We present Poblano v1.0, a Matlab toolbox for solving gradient-based unconstrained optimization problems. Poblano implements three optimization methods (nonlinear conjugate gradients, limited-memory BFGS, and truncated Newton) that require only first order derivative information. In this paper, we describe the Poblano methods, provide numerous examples on how to use Poblano, and present results of Poblano used in solving problems from a standard test collection of unconstrained optimization problems.
The difficulty of calculating the ambient properties of molecular crystals, such as the explosive PETN, has long hampered much needed computational investigations of these materials. One reason for the shortcomings is that the exchange-correlation functionals available for Density Functional Theory (DFT) based calculations do not correctly describe the weak intermolecular van der Waals' forces present in molecular crystals. However, this weak interaction also poses other challenges for the computational schemes used. We will discuss these issues in the context of calculations of lattice constants and structure of PETN with a number of different functionals, and also discuss if these limitations can be circumvented for studies at non-ambient conditions.
This presentation discusses the following topics: (1) Red Sky Background; (2) 3D Torus Interconnect Concepts; (3) Difficulties of Torus in IB; (4) New Routing Code for IB a 3D Torus; (5) Red Sky 3D Torus Implementation; and (6) Managing a Large IB Machine. Computing at Sandia: (1) Capability Computing - Designed for scaling of single large runs, Usually proprietary for maximum performance, and Red Storm is Sandia's current capability machine; (2) Capacity Computing - Computing for the masses, 100s of jobs and 100s of users, Extreme reliability required, Flexibility for changing workload, Thunderbird will be decommissioned this quarter, Red Sky is our future capacity computing platform, and Red Mesa machine for National Renewable Energy Lab. Red Sky main themes are: (1) Cheaper - 5X capacity of Tbird at 2/3 the cost, Substantially cheaper per flop than our last large capacity machine purchase; (2) Leaner - Lower operational costs, Three security environments via modular fabric, Expandable, upgradeable, extensible, and Designed for 6yr. life cycle; and (3) Greener - 15% less power-1/6th power per flop, 40% less water-5M gallons saved annually, 10X better cooling efficiency, and 4x denser footprint.
Constructing high-fidelity control pulses that are robust to control and system/environment fluctuations is a crucial objective for quantum information processing (QIP). We combine dynamical decoupling (DD) with optimal control (OC) to identify control pulses that achieve this objective numerically. Previous DD work has shown that general errors up to (but not including) third order can be removed from {pi}- and {pi}/2-pulses without concatenation. By systematically integrating DD and OC, we are able to increase pulse fidelity beyond this limit. Our hybrid method of quantum control incorporates a newly-developed algorithm for robust OC, providing a nested DD-OC approach to generate robust controls. Motivated by solid-state QIP, we also incorporate relevant experimental constraints into this DD-OC formalism. To demonstrate the advantage of our approach, the resulting quantum controls are compared to previous DD results in open and uncertain model systems.
Alkali nitrate eutectic mixtures are finding application as industrial heat transfer fluids in concentrated solar power generation systems. An important property for such applications is the melting point, or phase coexistence temperature. We have computed melting points for lithium, sodium and potassium nitrate from molecular dynamics simulations using a recently developed method, which uses thermodynamic integration to compute the free energy difference between the solid and liquid phases. The computed melting point for NaNO3 was within 15K of its experimental value, while for LiNO3 and KNO3, the computed melting points were within 100K of the experimental values [4]. We are currently extending the approach to calculate melting temperatures for binary mixtures of lithium and sodium nitrate.
This document provides common best practices for the efficient utilization of parallel file systems for analysts and application developers. A multi-program, parallel supercomputer is able to provide effective compute power by aggregating a host of lower-power processors using a network. The idea, in general, is that one either constructs the application to distribute parts to the different nodes and processors available and then collects the result (a parallel application), or one launches a large number of small jobs, each doing similar work on different subsets (a campaign). The I/O system on these machines is usually implemented as a tightly-coupled, parallel application itself. It is providing the concept of a 'file' to the host applications. The 'file' is an addressable store of bytes and that address space is global in nature. In essence, it is providing a global address space. Beyond the simple reality that the I/O system is normally composed of a small, less capable, collection of hardware, that concept of a global address space will cause problems if not very carefully utilized. How much of a problem and the ways in which those problems manifest will be different, but that it is problem prone has been well established. Worse, the file system is a shared resource on the machine - a system service. What an application does when it uses the file system impacts all users. It is not the case that some portion of the available resource is reserved. Instead, the I/O system responds to requests by scheduling and queuing based on instantaneous demand. Using the system well contributes to the overall throughput on the machine. From a solely self-centered perspective, using it well reduces the time that the application or campaign is subject to impact by others. The developer's goal should be to accomplish I/O in a way that minimizes interaction with the I/O system, maximizes the amount of data moved per call, and provides the I/O system the most information about the I/O transfer per request.
LAMMPS is a classical molecular dynamics code, and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator. LAMMPS has potentials for soft materials (biomolecules, polymers) and solid-state materials (metals, semiconductors) and coarse-grained or mesoscopic systems. It can be used to model atoms or, more generically, as a parallel particle simulator at the atomic, meso, or continuum scale. LAMMPS runs on single processors or in parallel using message-passing techniques and a spatial-decomposition of the simulation domain. The code is designed to be easy to modify or extend with new functionality.
This report describes the activities and results of a Sandia LDRD project whose objective was to develop and demonstrate foundational aspects of a next-generation nuclear reactor safety code that leverages advanced computational technology. The project scope was directed towards the systems-level modeling and simulation of an advanced, sodium cooled fast reactor, but the approach developed has a more general applicability. The major accomplishments of the LDRD are centered around the following two activities. (1) The development and testing of LIME, a Lightweight Integrating Multi-physics Environment for coupling codes that is designed to enable both 'legacy' and 'new' physics codes to be combined and strongly coupled using advanced nonlinear solution methods. (2) The development and initial demonstration of BRISC, a prototype next-generation nuclear reactor integrated safety code. BRISC leverages LIME to tightly couple the physics models in several different codes (written in a variety of languages) into one integrated package for simulating accident scenarios in a liquid sodium cooled 'burner' nuclear reactor. Other activities and accomplishments of the LDRD include (a) further development, application and demonstration of the 'non-linear elimination' strategy to enable physics codes that do not provide residuals to be incorporated into LIME, (b) significant extensions of the RIO CFD code capabilities, (c) complex 3D solid modeling and meshing of major fast reactor components and regions, and (d) an approach for multi-physics coupling across non-conformal mesh interfaces.
As scientific simulations scale to use petascale machines and beyond, the data volumes generated pose a dual problem. First, with increasing machine sizes, the careful tuning of IO routines becomes more and more important to keep the time spent in IO acceptable. It is not uncommon, for instance, to have 20% of an application's runtime spent performing IO in a 'tuned' system. Careful management of the IO routines can move that to 5% or even less in some cases. Second, the data volumes are so large, on the order of 10s to 100s of TB, that trying to discover the scientifically valid contributions requires assistance at runtime to both organize and annotate the data. Waiting for offline processing is not feasible due both to the impact on the IO system and the time required. To reduce this load and improve the ability of scientists to use the large amounts of data being produced, new techniques for data management are required. First, there is a need for techniques for efficient movement of data from the compute space to storage. These techniques should understand the underlying system infrastructure and adapt to changing system conditions. Technologies include aggregation networks, data staging nodes for a closer parity to the IO subsystem, and autonomic IO routines that can detect system bottlenecks and choose different approaches, such as splitting the output into multiple targets, staggering output processes. Such methods must be end-to-end, meaning that even with properly managed asynchronous techniques, it is still essential to properly manage the later synchronous interaction with the storage system to maintain acceptable performance. Second, for the data being generated, annotations and other metadata must be incorporated to help the scientist understand output data for the simulation run as a whole, to select data and data features without concern for what files or other storage technologies were employed. All of these features should be attained while maintaining a simple deployment for the science code and eliminating the need for allocation of additional computational resources.
The next generation of capability-class, massively parallel processing (MPP) systems is expected to have hundreds of thousands to millions of processors, In such environments, it is critical to have fault-tolerance mechanisms, including checkpoint/restart, that scale with the size of applications and the percentage of the system on which the applications execute. For application-driven, periodic checkpoint operations, the state-of-the-art does not provide a scalable solution. For example, on today's massive-scale systems that execute applications which consume most of the memory of the employed compute nodes, checkpoint operations generate I/O that consumes nearly 80% of the total I/O usage. Motivated by this observation, this project aims to improve I/O performance for application-directed checkpoints through the use of lightweight storage architectures and overlay networks. Lightweight storage provide direct access to underlying storage devices. Overlay networks provide caching and processing capabilities in the compute-node fabric. The combination has potential to signifcantly reduce I/O overhead for large-scale applications. This report describes our combined efforts to model and understand overheads for application-directed checkpoints, as well as implementation and performance analysis of a checkpoint service that uses available compute nodes as a network cache for checkpoint operations.