55,740 research outputs found
A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
The overwhelmingly increasing amount of stored data has spurred researchers
seeking different methods in order to optimally take advantage of it which
mostly have faced a response time problem as a result of this enormous size of
data. Most of solutions have suggested materialization as a favourite solution.
However, such a solution cannot attain Real- Time answers anyhow. In this paper
we propose a framework illustrating the barriers and suggested solutions in the
way of achieving Real-Time OLAP answers that are significantly used in decision
support systems and data warehouses
Scalable Model-Based Management of Correlated Dimensional Time Series in ModelarDB+
To monitor critical infrastructure, high quality sensors sampled at a high
frequency are increasingly used. However, as they produce huge amounts of data,
only simple aggregates are stored. This removes outliers and fluctuations that
could indicate problems. As a remedy, we present a model-based approach for
managing time series with dimensions that exploits correlation in and among
time series. Specifically, we propose compressing groups of correlated time
series using an extensible set of model types within a user-defined error bound
(possibly zero). We name this new category of model-based compression methods
for time series Multi-Model Group Compression (MMGC). We present the first MMGC
method GOLEMM and extend model types to compress time series groups. We propose
primitives for users to effectively define groups for differently sized data
sets, and based on these, an automated grouping method using only the time
series dimensions. We propose algorithms for executing simple and
multi-dimensional aggregate queries on models. Last, we implement our methods
in the Time Series Management System (TSMS) ModelarDB (ModelarDB+). Our
evaluation shows that compared to widely used formats, ModelarDB+ provides up
to 13.7 times faster ingestion due to high compression, 113 times better
compression due to the adaptivity of GOLEMM, 630 times faster aggregates by
using models, and close to linear scalability. It is also extensible and
supports online query processing.Comment: 12 Pages, 28 Figures, and 1 Tabl
cuIBM -- A GPU-accelerated Immersed Boundary Method
A projection-based immersed boundary method is dominated by sparse linear
algebra routines. Using the open-source Cusp library, we observe a speedup
(with respect to a single CPU core) which reflects the constraints of a
bandwidth-dominated problem on the GPU. Nevertheless, GPUs offer the capacity
to solve large problems on commodity hardware. This work includes validation
and a convergence study of the GPU-accelerated IBM, and various optimizations.Comment: Extended paper post-conference, presented at the 23rd International
Conference on Parallel Computational Fluid Dynamics (http://www.parcfd.org),
ParCFD 2011, Barcelona (unpublished
Emergent Predication Structure in Hidden State Vectors of Neural Readers
A significant number of neural architectures for reading comprehension have
recently been developed and evaluated on large cloze-style datasets. We present
experiments supporting the emergence of "predication structure" in the hidden
state vectors of these readers. More specifically, we provide evidence that the
hidden state vectors represent atomic formulas where is a
semantic property (predicate) and is a constant symbol entity identifier.Comment: Accepted for Repl4NLP: 2nd Workshop on Representation Learning for
NL
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