36 research outputs found
Early Accurate Results for Advanced Analytics on MapReduce
Approximate results based on samples often provide the only way in which
advanced analytical applications on very massive data sets can satisfy their
time and resource constraints. Unfortunately, methods and tools for the
computation of accurate early results are currently not supported in
MapReduce-oriented systems although these are intended for `big data'.
Therefore, we proposed and implemented a non-parametric extension of Hadoop
which allows the incremental computation of early results for arbitrary
work-flows, along with reliable on-line estimates of the degree of accuracy
achieved so far in the computation. These estimates are based on a technique
called bootstrapping that has been widely employed in statistics and can be
applied to arbitrary functions and data distributions. In this paper, we
describe our Early Accurate Result Library (EARL) for Hadoop that was designed
to minimize the changes required to the MapReduce framework. Various tests of
EARL of Hadoop are presented to characterize the frequent situations where EARL
can provide major speed-ups over the current version of Hadoop.Comment: VLDB201
FPGA Acceleration of Mean Variance Framework for Optimal Asset Allocation
Asset classes respond differently to shifts in financial markets, thus an investor can minimize the risk of loss and maximize return of his portfolio by diversification of assets. Increasing the number of diversified assets in a financial portfolio significantly improves the optimal allocation of different assets giving better investment opportunities. However, a large number of assets require a significant amount of computation that only high performance computing can currently provide. Because of the highly parallel nature of Markowitzpsila mean variance framework (the most popular approximation approach for optimal asset allocation) an FPGA implementation of the framework can also provide the performance necessary to compute the optimal asset allocation with a large number of assets. In this work, we propose an FPGA implementation of Markowitzpsila mean variance framework and show it has a potential performance ratio of 221 times over a software implementation