A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure

High Performance Clustering of Social Images in a Map- Collective Programming Model

Large-scale iterative computations are common in many important data mining and machine learning algorithms needed in analytics and deep learning. In most of these applications, individual iterations can be specified as MapReduce computations, leading to the Iterative MapReduce programming model for efficient execution of data-intensive iterative computations interoperably between HPC and cloud environments. Further one needs additional communication patterns from those familiar in MapReduce and we base our initial architecture on collectives that integrate capabilities developed by the MPI and MapReduce communities. This leads us to the Map-Collective programming model which here we develop based on requirements of a range of applications by extending our existing Iterative MapReduce environment Twister. This paper studies the implications of large scale Social Image clustering where large scale problems study 10-100 million images represented as points in a high dimensional (up to 2048) vector space which need to be divided into up to 1-10 million clusters. This Kmeans application needs 5 stages in each iteration: Broadcast, Map, Shuffle, Reduce and Combine, and this paper focuses on collective communication stages where large data transfers demand performance optimization. By comparing and combining ideas from MapReduce and MPI communities, we show that a topologyaware and pipeline-based broadcasting method gives better performance than other MPI and (Iterative) MapReduce systems