Reliable Real-Time Solution of Parametrized Elliptic Partial Differential Equations: Application to Elasticity

The optimization, control, and characterization of engineering components or systems require fast, repeated, and accurate evaluation of a partial-differential-equation-induced input-output relationship. We present a technique for the rapid and reliable prediction of linear-functional outputs of elliptic partial differential equations with affine parameter dependence. The method has three components: (i) rapidly convergent reduced{basis approximations; (ii) a posteriori error estimation; and (iii) off-line/on-line computational procedures. These components -- integrated within a special network architecture -- render partial differential equation solutions truly "useful": essentially real{time as regards operation count; "blackbox" as regards reliability; and directly relevant as regards the (limited) input-output data required.Singapore-MIT Alliance (SMA

Sparse multi-level representations for text retrieval

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. [153]-160).by Charles Lee Isbell, Junior.Ph.D

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

Strategies for including cloud-computing into an engineering modeling workflow

With the advent of cloud computing, high-end computing, networking, and storage resources are available on-demand at a relatively low price point. Internet applications in the consumer and increasingly in the enterprise space are making use of these resources to upgrade existing applications and build new ones. This is made possible by building decentralized applications that can be integrated with one another through web-enabled application programming interfaces (APIs). However, in the fields of engineering and computational science, cloud computing resources have been utilized primarily to augment existing high-performance computing hardware, but engineering model integrations still occur by the use of software libraries. In this research, a novel approach is proposed where engineering models are constructed as independent services that publish web-enabled APIs. To enable this, the engineering models are built as stateless microservices that solve a single computational problem. Composite services are then built utilizing these independent component models, much like in the consumer application space. Interactions between component models is orchestrated by a federation management system. This proposed approach is then demonstrated by disaggregating an existing monolithic model for a cookstove into a set of component models. The component models are then reintegrated and compared with the original model for computational accuracy and run-time. Additionally, a novel engineering workflow is proposed that reuses computational data by constructing reduced-order models (ROMs). This framework is evaluated empirically for a number of producers and consumers of engineering models based on computation and data synchronization aspects. The framework is also evaluated by simulating an engineering design workflow with multiple producers and consumers at various stages during the design process. Finally, concepts from the federated system of models and ROMs are combined to propose the concept of a hybrid model (information artefact). The hybrid model is a web-enabled microservice that encapsulates information from multiple engineering models at varying fidelities, and responds to queries based on the best available information. Rules for the construction of hybrid models have been proposed and evaluated in the context of engineering workflows