10,622 research outputs found
Model Coupling between the Weather Research and Forecasting Model and the DPRI Large Eddy Simulator for Urban Flows on GPU-accelerated Multicore Systems
In this report we present a novel approach to model coupling for
shared-memory multicore systems hosting OpenCL-compliant accelerators, which we
call The Glasgow Model Coupling Framework (GMCF). We discuss the implementation
of a prototype of GMCF and its application to coupling the Weather Research and
Forecasting Model and an OpenCL-accelerated version of the Large Eddy Simulator
for Urban Flows (LES) developed at DPRI.
The first stage of this work concerned the OpenCL port of the LES. The
methodology used for the OpenCL port is a combination of automated analysis and
code generation and rule-based manual parallelization. For the evaluation, the
non-OpenCL LES code was compiled using gfortran, fort and pgfortran}, in each
case with auto-parallelization and auto-vectorization. The OpenCL-accelerated
version of the LES achieves a 7 times speed-up on a NVIDIA GeForce GTX 480
GPGPU, compared to the fastest possible compilation of the original code
running on a 12-core Intel Xeon E5-2640.
In the second stage of this work, we built the Glasgow Model Coupling
Framework and successfully used it to couple an OpenMP-parallelized WRF
instance with an OpenCL LES instance which runs the LES code on the GPGPI. The
system requires only very minimal changes to the original code. The report
discusses the rationale, aims, approach and implementation details of this
work.Comment: This work was conducted during a research visit at the Disaster
Prevention Research Institute of Kyoto University, supported by an EPSRC
Overseas Travel Grant, EP/L026201/
The finite element machine: An experiment in parallel processing
The finite element machine is a prototype computer designed to support parallel solutions to structural analysis problems. The hardware architecture and support software for the machine, initial solution algorithms and test applications, and preliminary results are described
Solution of partial differential equations on vector and parallel computers
The present status of numerical methods for partial differential equations on vector and parallel computers was reviewed. The relevant aspects of these computers are discussed and a brief review of their development is included, with particular attention paid to those characteristics that influence algorithm selection. Both direct and iterative methods are given for elliptic equations as well as explicit and implicit methods for initial boundary value problems. The intent is to point out attractive methods as well as areas where this class of computer architecture cannot be fully utilized because of either hardware restrictions or the lack of adequate algorithms. Application areas utilizing these computers are briefly discussed
Towards developing robust algorithms for solving partial differential equations on MIMD machines
Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system
Parallel performance prediction for multigrid codes on distributed memory architectures
We propose a model for describing the parallel performance
of multigrid software on distributed memory architectures. The goal of the model is to allow reliable predictions to be made as to the execution time of a given code on a large number of processors, of a given parallel system, by only benchmarking the code on small numbers of processors. This has potential applications for the scheduling of jobs in a Grid computing environment where reliable predictions as to execution times on different systems will be valuable. The model is tested for two different multigrid codes running on two different parallel architectures and the
results obtained are discussed
An ADMM Based Framework for AutoML Pipeline Configuration
We study the AutoML problem of automatically configuring machine learning
pipelines by jointly selecting algorithms and their appropriate
hyper-parameters for all steps in supervised learning pipelines. This black-box
(gradient-free) optimization with mixed integer & continuous variables is a
challenging problem. We propose a novel AutoML scheme by leveraging the
alternating direction method of multipliers (ADMM). The proposed framework is
able to (i) decompose the optimization problem into easier sub-problems that
have a reduced number of variables and circumvent the challenge of mixed
variable categories, and (ii) incorporate black-box constraints along-side the
black-box optimization objective. We empirically evaluate the flexibility (in
utilizing existing AutoML techniques), effectiveness (against open source
AutoML toolkits),and unique capability (of executing AutoML with practically
motivated black-box constraints) of our proposed scheme on a collection of
binary classification data sets from UCI ML& OpenML repositories. We observe
that on an average our framework provides significant gains in comparison to
other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical
advantages of this framework
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