Big Data analytics has been approached exclusively from a data-parallel perspective, where data are partitioned to multiple workers ‐ threads or separate servers ‐ and model training is executed concurrently over different partitions, under various synchronization schemes that guarantee speedup and or convergence. The dual ‐ Big Model ‐ problem that, surprisingly, has received no attention in database analytics, is how to manage models with millions if not billions of parameters that do not fit in memory. This distinction in model representation changes fundamentally how in-database analytics tasks are carried out. In this paper, we introduce the first secondary storage array-relation dot-product join operator between a set of sparse arrays and a dense relation targeted. The paramount challenge in designing such an operator is how to optimally schedule access to the dense relation based on the sparse non-contiguous entries in the sparse arrays. We prove that this problem is NP-hard and propose a practical solution characterized by two important technical contributions— dynamic batch processing and array reordering. We execute extensive experiments over synthetic and real data that confirm the minimal overhead the operator incurs when sufficient memory is available and the graceful degradation it suffers as memory resources become scarce. Moreover, dot-product join achieves an order of magnitude reduction in execution time for Big Model analytics over alternative in-database solutions.
 RasDaMan is a universal — i.e., domain-independent — array DBMS for multidimensional arrays of arbitrary size and structure. A declarative, SQL-based array query language offers flexible retrieval and manipulation. Efficient server-based query evaluation is enabled by an intelligent optimizer and a streamlined storage architecture based on flexible array tiling and compression. RasDaMan is being used in several international projects for the management of geo and healthcare data of various dimensionality.  Scaling up the sparse matrix-vector multiplication kernel on modern Graphics Processing Units (GPU) has been at the heart of numerous studies in both academia and industry. In this article we present a novel non-parametric, self-tunable, approach to data representation for computing this kernel, particularly targeting sparse matrices representing power-law graphs. Using real web graph data, we show how our representation scheme, coupled with a novel tiling algorithm, can yield significant benefits over the current state of the art GPU efforts on a number of core data mining algorithms such as PageRank, HITS and Random Walk with Restart. SciDB [4, 3] is a new open-source data management system intended primarily for use in application domains that involve very large (petabyte) scale array data; for example, scientific applications such as astronomy, remote sensing and climate modeling, bio-science information management, risk management systems in financial applications, and the analysis of web log data. In this talk we will describe our set of motivating examples and use them to explain the features of SciDB. We then briefly give an overview of the project 'in flight', explaining our novel storage manager, array data model, query language, and extensibility frameworks. Intel Xeon Phi is a recently released high-performance coprocessor which features 61 cores each supporting 4 hardware threads with 512-bit wide SIMD registers achieving a peak theoretical performance of 1Tflop s in double precision. Its design differs from classical modern processors; it comes with a large number of cores, the 4-way hyperthreading capability allows many applications to saturate the massive memory bandwidth, and its large SIMD capabilities allow to reach high computation throughput. The core of many scientific applications involves the multiplication of a large, sparse matrix with a single or multiple dense vectors which are not compute-bound but memory-bound. In this paper, we investigate the performance of the Xeon Phi coprocessor for these sparse linear algebra kernels. We highlight the important hardware details and show that Xeon Phi’s sparse kernel performance is very promising and even better than that of cutting-edge CPUs and GPUs. We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from cell phones to supercomputers confronts parallelism of unprecedented scale. To fully unleash the potential of these systems, the HPC community must develop multicore specific optimization methodologies for important scientific computations. In this work, we examine sparse matrix-vector multiply (SpMV) - one of the most heavily used kernels in scientific computing - across a broad spectrum of multicore designs. Our experimental platform includes the homogeneous AMD dual-core and Intel quad-core designs, the heterogeneous STI Cell, as well as the first scientific study of the highly multithreaded Sun Niagara2. We present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations. Additionally, we present key insights into the architectural tradeoffs of leading multicore design strategies, in the context of demanding memory-bound numerical algorithms. Eigensolvers are important tools for analyzing and mining useful information from scale-free graphs. Such graphs are used in many applications and can be extremely large. Unfortunately, existing parallel eigensolvers do not scale well for these graphs due to the high communication overhead in the parallel matrix-vector multiplication (MatVec). We develop a MatVec algorithm based on 2D edge partitioning that significantly reduces the communication costs and embed it into a popular eigensolver library. We demonstrate that the enhanced eigensolver can attain two orders of magnitude performance improvement compared to the original on a state-of-art massively parallel machine. We illustrate the performance of the embedded MatVec by computing eigenvalues of a scale-free graph with 300 million vertices and 5 billion edges, the largest scale-free graph analyzed by any in-memory parallel eigensolver, to the best of our knowledge.