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

Parallel Processing of Large Graphs

More and more large data collections are gathered worldwide in various IT systems. Many of them possess the networked nature and need to be processed and analysed as graph structures. Due to their size they require very often usage of parallel paradigm for efficient computation. Three parallel techniques have been compared in the paper: MapReduce, its map-side join extension and Bulk Synchronous Parallel (BSP). They are implemented for two different graph problems: calculation of single source shortest paths (SSSP) and collective classification of graph nodes by means of relational influence propagation (RIP). The methods and algorithms are applied to several network datasets differing in size and structural profile, originating from three domains: telecommunication, multimedia and microblog. The results revealed that iterative graph processing with the BSP implementation always and significantly, even up to 10 times outperforms MapReduce, especially for algorithms with many iterations and sparse communication. Also MapReduce extension based on map-side join usually noticeably presents better efficiency, although not as much as BSP. Nevertheless, MapReduce still remains the good alternative for enormous networks, whose data structures do not fit in local memories.Comment: Preprint submitted to Future Generation Computer System

Design of testbed and emulation tools

The research summarized was concerned with the design of testbed and emulation tools suitable to assist in projecting, with reasonable accuracy, the expected performance of highly concurrent computing systems on large, complete applications. Such testbed and emulation tools are intended for the eventual use of those exploring new concurrent system architectures and organizations, either as users or as designers of such systems. While a range of alternatives was considered, a software based set of hierarchical tools was chosen to provide maximum flexibility, to ease in moving to new computers as technology improves and to take advantage of the inherent reliability and availability of commercially available computing systems

Shape-based cost analysis of skeletal parallel programs

Institute for Computing Systems ArchitectureThis work presents an automatic cost-analysis system for an implicitly parallel skeletal programming language. Although deducing interesting dynamic characteristics of parallel programs (and in particular, run time) is well known to be an intractable problem in the general case, it can be alleviated by placing restrictions upon the programs which can be expressed. By combining two research threads, the “skeletal” and “shapely” paradigms which take this route, we produce a completely automated, computation and communication sensitive cost analysis system. This builds on earlier work in the area by quantifying communication as well as computation costs, with the former being derived for the Bulk Synchronous Parallel (BSP) model. We present details of our shapely skeletal language and its BSP implementation strategy together with an account of the analysis mechanism by which program behaviour information (such as shape and cost) is statically deduced. This information can be used at compile-time to optimise a BSP implementation and to analyse computation and communication costs. The analysis has been implemented in Haskell. We consider different algorithms expressed in our language for some example problems and illustrate each BSP implementation, contrasting the analysis of their efficiency by traditional, intuitive methods with that achieved by our cost calculator. The accuracy of cost predictions by our cost calculator against the run time of real parallel programs is tested experimentally. Previous shape-based cost analysis required all elements of a vector (our nestable bulk data structure) to have the same shape. We partially relax this strict requirement on data structure regularity by introducing new shape expressions in our analysis framework. We demonstrate that this allows us to achieve the first automated analysis of a complete derivation, the well known maximum segment sum algorithm of Skillicorn and Cai