Improving the scalability of parallel N-body applications with an event driven constraint based execution model

The scalability and efficiency of graph applications are significantly constrained by conventional systems and their supporting programming models. Technology trends like multicore, manycore, and heterogeneous system architectures are introducing further challenges and possibilities for emerging application domains such as graph applications. This paper explores the space of effective parallel execution of ephemeral graphs that are dynamically generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The workloads are expressed using the semantics of an Exascale computing execution model called ParalleX. For comparison, results using conventional execution model semantics are also presented. We find improved load balancing during runtime and automatic parallelism discovery improving efficiency using the advanced semantics for Exascale computing.Comment: 11 figure

Accelerated Iterative Algorithms with Asynchronous Accumulative Updates on a Heterogeneous Cluster

In recent years with the exponential growth in web-based applications the amount of data generated has increased tremendously. Quick and accurate analysis of this \u27big data\u27 is indispensable to make better business decisions and reduce operational cost. The challenges faced by modern day data centers to process big data are multi fold: to keep up the pace of processing with increased data volume and increased data velocity, deal with system scalability and reduce energy costs. Today\u27s data centers employ a variety of distributed computing frameworks running on a cluster of commodity hardware which include general purpose processors to process big data. Though better performance in terms of big data processing speed has been achieved with existing distributed computing frameworks, there is still an opportunity to increase processing speed further. FPGAs, which are designed for computationally intensive tasks, are promising processing elements that can increase processing speed. In this thesis, we discuss how FPGAs can be integrated into a cluster of general purpose processors running iterative algorithms and obtain high performance. In this thesis, we designed a heterogeneous cluster comprised of FPGAs and CPUs and ran various benchmarks such as PageRank, Katz and Connected Components to measure the performance of the cluster. Performance improvement in terms of execution time was evaluated against a homogeneous cluster of general purpose processors and a homogeneous cluster of FPGAs. We built multiple four-node heterogeneous clusters with different configurations by varying the number of CPUs and FPGAs. We studied the effects of load balancing between CPUs and FPGAs. We obtained a speedup of 20X, 11.5X and 2X for PageRank, Katz and Connected Components benchmarks on a cluster cluster configuration of 2 CPU + 2 FPGA for an unbalancing ratio against a 4-node homogeneous CPU cluster. We studied the effect of input graph partitioning, and showed that when the input is a Multilevel-KL partitioned graph we obtain an improvement of 11%, 26% and 9% over randomly partitioned graph for Katz, PageRank and Connected Components benchmarks on a 2 CPU + 2 FPGA cluster

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Impact of Asynchronism on GPU Accelerated Parallel Iterative Computations

International audienceWe study the impact of asynchronism on parallel iterative algorithms in the particular context of local clusters of workstations including GPUs. The application test is a classical PDE problem of advection-diffusion-reaction in 3D. We propose an asynchronous version of a previously developed PDE solver using GPUs for the inner computations. The algorithm is tested with two kinds of clusters, a homogeneous one and a heterogeneous one (with different CPUs and GPUs)

A Comparison of Big Data Frameworks on a Layered Dataflow Model

In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models, for which only informal (and often confusing) semantics is generally provided, all share a common underlying model, namely, the Dataflow model. The Dataflow model we propose shows how various tools share the same expressiveness at different levels of abstraction. The contribution of this work is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm), thus making it easier to understand high-level data-processing applications written in such frameworks. Second, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level.Comment: 19 pages, 6 figures, 2 tables, In Proc. of the 9th Intl Symposium on High-Level Parallel Programming and Applications (HLPP), July 4-5 2016, Muenster, German