new way to deal with hash-collisions tha…
View article: Scalable Hash Table for NUMA Systems
Scalable Hash Table for NUMA Systems Open
Hash tables are used in a plethora of applications, including database operations, DNA sequencing, string searching, and many more. As such, there are many parallelized hash tables targeting multicore, distributed, and accelerator-based sy…
View article: ButterFly BFS -- An Efficient Communication Pattern for Multi Node Traversals
ButterFly BFS -- An Efficient Communication Pattern for Multi Node Traversals Open
Breadth-First Search (BFS) is a building block used in a wide array of graph analytics and is used in various network analysis domains: social, road, transportation, communication, and much more. Over the last two decades, network sizes ha…
View article: ButterFly BFS -- An Efficient Communication Pattern for Multi Node\n Traversals
ButterFly BFS -- An Efficient Communication Pattern for Multi Node\n Traversals Open
Breadth-First Search (BFS) is a building block used in a wide array of graph\nanalytics and is used in various network analysis domains: social, road,\ntransportation, communication, and much more. Over the last two decades,\nnetwork sizes…
View article: Performance Impact of Memory Channels on Sparse and Irregular Algorithms
Performance Impact of Memory Channels on Sparse and Irregular Algorithms Open
Graph processing is typically considered to be a memory-bound rather than compute-bound problem. One common line of thought is that more available memory bandwidth corresponds to better graph processing performance. However, in this work w…
View article: Self-stabilizing Connected Components
Self-stabilizing Connected Components Open
For the problem of computing the connected components of a graph, this paper considers the design of algorithms that are resilient to transient hardware faults, like bit Hips. More specifically, it applies the technique of self-stabilizati…
View article: Skip the Intersection: Quickly Counting Common Neighbors on Shared-Memory Systems
Skip the Intersection: Quickly Counting Common Neighbors on Shared-Memory Systems Open
Counting common neighbors between all vertex pairs in a graph is a fundamental operation, with uses in similarity measures, link prediction, graph compression, community detection, and more. Current shared-memory approaches either rely on …
View article: Fast and Adaptive List Intersections on the GPU
Fast and Adaptive List Intersections on the GPU Open
List intersections are ubiquitous and can be found in a wide range of applications, including triangle counting and finding the maximal k-truss, both of which are part of the HPEC Static Graph Challenge. For many graph based problems it is…
View article: Logarithmic Radix Binning and Vectorized Triangle Counting
Logarithmic Radix Binning and Vectorized Triangle Counting Open
Triangle counting is a building block for numerous graph applications and given the fact that graphs continue to grow in size, its scalability is important. As such, numerous algorithms have been designed for triangle counting - some of wh…
View article: Scalable Katz Ranking Computation in Large Static and Dynamic Graphs
Scalable Katz Ranking Computation in Large Static and Dynamic Graphs Open
Network analysis defines a number of centrality measures to identify the most central nodes in a network. Fast computation of those measures is a major challenge in algorithmic network analysis. Aside from closeness and betweenness, Katz c…
View article: Scalable Katz Ranking Computation in Large Static and Dynamic Graphs
Scalable Katz Ranking Computation in Large Static and Dynamic Graphs Open
Network analysis defines a number of centrality measures to identify the most central nodes in a network. Fast computation of those measures is a major challenge in algorithmic network analysis. Aside from closeness and betweenness, Katz c…
View article: Design and implementation of parallel PageRank on multicore platforms
Design and implementation of parallel PageRank on multicore platforms Open
Runtime-reconfigurable software coupled with reconfigurable hardware is\nhighly desirable as a means towards maximizing runtime efficiency without\ncompromising programmability. Compilers for such software systems are extremely\ndifficult …
View article: Parallelized Kendall's Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors
Parallelized Kendall's Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors Open
Pairwise association measure is an important operation in data analytics. Kendall's tau coefficient is one widely used correlation coefficient identifying non-linear relationships between ordinal variables. In this paper, we investigated a…
View article: An Adaptive Parallel Algorithm for Computing Connected Components
An Adaptive Parallel Algorithm for Computing Connected Components Open
We present an efficient distributed memory parallel algorithm for computing connected components in undirected graphs based on Shiloach-Vishkin's PRAM approach. We discuss multiple optimization techniques that reduce communication volume a…
View article: An Adaptive Parallel Algorithm for Computing Connectivity.
An Adaptive Parallel Algorithm for Computing Connectivity. Open
We present an efficient distributed memory parallel algorithm for computing
connected components in undirected graphs based on Shiloach-Vishkin's PRAM
approach. We discuss multiple optimization techniques that reduce communication
volume a…