Reading list of selected PASM-related publications

Prepared for a chapter to be published in the forthcoming Encyclopedia of Parallel Computing by Springer Publishing Company. The Encyclopedia will contain a broad coverage of the field and will include entries on machine organization, programming, algorithms, and applications. The broad coverage, together with extensive pointers to the literature for in-depth study, is expected to make the Encyclopedia a useful reference tool in parallel computing

Efficient Mapping of Neural Network Models on a Class of Parallel Architectures.

This dissertation develops a formal and systematic methodology for efficient mapping of several contemporary artificial neural network (ANN) models on k-ary n-cube parallel architectures (KNC\u27s). We apply the general mapping to several important ANN models including feedforward ANN\u27s trained with backpropagation algorithm, radial basis function networks, cascade correlation learning, and adaptive resonance theory networks. Our approach utilizes a parallel task graph representing concurrent operations of the ANN model during training. The mapping of the ANN is performed in two steps. First, the parallel task graph of the ANN is mapped to a virtual KNC of compatible dimensionality. This involves decomposing each operation into its atomic tasks. Second, the dimensionality of the virtual KNC architecture is recursively reduced through a sequence of transformations until a desired metric is optimized. We refer to this process as folding the virtual architecture. The optimization criteria we consider in this dissertation are defined in terms of the iteration time of the algorithm on the folded architecture. If necessary, the mapping scheme may utilize a subset of the processors of a given KNC architecture if it results in the most efficient simulation. A unique feature of our mapping is that it systematically selects an appropriate degree of parallelism leading to a highly efficient realization of the ANN model on KNC architectures. A novel feature of our work is its ability to efficiently map unit-allocating ANN\u27s. These networks possess a dynamic structure which grows during training. We present a highly efficient scheme for simulating such networks on existing KNC parallel architectures. We assume an upper bound on size of the neural network We perform the folding such that the iteration time of the largest network is minimized. We show that our mapping leads to near-optimal simulation of smaller instances of the neural network. In addition, based on our mapping no data migration or task rescheduling is needed as the size of network grows

Doctor of Philosophy

dissertationIn-memory big data applications are growing in popularity, including in-memory versions of the MapReduce framework. The move away from disk-based datasets shifts the performance bottleneck from slow disk accesses to memory bandwidth. MapReduce is a data-parallel application, and is therefore amenable to being executed on as many parallel processors as possible, with each processor requiring high amounts of memory bandwidth. We propose using Near Data Computing (NDC) as a means to develop systems that are optimized for in-memory MapReduce workloads, offering high compute parallelism and even higher memory bandwidth. This dissertation explores three different implementations and styles of NDC to improve MapReduce execution. First, we use 3D-stacked memory+logic devices to process the Map phase on compute elements in close proximity to database splits. Second, we attempt to replicate the performance characteristics of the 3D-stacked NDC using only commodity memory and inexpensive processors to improve performance of both Map and Reduce phases. Finally, we incorporate fixed-function hardware accelerators to improve sorting performance within the Map phase. This dissertation shows that it is possible to improve in-memory MapReduce performance by potentially two orders of magnitude by designing system and memory architectures that are specifically tailored to that end

Scheduled routing for the NuMesh

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 66-68).by Milan Singh Minsky.M.S

Sixth Goddard Conference on Mass Storage Systems and Technologies Held in Cooperation with the Fifteenth IEEE Symposium on Mass Storage Systems

This document contains copies of those technical papers received in time for publication prior to the Sixth Goddard Conference on Mass Storage Systems and Technologies which is being held in cooperation with the Fifteenth IEEE Symposium on Mass Storage Systems at the University of Maryland-University College Inn and Conference Center March 23-26, 1998. As one of an ongoing series, this Conference continues to provide a forum for discussion of issues relevant to the management of large volumes of data. The Conference encourages all interested organizations to discuss long term mass storage requirements and experiences in fielding solutions. Emphasis is on current and future practical solutions addressing issues in data management, storage systems and media, data acquisition, long term retention of data, and data distribution. This year's discussion topics include architecture, tape optimization, new technology, performance, standards, site reports, vendor solutions. Tutorials will be available on shared file systems, file system backups, data mining, and the dynamics of obsolescence

HPCCP/CAS Workshop Proceedings 1998

This publication is a collection of extended abstracts of presentations given at the HPCCP/CAS (High Performance Computing and Communications Program/Computational Aerosciences Project) Workshop held on August 24-26, 1998, at NASA Ames Research Center, Moffett Field, California. The objective of the Workshop was to bring together the aerospace high performance computing community, consisting of airframe and propulsion companies, independent software vendors, university researchers, and government scientists and engineers. The Workshop was sponsored by the HPCCP Office at NASA Ames Research Center. The Workshop consisted of over 40 presentations, including an overview of NASA's High Performance Computing and Communications Program and the Computational Aerosciences Project; ten sessions of papers representative of the high performance computing research conducted within the Program by the aerospace industry, academia, NASA, and other government laboratories; two panel sessions; and a special presentation by Mr. James Bailey

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Efficient mechanisms to provide fault tolerance in interconnection networks for pc clusters

Actualmente, los clusters de PC son un alternativa rentable a los computadores paralelos. En estos sistemas, miles de componentes (procesadores y/o discos duros) se conectan a través de redes de interconexión de altas prestaciones. Entre las tecnologías de red actualmente disponibles para construir clusters, InfiniBand (IBA) ha emergido como un nuevo estándar de interconexión para clusters. De hecho, ha sido adoptado por muchos de los sistemas más potentes construidos actualmente (lista top500). A medida que el número de nodos aumenta en estos sistemas, la red de interconexión también crece. Junto con el aumento del número de componentes la probabilidad de averías aumenta dramáticamente, y así, la tolerancia a fallos en el sistema en general, y de la red de interconexión en particular, se convierte en una necesidad. Desafortunadamente, la mayor parte de las estrategias de encaminamiento tolerantes a fallos propuestas para los computadores masivamente paralelos no pueden ser aplicadas porque el encaminamiento y las transiciones de canal virtual son deterministas en IBA, lo que impide que los paquetes eviten los fallos. Por lo tanto, son necesarias nuevas estrategias para tolerar fallos. Por ello, esta tesis se centra en proporcionar los niveles adecuados de tolerancia a fallos a los clusters de PC, y en particular a las redes IBA. En esta tesis proponemos y evaluamos varios mecanismos adecuados para las redes de interconexión para clusters. El primer mecanismo para proporcionar tolerancia a fallos en IBA (al que nos referimos como encaminamiento tolerante a fallos basado en transiciones; TFTR) consiste en usar varias rutas disjuntas entre cada par de nodos origen-destino y seleccionar la ruta apropiada en el nodo fuente usando el mecanismo APM proporcionado por IBA. Consiste en migrar las rutas afectadas por el fallo a las rutas alternativas sin fallos. Sin embargo, con este fin, es necesario un algoritmo eficiente de encaminamiento capaz de proporcionar suficientesMontañana Aliaga, JM. (2008). Efficient mechanisms to provide fault tolerance in interconnection networks for pc clusters [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/2603Palanci