NAIS Lecturer, Peter Richtarik, will give a talk at the Strathclyde NA Seminar series
Title: How to climb a billion dimensional hill using a coin and a compass and count the steps before departure" : Parallel coordinate descent methods for big data optimization
3.15pm, Rm EM182, Heriot Watt. ‘On the Origins of Domain Decomposition Methods’, Martin Gander, University of Geneva. Domain decomposition methods have been developed in various contexts, and with very different goals in mind. I will start my presentation with the historical inventions of the Schwarz method, the Schur methods and Waveform Relaxation. I will show for a simple mode l problem how all these domain decomposition methods function, give precise results for the model problem, and also explain the most general convergence results available currently for these methods. I will conclude with the parareal algorithm as a new variant for parallelization of evolution problems in the time direction.
3.30pm, JCMB 5215, Kings Buildings. Chris L Farmer, University of Oxford. ‘A variational smoothing filter for sequential inverse problems’. Uncertainty quantification can begin by specifying the initial state of a system as a probability measure. Part of the state (the 'parameters') might not evolve, and might not be directly observable. Many inverse problems are generalisations of uncertainty quantification such that one modifies the probability measure to be consistent with measurements, a forward model and the initial measure. The main problem in the field is to devise a method for computing the posterior probability measure of the states, including the parameters and the variables, from a sequence of noise-corrupted observations. Bayesian statistics provides a framework for this, but leads to very challenging computational problems, particularly when the dimension of the state space is very large, as with problems arising from the discretisation of a partial differential equation theory. In this talk we show how to motivate and implement a 'Variational Smoothing Filter'. The full abstract is available on the Event Website.
12 Noon, Monday 17th September Tammy Kolda, Sandia National Laboratories, Capturing Community Behaviour in Very Large Networks Room LT908, Livingstone Tower, University of Strathclyde Tammy Kolda is a distinguished member of technical staff in the Informatics and Systems Assessments department at Sandia National Laboratories in Livermore, California. Her research interests include multilinear algebra and tensor decompositions, graph models and algorithms, data mining, optimization, nonlinear solvers, parallel computing and the design of scientific software. She serves as Section Editor for the Software and High Performance computing Section of the SIAM J Sci comp (SISC).
In today's digital world, with ever increasing amounts of readily-available data comes the need to solve optimization problems of unprecedented sizes. Machine learning, compressed sensing, natural language processing, truss topology design and computational genetics are some of many prominent application domains where it is easy to formulate optimization problems with tens of thousands or millions of variables. Many modern optimization algorithms, while exhibiting great efficiency in modest dimensions, are not designed to scale to instances of this size and are hence often, unfortunately, not applicable. On the other hand, simple methods, some having been proposed decades ago, are experiencing a comeback — albeit in modern forms. This workshop aims to bring together researchers working on novel optimization algorithms capable of working in large-scale setting.
Molecular dynamics has been described as a computational microscope, a versatile, high resolution method that can help to guide experiment or explore detailed mechanisms of molecular motion. Applications are wide ranging and include examples like the fracture or indentation of materials, structural rearrangements in crystals, and the transport of ions and small molecules through membranes. The evolution of computer hardware is rapidly changing the subject, with new algorithms needed for graphics processing units and hybrid computing architectures. The challenge is to boost the time and spatial scales accessible in simulation, while maintaining or improving accuracy in essential characteristics of the systems under study. The workshop will explore a range of new types of methods for accelerating molecular dynamics and for expanding its range of application.
The tutorial runs from 1pm Monday (April 30) to noon on Wednesday (May 2). It is organized by Berk Hess (KTH, Stockholm) and David Hardy (University of Illinois) with additional lecturers providing talks on special topics. The tutorial will address a variety of topics related to developing software for molecular dynamics in the HPC environment, including GPU computing and other current topics. The tutorial is intended for PhD students and other researchers who are either working in computational molecular science (interpreted broadly) or in high performance computing. The tutorial has strictly limited attendance due to space constraints.
The arrival of highly parallel, heterogeneous hardware in the computing mainstream challenges conventional software models. This workshop brings together a series of talks on projects which seek to address the issues, focusing in particular on the synthesis between pattern and domain oriented programming abstractions and the ability to autotune code for performance portability.
This workshop, presented and led by Mike Giles (Oxford), will cover a variety of topics related to the use of GPUs for advanced scientific computing applications, including programming methods and example case studies. UK academics, including PhD students and postdoctoral researchers are encouraged to attend.