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Showing 1 to 20 of 30 for “"Graph Processing"”.
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Approximate failure recovery in distributed graph processing systems
Distributed graph processing systems are an emerging area of big data systems. As graphs continue to grow in size and prevalence, these systems must become faster and more scalable. However, after failures, distributed graph processing systems either largely rely on proactive fault tolerance …
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Topology-aware distributed graph processing for tightly-coupled clusters
… machine learning systems called distributed graph processing systems, and run them on NCSA Blue Waters. Partitioning the graph is key to achieving performance in distributed graph processing systems. We present new topology-aware partitioning techniques that better exploit the structure of …
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Using Workload Characterization to Guide High Performance Graph Processing
Graph analytics represent an important application domain widely used in many fields such as web graphs, social networks, and Bayesian networks. The sheer size of the graph data sets combined with the irregular nature of the underlying problem pose a significant challenge for performance, …
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Zorro: zero-cost reactive failure recovery in distributed graph processing
Distributed graph processing frameworks have become increasingly popular for processing large graphs. However, existing frameworks either lack the ability to recovery from failures or support proactive recovery methods. Proactive recovery methods like checkpointing incur high overheads during …
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An experimental comparison of partitioning strategies in distributed graph processing
… among partitioning strategies in distributed graph processing systems. To this end, we evaluate and characterize both the performance and resource usage of different partitioning strategies under various popular distributed graph processing systems, applications, input graphs, and execution …
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Mélange: Multi-tenant scheduling with adaptive eviction for graph processing clusters
… multi-tenant scheduler targeted towards graph processing jobs. Mélange supports job priorities and eviction, while attempting to avoid starvation. We propose novel ways of exploiting domain-specific knowledge to achieve better scheduling decisions for graph processing jobs. We evaluate …
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Cache and NUMA optimizations in a domain-specific language for graph processing
High-performance graph processing is challenging because the sizes and structures of real-world graphs can vary widely. Graph algorithms also have distinct performance characteristics that lead to different performance bottlenecks. Even though memory technologies such as CPU cache and non-uniform …
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Speeding-up graph processing on shared-memory platforms by optimizing scheduling and compute
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-12-01
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Dynamic Large-Scale Graph Processing over Data Streams with Community Detection as a Case Study
Processing large graphs provides invaluable insights for the industry and research alike. The applications range from e-commerce, web, and social networking to analyzing gene expressions and cellular signaling. While numerous graph processing solutions have been developed with the capability to …
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Systems and Techniques for Efficient Real-World Graph Analytics
Graphs are a natural way to model real-world entities and relationships between them, ranging from social networks and biological datasets to cloud computing infrastructure data lineage graphs. Queries over these large graphs often involve expensive subgraph traversals and complex analytical …
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High Performance Large Graph Analytics by Enhancing Locality
<p>Graphs are widely used in a variety of domains for representing entities and their relationship to each other. Graph analytics helps to understand, detect, extract and visualize insightful relationships between different entities. Graph analytics has a wide range of applications in various …
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Bridging Theory and Practice in Parallel Clustering
Large-scale graph processing is a fundamental tool in modern data mining, with wide-ranging applications in domains including social network analysis, bioinformatics, and machine learning. In particular, graph clustering, or community detection, is an important problem in graph processing that …
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Nuclear reactor multiphysics via bond graph formalism
… nuclear reactor multiphysics problems using bond graphs. Conventional multiphysics simulation paradigms normally use operator splitting, which treats the individual physics separately and exchanges the information at every time step. This approach has limited accuracy, and so recently, there has …
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Distributed Computing for Large-scale Graphs
… Since many of these sources can be modeled as graphs, many large-scale graph processing frameworks have been developed, from vertex-centric models such as pregel to more complex programming models that allow asynchronous computation, can tackle dynamism in the data and permit the usage of …
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SCALABLE GRAPH REPRESENTATIONAL LEARNING ALGORITHMS FOR NETWORK MEDICINE
… In this context, analyzing biomedical Knowledge Graphs that embrace bio- logical and medical concepts structured in ontologies and data generated from high- throughput bio-technologies represents a central Machine Learning and Computational Biology challenge. Indeed several compelling problems in …
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Parallel SPARQL Query Execution using Apache Spark
… techniques for RDF indexing and SPARQL query processing, the rapid growth in the size of RDF knowledge bases demands scalable techniques that can leverage the power of cluster computing. Big data ecosystems like Apache Spark provide new opportunities for designing scalable RDF indexing and …
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Investigation of bond graphs for nuclear reactor simulations
… multiphysics nuclear reactor problems using bond graphs. The conventional method of modeling the coupled multiphysics transients in nuclear reactors is operator splitting, which treats the single physics individually and exchanges the information at every time step. This approach has limited …
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Storage and processing systems for power-law graphs
Large graphs abound around us - online social networks, Web graphs, the Internet, citation networks, protein interaction networks, telephone call graphs, peer-to-peer overlay networks, electric power grid networks, etc. Many real- life graphs are power-law graphs. A fundamental challenge in today’s …
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Scaling overlapping community detection algorithms
… the underlying structure of communities in graphs with a greater degree of correctness. This specific area of graph mining is known as community detection. It is one of the most critical components of graph mining. It helps in understanding the underlying properties of the graph. While a lot …
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Exploiting cost-performance tradeoffs for modern cloud systems
… As another example, run-time performance of graph analytics jobs sharing a multi-tenant cluster can be made better by trading of the cost of replication of the input graph data-set stored in the associated distributed file system. Today cloud system providers have to manually tune the system …
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