Dynamic adaptation to CPU and memory load in scientific applications

As commodity computers and networking technologies have become faster and more affordable, fairly capable machines have become nearly ubiquitous while the effective distance between them has decreased as network connectivity and capacity has multiplied. There is considerable interest in developing means to readily access such vast amounts of computing power to solve scientific problems, but the complexity of these modern computing environments pose problems for conventional computer codes designed to run on a static, homogeneous set of resources. One source of problems is the heterogeneity that is naturally present in these settings. More problematic is the competition that arises between programs for shared resources in these semi-autonomous environments. Fluctuations in the availability of CPU, memory, and other resources can cripple application performance. Contention for CPU time between jobs may introduce significant load imbalance in parallel applications. Contention for limited memory resources may cause even more severe performance problems, as thrashing may increase execution times by an order of magnitude or more.;Our goal is to develop techniques that enable scientific applications to achieve good performance in non-dedicated environments by monitoring system conditions and adapting their behavior accordingly. We focus on two important shared resources, CPU and memory, and pursue our goal on two distinct but complementary fronts: First, we present some simple algorithmic modifications that can significantly improve load balance in a class of iterative methods that form the computational core of many scientific and engineering applications. Second, we introduce a framework for enabling scientific applications to dynamically adapt their memory usage according to current availability of main memory. An application-specific caching policy is used to keep as much of the data set as possible in main memory, while the remainder of the data are accessed in an out-of-core fashion.;We have developed modular code libraries to facilitate implementation of our techniques, and have deployed them in a variety of scientific application kernels. Experimental evaluation of their performance indicates that our techniques provide some important classes of scientific applications with robust and low-overhead means for mitigating the effects of fluctuations in CPU and memory availability

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

The density-matrix renormalization group

The density-matrix renormalization group (DMRG) is a numerical algorithm for the efficient truncation of the Hilbert space of low-dimensional strongly correlated quantum systems based on a rather general decimation prescription. This algorithm has achieved unprecedented precision in the description of one-dimensional quantum systems. It has therefore quickly acquired the status of method of choice for numerical studies of one-dimensional quantum systems. Its applications to the calculation of static, dynamic and thermodynamic quantities in such systems are reviewed. The potential of DMRG applications in the fields of two-dimensional quantum systems, quantum chemistry, three-dimensional small grains, nuclear physics, equilibrium and non-equilibrium statistical physics, and time-dependent phenomena is discussed. This review also considers the theoretical foundations of the method, examining its relationship to matrix-product states and the quantum information content of the density matrices generated by DMRG.Comment: accepted by Rev. Mod. Phys. in July 2004; scheduled to appear in the January 2005 issu

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018