363 research outputs found
Computing fuzzy rough approximations in large scale information systems
Rough set theory is a popular and powerful machine learning tool. It is especially suitable for dealing with information systems that exhibit inconsistencies, i.e. objects that have the same values for the conditional attributes but a different value for the decision attribute. In line with the emerging granular computing paradigm, rough set theory groups objects together based on the indiscernibility of their attribute values. Fuzzy rough set theory extends rough set theory to data with continuous attributes, and detects degrees of inconsistency in the data. Key to this is turning the indiscernibility relation into a gradual relation, acknowledging that objects can be similar to a certain extent. In very large datasets with millions of objects, computing the gradual indiscernibility relation (or in other words, the soft granules) is very demanding, both in terms of runtime and in terms of memory. It is however required for the computation of the lower and upper approximations of concepts in the fuzzy rough set analysis pipeline. Current non-distributed implementations in R are limited by memory capacity. For example, we found that a state of the art non-distributed implementation in R could not handle 30,000 rows and 10 attributes on a node with 62GB of memory. This is clearly insufficient to scale fuzzy rough set analysis to massive datasets. In this paper we present a parallel and distributed solution based on Message Passing Interface (MPI) to compute fuzzy rough approximations in very large information systems. Our results show that our parallel approach scales with problem size to information systems with millions of objects. To the best of our knowledge, no other parallel and distributed solutions have been proposed so far in the literature for this problem
Fast Monte Carlo Simulations for Quality Assurance in Radiation Therapy
Monte Carlo (MC) simulation is generally considered to be the most accurate method for dose calculation in radiation therapy. However, it suffers from the low simulation efficiency (hours to days) and complex configuration, which impede its applications in clinical studies. The recent rise of MRI-guided radiation platform (e.g. ViewRay’s MRIdian system) brings urgent need of fast MC algorithms because the introduced strong magnetic field may cause big errors to other algorithms. My dissertation focuses on resolving the conflict between accuracy and efficiency of MC simulations through 4 different approaches: (1) GPU parallel computation, (2) Transport mechanism simplification, (3) Variance reduction, (4) DVH constraint. Accordingly, we took several steps to thoroughly study the performance and accuracy influence of these methods. As a result, three Monte Carlo simulation packages named gPENELOPE, gDPMvr and gDVH were developed for subtle balance between performance and accuracy in different application scenarios. For example, the most accurate gPENELOPE is usually used as golden standard for radiation meter model, while the fastest gDVH is usually used for quick in-patient dose calculation, which significantly reduces the calculation time from 5 hours to 1.2 minutes (250 times faster) with only 1% error introduced. In addition, a cross-platform GUI integrating simulation kernels and 3D visualization was developed to make the toolkit more user-friendly. After the fast MC infrastructure was established, we successfully applied it to four radiotherapy scenarios: (1) Validate the vender provided Co60 radiation head model by comparing the dose calculated by gPENELOPE to experiment data; (2) Quantitatively study the effect of magnetic field to dose distribution and proposed a strategy to improve treatment planning efficiency; (3) Evaluate the accuracy of the build-in MC algorithm of MRIdian’s treatment planning system. (4) Perform quick quality assurance (QA) for the “online adaptive radiation therapy” that doesn’t permit enough time to perform experiment QA. Many other time-sensitive applications (e.g. motional dose accumulation) will also benefit a lot from our fast MC infrastructure
X-ray CT Image Reconstruction on Highly-Parallel Architectures.
Model-based image reconstruction (MBIR) methods for X-ray CT use accurate
models of the CT acquisition process, the statistics of the noisy measurements,
and noise-reducing regularization to produce potentially higher quality images
than conventional methods even at reduced X-ray doses. They do this by
minimizing a statistically motivated high-dimensional cost function; the high
computational cost of numerically minimizing this function has prevented MBIR
methods from reaching ubiquity in the clinic. Modern highly-parallel hardware
like graphics processing units (GPUs) may offer the computational resources to
solve these reconstruction problems quickly, but simply "translating" existing
algorithms designed for conventional processors to the GPU may not fully
exploit the hardware's capabilities.
This thesis proposes GPU-specialized image denoising and image reconstruction
algorithms. The proposed image denoising algorithm uses group coordinate
descent with carefully structured groups. The algorithm converges very
rapidly: in one experiment, it denoises a 65 megapixel image in about 1.5
seconds, while the popular Chambolle-Pock primal-dual algorithm running on the
same hardware takes over a minute to reach the same level of accuracy.
For X-ray CT reconstruction, this thesis uses duality and group coordinate
ascent to propose an alternative to the popular ordered subsets (OS) method.
Similar to OS, the proposed method can use a subset of the data to update the
image. Unlike OS, the proposed method is convergent. In one helical CT
reconstruction experiment, an implementation of the proposed algorithm using
one GPU converges more quickly than a state-of-the-art algorithm converges
using four GPUs. Using four GPUs, the proposed algorithm reaches near
convergence of a wide-cone axial reconstruction problem with over 220 million
voxels in only 11 minutes.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113551/1/mcgaffin_1.pd
ExaGeoStatR: A Package for Large-Scale Geostatistics in R
Parallel computing in Gaussian process calculation becomes a necessity for
avoiding computational and memory restrictions associated with Geostatistics
applications. The evaluation of the Gaussian log-likelihood function requires
O(n^2) storage and O(n^3) operations where n is the number of geographical
locations. In this paper, we present ExaGeoStatR, a package for large-scale
Geostatistics in R that supports parallel computation of the maximum likelihood
function on shared memory, GPU, and distributed systems. The parallelization
depends on breaking down the numerical linear algebra operations into a set of
tasks and rendering them for a task-based programming model. ExaGeoStatR
supports several maximum likelihood computation variants such as exact,
Diagonal Super Tile (DST), and Tile Low-Rank (TLR) approximation besides
providing a tool to generate large-scale synthetic datasets which can be used
to test and compare different approximations methods. The package can be used
directly through the R environment without any C, CUDA, or MPIknowledge. Here,
we demonstrate the ExaGeoStatR package by illustrating its implementation
details, analyzing its performance on various parallel architectures, and
assessing its accuracy using both synthetic datasets and a sea surface
temperature dataset. The performance evaluation involves spatial datasets with
up to 250K observations
EDLaaS : Fully Homomorphic Encryption Over Neural Network Graphs
The authors would like to thank the British Biotechnology and Biological Sciences Research Council (BBSRC) in collaboration with Berry Gardens Growers and the University of Lincoln for funding and support.Preprin
Solving shallow-water systems in 2D domains using Finite Volume methods and multimedia SSE instructions
AbstractThe goal of this paper is to construct efficient parallel solvers for 2D hyperbolic systems of conservation laws with source terms and nonconservative products. The method of lines is applied: at every intercell a projected Riemann problem along the normal direction is considered which is discretized by means of well-balanced Roe methods. The resulting 2D numerical scheme is explicit and first-order accurate. In [M.J. Castro, J.A. GarcĂa, J.M. González, C. Pares, A parallel 2D Finite Volume scheme for solving systems of balance laws with nonconservative products: Application to shallow flows, Comput. Methods Appl. Mech. Engrg. 196 (2006) 2788–2815] a domain decomposition method was used to parallelize the resulting numerical scheme, which was implemented in a PC cluster by means of MPI techniques.In this paper, in order to optimize the computations, a new parallelization of SIMD type is performed at each MPI thread, by means of SSE (“Streaming SIMD Extensions”), which are present in common processors. More specifically, as the most costly part of the calculations performed at each processor consists of a huge number of small matrix and vector computations, we use the Intel© Integrated Performance Primitives small matrix library. To make easy the use of this library, which is implemented using assembler and SSE instructions, we have developed a C++ wrapper of this library in an efficient way. Some numerical tests were carried out to validate the performance of the C++ small matrix wrapper. The specific application of the scheme to one-layer Shallow-Water systems has been implemented on a PC’s cluster. The correct behavior of the one-layer model is assessed using laboratory data
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