Risk-averse structural topology optimization under random fields using stochastic expansion methods

This work proposes and analyzes an anisotropic sparse grid stochastic collocation
\nmethod for solving partial differential equations with random coefficients and forcing terms (input
\ndata of the model). The method consists of a Galerkin approximation in the space variables and a
\ncollocation, in probability space, on sparse tensor product grids utilizing either Clenshaw–Curtis or
\nGaussian knots. Even in the presence of nonlinearities, the collocation approach leads to the solution
\nof uncoupled deterministic problems, just as in the Monte Carlo method. This work includes a priori
\nand a posteriori procedures to adapt the anisotropy of the sparse grids to each given problem.
\nThese procedures seem to be very effective for the problems under study. The proposed method
\ncombines the advantages of isotropic sparse collocation with those of anisotropic full tensor product
\ncollocation: the first approach is effective for problems depending on random variables which weigh
\napproximately equally in the solution, while the benefits of the latter approach become apparent when
\nsolving highly anisotropic problems depending on a relatively small number of random variables, as
\nin the case where input random variables are Karhunen–Lo`eve truncations of “smooth” random
\nfields. This work also provides a rigorous convergence analysis of the fully discrete problem and
\ndemonstrates (sub)exponential convergence in the asymptotic regime and algebraic convergence in
\nthe preasymptotic regime, with respect to the total number of collocation points. It also shows
\nthat the anisotropic approximation breaks the curse of dimensionality for a wide set of problems.
\nNumerical examples illustrate the theoretical results and are used to compare this approach with
\nseveral others, including the standard Monte Carlo. In particular, for moderately large-dimensional
\nproblems, the sparse grid approach with a properly chosen anisotropy seems to be very efficient and
\nsuperior to all examined methods.

For decades, Lawrence Livermore National Laboratory (LLNL) has been engaged in significant research, development, and support for software to enable scientific computing and, particularly, the use of high performance computing (HPC) in the NNSA mission space. In particular, the move in the mid-1990’s to simulation as a leading component of stockpile stewardship through the ASCI and the successor ASC programs, as well as the need for reliable data acquisition and control software for the National Ignition Facility, have been important drivers in building expertise in production-quality software development at LLNL. LLNL has also been a leader in the DOE SciDAC FASTMath Institute and the DOE Exascale Computing Project (ECP), both of which have striven to make scientific computing software – in particular, the enabling technologies underpinning simulation capabilities – more widely adopted and sustainable. As such, we believe that our experience can inform the broader goal of software stewardship for scientific and high-performance computing. LLNL strongly supports the formation of a new DOE ASCR program element in software stewardship and sustainment. Historically, DOE ASCR has funded applied mathematics and computer science research that has led to the development of important new capabilities and algorithms that are expressed as artifacts in research software. Such frameworks, libraries, and tools have seldom been directly funded to address the important issues of code maintenance, documentation, robustness, and community building. Software engineering and support have typically been done on the side in support of the ASCR-driven research products. DOE funding priorities have been slow to recognize that good software engineering, the kind that ensures research investments have more adoption and longevity, requires significant resources. Based upon our experiences, we have prepared this response to highlight the concerns and issues we believe to be important as DOE ASCR considers its role in scientific software stewardship. We believe that role is important and will require a significant investment of new funding to legitimately support the technologies past and future DOE ASCR investments have and will produce to facilitate their uptake and adoption in the broader scientific computing community. Following a summary of our involvement in scientific software development, the remainder our response is organized around the nine topics specifically identified in the RFI.

Uncertainty quantification for personalized analyses of human proximal femurs

Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers

The technologically relevant adsorbate systems S:Ge(001)-(1 x 1) and Se:Ge(001)-(1 x 1) are prototypes for the passivation of semiconductor surfaces. Over the last years, their electronic and structural properties have been studied by ab-initio methods in detail, while the description of vibronic properties was limited to semi-empirical methods. The use of high performance computing (HPC) allows for the first time the calculation of surface phonons for these particular systems from first principles within a reasonable time, for example with the help of density functional perturbation theory. This theory, its implementation on a parallel computer and results concerning the vibronic properties of the above-mentioned surfaces are presented in this article.

Scientific workflows are models composed of activities, data and dependencies whose objective is to represent a computer simulation. Workflows are managed by Scientific Workflow Management System (SWfMS). Such workflows commonly demand for many computational resources once their executions may involve a number of different programs processing a huge volume of data. Thus, the use of High Performance Computing (HPC) environments allied to parallelization techniques provides the support for the execution of such experiments. Some resources provided by clouds can be used to build HPC environments. Although clouds offer advantages such as elasticity and availability, failures are a reality rather than a possibility. Thus, SWfMS must be fault-tolerant. There are several types of fault tolerance techniques used in SWfMS such as checkpoint-restart and replication, but which fault tolerance technique best fits with a specific workflow? This work aims at analyzing several fault tolerance techniques in SWfMSs and recommending the suitable one for the user’s workflow using machine learning techniques and provenance data, thus improving resiliency.

• We highlighted Silicon Photonics Technology Platform and its scalability to increased data rates, higher interconnect densities and low system level power dissipation. • Silicon photonics has been in production since 2009 and has shipped > 500K+ chipsets for use in High Performance Computing and Datacenters. • We made the trade-off between hybrid and monolithic integration of photonics and electronics. Hybrid Silicon Photonics allows cost effective integration of photonics with advanced electronic nodes. • By integrating 3rd Party IP in the electronic IC, hybrid integration enables a first level of ASIC integration with Photonics. • A next level of ASIC integration is enabled by a “Silicon Photonics Interposer” where photonic capabilities are combined with hybrid integration with electronics and TSV technology.

The increasingly computational and data intensive nature of geospatial problem solving and the requirement to provide collaborative support across numerous fields, necessitates the use of high performance computing and distributed systems which have emerged as prominent elements in the landscape of computing and information technologies. The inaugural ACM SIGSPATIAL HPDGIS workshop was hence launched to bring together researchers and practitioners to map out the fundamental research areas centered on synergistic advances in Geographic Information Systems (GIS) and spatial analysis, high performance computing, and distributed systems.

Supercomputers were once regarded as being of very limited use - of interest to a very few national centers and used by a small fraction of researchers at any given university. As scientific research becomes more and more dependent upon management and analysis of massive amounts of data, advances in human knowledge will become increasingly dependent upon use of high performance computers and parallel programming techniques. Indiana University has undergone a transformation over the past four years, during which the capacity, use, and number of users of High Performance Computing (HPC) systems has dramatically increased. HPC systems are widely viewed as valuable to the scholarly community of Indiana University - even by those researchers who do not use parallel programming techniques. Economies of scale and vendor partnerships have enabled Indiana University to amass significant HPC systems. Carefully implemented strategies in delivery of consulting support have expanded the use of parallel programming techniques. Such techniques are of critical value to advancement of human knowledge in many disciplines, and it is now possible for any institution of higher education to provide some sort of parallel computing resource for education and research.

A mitral valve reconstruction (MVR) is a complex operation in which the functionality of incompetent mitral valves is re-established by applying surgical techniques. This work deals with predictive biomechanical simulations of operation scenarios for an MVR, and the simulation's integration into a knowledge-based surgery assistance system. We present a framework for the definition of the corresponding surgical workflow, which combines semantically enriched surgical expert knowledge with a biomechanical simulation. Using an ontology, 'surgical rules' which describe decision and assessment criteria for surgical decision-making are represented in a knowledge base. Through reasoning these 'rules' can then be applied on patient-specific data in order to be converted into boundary conditions for the biomechanical soft tissue simulation, which is based on the Finite Elements Method (FEM). The simulation, which is implemented in the open-source C++ FEM software HiFlow3, is controlled via the Medical Simulation Markup Language (MSML), and makes use of High Performance Computing (HPC) methods to cope with real-time requirements in surgery. The simulation results are presented to surgeons to assess the quality of the virtual reconstruction and the consequential remedial effects on the mitral valve and its functionality. The whole setup has the potential to support the intraoperative decision-making process during MVR where the surgeon usually has to make fundamental decision under time pressure.

Generating large prime number is a time consuming problem. It is useful in key generation in network security. The problem of generating prime number is easy to parallelize. Use of high performance computing (HPC) facilities can be applied for getting faster results. Embarrassing parallel pattern is applied to categories of algorithms where concurrency is explicit. We have conducted experiments on sequential and parallel approaches of prime numbers generation. In this paper, our objective is to analyze time taken in generating large prime numbers using multicore cluster. The result could be reusable in estimating time required for key generation in cryptography.

The Superfacility API brings automation to the use of High Performance Computing (HPC) systems. Our aim is to enable scientists to reliably automate their interactions with computational resources at the National Energy Research Scientific Computing Center (NERSC), removing human intervention from the process of transferring, analyzing, and managing data. In this paper, we describe the science use cases that drive the API design, our schema of API endpoints, and implementation details and considerations, including authentication and authorization. We also discuss future plans, working toward our vision of supporting entirely automated experiment-network-HPC workflows.

As simulations become popular in analyzing large-scale real-world systems, more engineers are experiencing difficulties to deal with extensive workload of computations. The use of high performance computing (HPC) infrastructure, such as cloud computing, opens an opportunity to reduce computation time by distributing the workloads over the cloud, on-demand. We are currently working on a cloud-based platform which provides modeling, simulation, and analysis services to users. By utilizing the cloud services, users can execute their simulations on the cloud and thus save simulation time. In this paper, we present the cloud services and illustrate the usage of them with an example of a chemical gas diffusion simulation.

In this paper, we propose a method for taking into account uncertainties based on the projection on polynomial chaos. Due to the manufacturing and assembly errors, uncertainties in material and geometric properties, the system parameters including assembly defect, damping coefficients, bending stiffness and traction-compression stiffness are uncertain. The proposed method is used to determine the dynamic response of a one-stage spur gear system with uncertainty associated to gear system parameters. An analysis of the effect of these parameters on the one stage gear system dynamic behavior is then treated. The simulation results are obtained by the polynomial chaos method for dynamic analysis under uncertainty. The proposed method is an efficient probabilistic tool for uncertainty propagation. The polynomial chaos results are compared with Monte Carlo simulations.

This article presents methods for correct decomposition for high performance computations related to large sets of graphs. These computations contain large number of calls of sequential, recursive algorithm for NP-complete problem - proper edge coloring of graph. Decomposition of this computational problem is not trivial, since the number of recursions in various parts of the computation is different and causes a high load and time imbalance. We designed, implemented and experimentally verified a new decomposition method that significantly reduces the computational time for a large set of graphs (up to 404 million graphs). This method ensures the same duration of computational time for partial subtasks and thus eliminates the need to wait for synchronization of parallel computations.