Adaptive control in rollforward recovery for extreme scale multigrid

With the increasing number of compute components, failures in future exa-scale computer systems are expected to become more frequent. This motivates the study of novel resilience techniques. Here, we extend a recently proposed algorithm-based recovery method for multigrid iterations by introducing an adaptive control. After a fault, the healthy part of the system continues the iterative solution process, while the solution in the faulty domain is re-constructed by an asynchronous on-line recovery. The computations in both the faulty and healthy subdomains must be coordinated in a sensitive way, in particular, both under and over-solving must be avoided. Both of these waste computational resources and will therefore increase the overall time-to-solution. To control the local recovery and guarantee an optimal re-coupling, we introduce a stopping criterion based on a mathematical error estimator. It involves hierarchical weighted sums of residuals within the context of uniformly refined meshes and is well-suited in the context of parallel high-performance computing. The re-coupling process is steered by local contributions of the error estimator. We propose and compare two criteria which differ in their weights. Failure scenarios when solving up to $6.9\cdot10^{11}$ unknowns on more than 245\,766 parallel processes will be reported on a state-of-the-art peta-scale supercomputer demonstrating the robustness of the method

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

A high-performance open-source framework for multiphysics simulation and adjoint-based shape and topology optimization

The first part of this thesis presents the advances made in the Open-Source software SU2, towards transforming it into a high-performance framework for design and optimization of multiphysics problems. Through this work, and in collaboration with other authors, a tenfold performance improvement was achieved for some problems. More importantly, problems that had previously been impossible to solve in SU2, can now be used in numerical optimization with shape or topology variables. Furthermore, it is now exponentially simpler to study new multiphysics applications, and to develop new numerical schemes taking advantage of modern high-performance-computing systems. In the second part of this thesis, these capabilities allowed the application of topology optimiza- tion to medium scale fluid-structure interaction problems, using high-fidelity models (nonlinear elasticity and Reynolds-averaged Navier-Stokes equations), which had not been done before in the literature. This showed that topology optimization can be used to target aerodynamic objectives, by tailoring the interaction between fluid and structure. However, it also made ev- ident the limitations of density-based methods for this type of problem, in particular, reliably converging to discrete solutions. This was overcome with new strategies to both guarantee and accelerate (i.e. reduce the overall computational cost) the convergence to discrete solutions in fluid-structure interaction problems.Open Acces

Computer Science Research Institute 2005 annual report of activities.

This report summarizes the activities of the Computer Science Research Institute (CSRI) at Sandia National Laboratories during the period January 1, 2005 to December 31, 2005. During this period, the CSRI hosted 182 visitors representing 83 universities, companies and laboratories. Of these, 60 were summer students or faculty. The CSRI partially sponsored 2 workshops and also organized and was the primary host for 3 workshops. These 3 CSRI sponsored workshops had 105 participants, 78 from universities, companies and laboratories, and 27 from Sandia. Finally, the CSRI sponsored 12 long-term collaborative research projects and 3 Sabbaticals

Principled and Efficient Bilevel Optimization for Machine Learning

Automatic differentiation (AD) is a core element of most modern machine learning libraries that allows to efficiently compute derivatives of a function from the corresponding program. Thanks to AD, machine learning practitioners have tackled increasingly complex learning models, such as deep neural networks with up to hundreds of billions of parameters, which are learned using the derivative (or gradient) of a loss function with respect to those parameters. While in most cases gradients can be computed exactly and relatively cheaply, in others the exact computation is either impossible or too expensive and AD must be used in combination with approximation methods. Some of these challenging scenarios arising for example in meta-learning or hyperparameter optimization, can be framed as bilevel optimization problems, where the goal is to minimize an objective function that is evaluated by first solving another optimization problem, the lower-level problem. In this work, we study efficient gradient-based bilevel optimization algorithms for machine learning problems. In particular, we establish convergence rates for some simple approaches to approximate the gradient of the bilevel objective, namely the hypergradient, when the objective is smooth and the lower-level problem consists in finding the fixed point of a contraction map. Leveraging such results, we also prove that the projected inexact hypergradient method achieves a (near) optimal rate of convergence. We establish these results for both the deterministic and stochastic settings. Additionally, we provide an efficient implementation of the methods studied and perform several numerical experiments on hyperparameter optimization, meta-learning, datapoisoning and equilibrium models, which show that our theoretical results are good indicators of the performance in practice

HPCCP/CAS Workshop Proceedings 1998

This publication is a collection of extended abstracts of presentations given at the HPCCP/CAS (High Performance Computing and Communications Program/Computational Aerosciences Project) Workshop held on August 24-26, 1998, at NASA Ames Research Center, Moffett Field, California. The objective of the Workshop was to bring together the aerospace high performance computing community, consisting of airframe and propulsion companies, independent software vendors, university researchers, and government scientists and engineers. The Workshop was sponsored by the HPCCP Office at NASA Ames Research Center. The Workshop consisted of over 40 presentations, including an overview of NASA's High Performance Computing and Communications Program and the Computational Aerosciences Project; ten sessions of papers representative of the high performance computing research conducted within the Program by the aerospace industry, academia, NASA, and other government laboratories; two panel sessions; and a special presentation by Mr. James Bailey