Search-based Model-driven Loop Optimizations for Tensor Contractions

Complex tensor contraction expressions arise in accurate electronic structure models in quantum chemistry, such as the coupled cluster method. The Tensor Contraction Engine (TCE) is a high-level program synthesis system that facilitates the generation of high-performance parallel programs from tensor contraction equations. We are developing a new software infrastructure for the TCE that is designed to allow experimentation with optimization algorithms for modern computing platforms, including for heterogeneous architectures employing general-purpose graphics processing units (GPGPUs). In this dissertation, we present improvements and extensions to the loop fusion optimization algorithm, which can be used with cost models, e.g., for minimizing memory usage or for minimizing data movement costs under a memory constraint. We show that our data structure and pruning improvements to the loop fusion algorithm result in significant performance improvements that enable complex cost models being use for large input equations. We also present an algorithm for optimizing the fused loop structure of handwritten code. It determines the regions in handwritten code that are safe to be optimized and then runs the loop fusion algorithm on the dependency graph of the code. Finally, we develop an optimization framework for generating GPGPU code consisting of loop fusion optimization with a novel cost model, tiling optimization, and layout optimization. Depending on the memory available on the GPGPU and the sizes of the tensors, our framework decides which processor (CPU or GPGPU) should perform an operation and where the result should be moved. We present extensive measurements for tuning the loop fusion algorithm, for validating our optimization framework, and for measuring the performance characteristics of GPGPUs. Our measurements demonstrate that our optimization framework outperforms existing general-purpose optimization approaches both on multi-core CPUs and on GPGPUs

High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation

Quantum chemical calculations on atomistic systems have evolved into a standard approach to study molecular matter. These calculations often involve a significant amount of manual input and expertise although most of this effort could be automated, which would alleviate the need for expertise in software and hardware accessibility. Here, we present the AutoRXN workflow, an automated workflow for exploratory high-throughput lectronic structure calculations of molecular systems, in which (i) density functional theory methods are exploited to deliver minimum and transition-state structures and corresponding energies and properties, (ii) coupled cluster calculations are then launched for optimized structures to provide more accurate energy and property estimates, and (iii) multi-reference diagnostics are evaluated to back check the coupled cluster results and subject hem to automated multi-configurational calculations for potential multi-configurational cases. All calculations are carried out in a cloud environment and support massive computational campaigns. Key features of all omponents of the AutoRXN workflow are autonomy, stability, and minimum operator interference. We highlight the AutoRXN workflow at the example of an autonomous reaction mechanism exploration of the mode of action of a homogeneous catalyst for the asymmetric reduction of ketones.Comment: 29 pages, 11 figure

COMPOSE-HPC: A Transformational Approach to Exascale

The goal of the COMPOSE-HPC project is to 'democratize' tools for automatic transformation of program source code so that it becomes tractable for the developers of scientific applications to create and use their own transformations reliably and safely. This paper describes our approach to this challenge, the creation of the KNOT tool chain, which includes tools for the creation of annotation languages to control the transformations (PAUL), to perform the transformations (ROTE), and optimization and code generation (BRAID), which can be used individually and in combination. We also provide examples of current and future uses of the KNOT tools, which include transforming code to use different programming models and environments, providing tests that can be used to detect errors in software or its execution, as well as composition of software written in different programming languages, or with different threading patterns

Bacterial antimicrobial metal ion resistance

Metals such as mercury, arsenic, copper and silver have been used in various forms as antimicrobials for thousands of years with until recently, little understanding of their mode of action. The discovery of antibiotics and new organic antimicrobial compounds during the twentieth century saw a general decline in the clinical use of antimicrobial metal compounds, with the exception of the rediscovery of the use of silver for burns treatments and niche uses for other metal compounds. Antibiotics and new antimicrobials were regarded as being safer for the patient and more effective than the metal-based compounds they supplanted. Bacterial metal ion resistances were first discovered in the second half of the twentieth century. The detailed mechanisms of resistance have now been characterized in a wide range of bacteria. As the use of antimicrobial metals is limited, it is legitimate to ask: are antimicrobial metal resistances in pathogenic and commensal bacteria important now? This review details the new, rediscovered and 'never went away' uses of antimicrobial metals; examines the prevalence and linkage of antimicrobial metal resistance genes to other antimicrobial resistance genes; and examines the evidence for horizontal transfer of these genes between bacteria. Finally, we discuss the possible implications of the widespread dissemination of these resistances on re-emergent uses of antimicrobial metals and how this could impact upon the antibiotic resistance problem

Laser desorption ionization time-of-flight mass spectrometry of erbium-doped Ga-Ge-Sb-S glasses.

International audienceRATIONALE: Rare earth-doped sulphide glasses in the Ga-Ge-Sb-S system present radiative emissions from the visible to the middle infrared range (mid-IR) range, which are of interest for a variety of applications including (bio)-chemical optical sensing, light detection, and military counter-measures. The aim of this work was to reveal structural motifs present during the fabrication of thin films by plasma deposition techniques as such knowledge is important for the optimization of thin film growth. METHODS: The formation of clusters in plasma plume from different concentrations of erbium-doped Ga5 Ge20 Sb10 S65 glasses (0.05, 0.1, and 0.5 wt. % of erbium) using laser (337 nm) desorption ionization (LDI) was studied by time-of-flight mass spectrometry (TOF MS) in both positive and negative ion mode. The stoichiometry of the Gam Gen Sbo Sp (+/-) clusters was determined via isotopic envelope analysis and computer modelling. RESULTS: Several Gam Gen Sbo Sp (+/-) singly charged clusters were found but, surprisingly, only four species (Sb3 S4 (+/-) , GaSb2 Sp (+/-) (p = 4, 5), Ga3 Sb2 S7 (+/-) ) were common to both ion modes. For the first time, species containing rare earths (GaSb2 SEr(+) and GaS6 Er2 (+) ) were identified in the plasma formed from rare earth-doped chalcogenide glasses, directly confirming the importance of gallium presence for rare earth bonding within the glassy matrix. CONCLUSIONS: The local structure of Ga-Ge-Sb-S glasses is at least partly different from the structure of species identified in plasma by mass spectrometry, as deduced from Raman scattering spectroscopy analysis; these glasses are mainly formed by [GeS4/2 ]/[GaS4/2 ] tetrahedra and [SbS3/2 ] pyramids. Extended X-ray absorption fine structure measurements show that Er(3+) ions in Ga-Ge-Sb-S glasses are surrounded by 7 sulphur atoms. Copyright © 2014 John Wiley & Sons, Ltd

Comparative study on cellular entry of incinerated ancient gold particles (Swarna Bhasma) and chemically synthesized gold particles

Gold nanoparticles (AuNPs) are used for a number of imaging and therapeutic applications in east and western part of the world. For thousands of years, the traditional Indian Ayurvedic approach to healing involves the use of incinerated gold ash, prepared with a variety of plant extracts and minerals depending on the region. Here, we describe the characterization of incinerated gold particles (IAuPs) in HeLa (human cells derived from cervical cancer) and HFF-1 (human foreskin fibroblast cells) in comparison to synthesized citrate-capped gold nanoparticles (AuNPs). We found that while individual IAuP crystallites are around 60 nm in size, they form large aggregates with a mean diameter of 4711.7 nm, some of which can enter cells. Fewer cells appeared to have IAuPs compared to AuNPs, although neither type of particle was toxic to cells. Imaging studies revealed that IAuPs were in vesicles, cytosol, or in the nucleus. We found that their nuclear accumulation likely occurred after nuclear envelope breakdown during cell division. We also found that larger IAuPs entered cells via macropinocytosis, while smaller particles entered via clathrin-dependent receptor-mediated endocytosis