72,489 research outputs found
High-performance Kernel Machines with Implicit Distributed Optimization and Randomization
In order to fully utilize "big data", it is often required to use "big
models". Such models tend to grow with the complexity and size of the training
data, and do not make strong parametric assumptions upfront on the nature of
the underlying statistical dependencies. Kernel methods fit this need well, as
they constitute a versatile and principled statistical methodology for solving
a wide range of non-parametric modelling problems. However, their high
computational costs (in storage and time) pose a significant barrier to their
widespread adoption in big data applications.
We propose an algorithmic framework and high-performance implementation for
massive-scale training of kernel-based statistical models, based on combining
two key technical ingredients: (i) distributed general purpose convex
optimization, and (ii) the use of randomization to improve the scalability of
kernel methods. Our approach is based on a block-splitting variant of the
Alternating Directions Method of Multipliers, carefully reconfigured to handle
very large random feature matrices, while exploiting hybrid parallelism
typically found in modern clusters of multicore machines. Our implementation
supports a variety of statistical learning tasks by enabling several loss
functions, regularization schemes, kernels, and layers of randomized
approximations for both dense and sparse datasets, in a highly extensible
framework. We evaluate the ability of our framework to learn models on data
from applications, and provide a comparison against existing sequential and
parallel libraries.Comment: Work presented at MMDS 2014 (June 2014) and JSM 201
Designing Algorithms for Optimization of Parameters of Functioning of Intelligent System for Radionuclide Myocardial Diagnostics
The influence of the number of complex components of Fast Fourier transformation in analyzing the polar maps of radionuclide examination of myocardium at rest and stress on the functional efficiency of the system of diagnostics of pathologies of myocardium was explored, and there were defined their optimum values in the information sense, which allows increasing the efficiency of the algorithms of forming the diagnostic decision rules by reducing the capacity of the dictionary of features of recognition.The information-extreme sequential cluster algorithms of the selection of the dictionary of features, which contains both quantitative and category features were developed and the results of their work were compared. The modificatios of the algorithms of the selection of the dictionary were suggested, which allows increasing both the search speed of the optimal in the information sense dictionary and reducing its capacity by 40 %. We managed to get the faultless by the training matrix decision rules, the accuracy of which is in the exam mode asymptotically approaches the limit.It was experimentally confirmed that the implementation of the proposed algorithm of the diagnosing system training has allowed to reduce the minimum representative volume of the training matrix from 300 to 81 vectors-implementations of the classes of recognition of the functional myocardium state
Polly's Polyhedral Scheduling in the Presence of Reductions
The polyhedral model provides a powerful mathematical abstraction to enable
effective optimization of loop nests with respect to a given optimization goal,
e.g., exploiting parallelism. Unexploited reduction properties are a frequent
reason for polyhedral optimizers to assume parallelism prohibiting dependences.
To our knowledge, no polyhedral loop optimizer available in any production
compiler provides support for reductions. In this paper, we show that
leveraging the parallelism of reductions can lead to a significant performance
increase. We give a precise, dependence based, definition of reductions and
discuss ways to extend polyhedral optimization to exploit the associativity and
commutativity of reduction computations. We have implemented a
reduction-enabled scheduling approach in the Polly polyhedral optimizer and
evaluate it on the standard Polybench 3.2 benchmark suite. We were able to
detect and model all 52 arithmetic reductions and achieve speedups up to
2.21 on a quad core machine by exploiting the multidimensional
reduction in the BiCG benchmark.Comment: Presented at the IMPACT15 worksho
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