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ParaGraphE: A Library for Parallel Knowledge Graph Embedding
[article]
2017
arXiv
pre-print
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We name our framework as ParaGraphE, which provides a library for parallel knowledge graph embedding. ...
Recently, many methods [1, 5, 3, 2, 6] have been proposed to deal with this problem, but existing single-thread implementations of them are time-consuming for large-scale knowledge graphs. ...
We name our framework as ParaGraphE, which provides a library for parallel knowledge graph embedding. ...
arXiv:1703.05614v3
fatcat:myujopmdijhtldfhii6b2ldlsq
Algorithm Parallelization Using Software Design Patterns, an Embedded Case Study Approach
2013
2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing
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First, an application is implemented with a minimum on algorithm modifications using the Map-Reduce design pattern. Next, the same algorithm is rewritten using the graph theory. ...
Parallel Design Patterns can help in the migration process from legacy sequential to high-performing parallel code. Therefore we propose a layered model of parallel design patterns. ...
A sequential variant of the Graph Theory implementation will be used as reference implementation for computing the speedup of the parallel Graph Theory algorithm. ...
doi:10.1109/3pgcic.2013.80
dblp:conf/3pgcic/VinckeLCPSB13
fatcat:fwxyknpltzfwfjsbwjqkgyshpe
Improving the expressiveness of deep learning frameworks with recursion
2018
Proceedings of the Thirteenth EuroSys Conference on - EuroSys '18
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2018 pre-print arXiv:1809.00832v1 fatcat:rg3l4og5onbndhw6lwktnlr56i
In this paper, we add recursion to the programming model of existing frameworks by complementing their design with recursive execution of dataflow graphs as well as additional APIs for recursive definitions ...
between nodes for efficient execution based on parallel computation. ...
Analysis of Recursive Graphs: Parallelization The performance difference between the iterative and recursive implementation of the same recursive neural network mainly comes from the parallelization of ...
doi:10.1145/3190508.3190530
dblp:conf/eurosys/JeongJKYC18
fatcat:yjt6dn2sw5fdppk664dtgliqg4
From Scilab to multicore embedded systems: Algorithms and methodologies
2012
2012 International Conference on Embedded Computer Systems (SAMOS)
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This paper presents the methodology and algorithms for the creation of parallel software written in Scilab source code for multicore embedded processors in the context of the "Architecture oriented paraLlelization ...
for high performance embedded Multicore systems using scilAb" (ALMA) EU FP7 project. ...
In fact, MIP has been used before for automatic parallelization of embedded software [28] . ...
doi:10.1109/samos.2012.6404184
dblp:conf/samos/GoulasAVVGKDMGDMSHSOBRSKM12
fatcat:ldkdwbhc5batjfkzi6vphfvxbq
Exploring Single Source Shortest Path Parallelization on Shared Memory Accelerators
2016
Proceedings of the 19th International Workshop on Software and Compilers for Embedded Systems - SCOPES '16
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performance/watt, but calls for efficient parallel SSSP implementation. ...
In this work we provide a detailed exploration of the ∆-stepping algorithm performance on a representative heterogeneous embedded system, TI Keystone II, considering the impact of several parallelization ...
The emerging trend towards the adoption of low-power parallel accelerators in embedded systems 1 opens new opportunities to deliver superior performance/watt, but calls for efficient parallel implementation ...
doi:10.1145/2906363.2915925
dblp:conf/scopes/PalossiM16
fatcat:ptnriyia7fc7xdvfrri2lk4bnm
A Multi-Level Parallel Implementation of a Program for Finding Frequent Patterns in a Large Sparse Graph
2007
2007 IEEE International Parallel and Distributed Processing Symposium
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The parallel implementation required the addition of 21 directives and about 50 accompanying lines of code, in an original code of about 15,000 lines. ...
embeddings. ...
The OpenMP work was greatly accelerated by discussions with John Baron of SGI and Jay Hoeflinger and Grant Haab of Intel, especially concerning the use of the taskq/task constructs. ...
doi:10.1109/ipdps.2007.370404
dblp:conf/ipps/ReinhardtK07
fatcat:xqgt4v44xbacdf5q6fn4dpuxki
Pangolin: An Efficient and Flexible Graph Pattern Mining System on CPU and GPU
[article]
2020
arXiv
pre-print
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There is growing interest in graph pattern mining (GPM) problems such as motif counting. ...
GPM systems have been developed to provide unified interfaces for programming algorithms for these problems and for running them on parallel systems. ...
Compared to graph analytics, GPM algorithms are more difficult to implement on parallel platforms; for example, unlike graph analytics algorithms, they usually generate enormous amounts of intermediate ...
arXiv:1911.06969v2
fatcat:jqj6f2gxxrcylfybqt4mkq6xsm
FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming
[article]
2022
arXiv
pre-print
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The architecture features a configurable dataflow optimized for simultaneous computation of node embedding, edge embedding, and message passing, which is generally applicable to all models. ...
Our implementation code and on-board measurement are publicly available on GitHub. ...
ACKNOWLEDGEMENTS The authors would like to thank Zihang Qiao for his contributions to the development of PNA and DGN, as well as Parima Mehta for her contribution to the virtual node model. ...
arXiv:2204.13103v1
fatcat:5mbf62bdp5achp7cx5xb2psz2a
Force2Vec: Parallel force-directed graph embedding
[article]
2020
arXiv
pre-print
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A graph embedding algorithm embeds a graph into a low-dimensional space such that the embedding preserves the inherent properties of the graph. ...
We make Force2Vec highly parallel by mapping its core computations to linear algebra and utilizing multiple levels of parallelism available in modern processors. ...
Other graph embedding and visualization algorithms can be implemented in this framework. • We present a highly-parallel algorithm that uses multicore processors and memory efficiently. ...
arXiv:2009.10035v1
fatcat:bm4dieccxnctfm4qcisdon46py
Hardware and Software Implementations of Prim's Algorithm for Efficient Minimum Spanning Tree Computation
[chapter]
2013
IFIP Advances in Information and Communication Technology
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Prim's algorithm is based on graph traversals, which are inherently hard to parallelize. ...
We study two algorithmic variants and compare their performance against implementations on desktop-class and embedded CPUs. ...
Parallelism Analysis Depending on the implementation of Prim's algorithm, it can exhibit some parallelism. ...
doi:10.1007/978-3-642-38853-8_14
fatcat:wjd3ttgqrfhllchsroeoul34dy
Pangolin
2020
Proceedings of the VLDB Endowment
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There is growing interest in graph pattern mining (GPM) problems such as motif counting. ...
GPM systems have been developed to provide unified interfaces for programming algorithms for these problems and for running them on parallel systems. ...
Compared to graph analytics, GPM algorithms are more difficult to implement on parallel platforms; for example, unlike graph analytics algorithms, they usually generate enormous amounts of intermediate ...
doi:10.14778/3389133.3389137
fatcat:53fbcjljv5hpxa5yeqv4ioviou
PecanPy: a fast, efficient, and parallelized Python implementation of node2vec
2021
Bioinformatics
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We have developed PecanPy, a new Python implementation of node2vec that uses cache-optimized compact graph data structures and precomputing/parallelization to result in fast, high-quality node embeddings ...
Learning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks. ...
Mancuso, Anna Yannakopoulos, and the rest of the Krishnan Lab for valuable discussions and feedback on the manuscript. We are grateful to Charles T. ...
doi:10.1093/bioinformatics/btab202
pmid:33760066
pmcid:PMC8504639
fatcat:ed6psnd4b5hq5fekiaqfs6aapm
PecanPy: a fast, efficient, and parallelized Python implementation of node2vec
[article]
2020
bioRxiv
pre-print
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The file type is application/pdf.
We have developed PecanPy, a new Python implementation of node2vec that uses cache-optimized compact graph data structures and precomputing/parallelization to result in fast, high-quality node embeddings ...
Learning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks. ...
Mancuso, Anna Yannakopoulos, and the rest of the Krishnan Lab for valuable discussions and feedback on the software and manuscript. ...
doi:10.1101/2020.07.23.218487
fatcat:ukgmp3keobdwngfew3pxwuwluu
Dense edge-disjoint embedding of complete binary trees in interconnection networks
2000
Theoretical Computer Science
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For the mesh and the shu e-exchange graphs each edge is regarded as two parallel (or anti-parallel) edges. ...
The embeddings have the following properties: paths of the tree are mapped onto edge-disjoint paths of the host graph and at most two tree nodes (just one of which is a leaf) are mapped onto each host ...
For the mesh and shu e-exchange graphs these embeddings required replacing each edge by a pair of parallel (or anti-parallel) edges. A number of problems remain open. ...
doi:10.1016/s0304-3975(00)00066-9
fatcat:zgilbrqsubfbvgyzebbyvajhou
Graph Data Mining with Arabesque
2017
Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17
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Arabesque provides a simple programming model to express graph data mining computations, and a highly scalable and efficient implementation of this model, scaling to billions of subgraphs on hundreds of ...
Most current parallel graph analytics systems do not provide good support for graph data mining. One notable exception is Arabesque, a system that was built specifically to support graph data mining. ...
Arabesque explores the input graph in a series of bulk synchronous parallel (BSP) steps, and maintains a set of candidate embeddings at each step. ...
doi:10.1145/3035918.3058742
dblp:conf/sigmod/HusseinGDTASSMA17
fatcat:yfesugez3rcqhjsbusxixb423m
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