Browsing by Author "Pandey, Varun"

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    Investigation of a Transactional Model for Incremental Parallel Computing in Dynamic Graphs
    (2017-05-25) Tripathi, Anand; Sharma, Rahul R.; Khandelwal, Manu; Mehta, Tanmay; Pandey, Varun; Parisineti, Charandeep
    In many applications involving dynamic graph structures one may be interested in continuously observing certain properties of interest. We present here the result of our investigation of utilizing a transactional model of parallel programming for supporting continuous queries on dynamic and evolving graph structures. The goal of our work is to continuously monitor a graph data structure as it is updated to check for the properties of interest. One approach for continuous monitoring is to re-execute the graph analytics program on the entire graph structure after it is updated. However, this approach can lead to high computation cost and latency in case of large graphs. An alternate approach is to execute the analytics program only initially, and then perform incremental computations for supporting continuous queries as the graph data is modified. In our model, the graph updates are performed as transactions, which trigger execution of a set of transactional tasks to perform computations for a continuous query. In our testbed system, the graph data is stored in the RAM of cluster nodes, and continuous queries involve parallel execution of transactional tasks on cluster nodes. Using a set of graph problems we illustrate this approach and its performance benefits for supporting continuous queries in dynamic graph data structure.
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    A Transactional Model for Parallel Programming of Graph Applications on Computing Clusters
    (IEEE, 2017) Tripathi, Anand; Padhye, Vinit; Sunkara, Tara Sasank; Tucker, Jeremy; Thirunavukarasu, BhagavathiDhass; Pandey, Varun; Sharma, Rahul R.
    We present here the results of our investigation of a transactional model of parallel programming on cluster computing systems. This model is specifically targeted for graph applications with the goal of harnessing unstructured parallelism inherently present in many such problems. In this model, tasks for vertex-centric computations are executed optimistically in parallel as serializable transactions. A key-value based globally shared object store is implemented in the main memory of the cluster nodes for storing the graph data. Task computations read and modify data in the distributed global store, without any explicitly programmed message-passing in the application code. Based on this model we developed a framework for parallel programming of graph applications on computing clusters. We present here the programming abstractions provided by this framework and its architecture. Using several graph problems we illustrate the simplicity of the abstractions provided by this model. These problems include graph coloring, k-nearest neighbors, and single-source shortest path computation. We also illustrate how incremental computations can be supported by this programming model. Using these problems we evaluate the transactional programming model and the mechanisms provided by this framework.