Massively Parallel Simulation of Structured Connectionist Networks: An Interim Report

We map structured connectionist models of knowledge representation and reasoning onto existing general purpose massively parallel architectures with the objective of developing and implementing practical, real-time knowledge base systems. Shruti, a connectionist knowledge representation and reasoning system which attempts to model reflexive reasoning, will serve as our representative connectionist model. Efficient simulation systems for shruti are developed on the Connection Machine CM-2 - an SIMD architecture - and on the Connection Machine CM-5 - an MIMD architecture. The resulting simulators are evaluated and tested using large, random knowledge bases with up to half a million rules and facts. Though SIMD simulations on the CM-2 are reasonably fast - requiring a few seconds to tens of seconds for answering simple queries - experiments indicate that MIMD simulations are vastly superior to SIMD simulations and offer hundred- to thousand-fold speedups. This work provides new insights into the simulation of structured connectionist networks on massively parallel machines and is a step toward developing large yet efficient knowledge representation and reasoning systems

Coupling DSM-based Parallel Applications

When coupling applications running on distributed memory architectures or clusters, the coupling library must adapt to the distribution of the data in the memory of each computation node. The library must be prepared to redistribute the data when the coupled applications use different data mappings or when the number of processors of the two architectures are different. Mome is a user-level software DSM which allows programs running on a distributed memory architecture or cluster to create segments and to share data objects through memory mapping. The segments of the DSM form a simple linear address space where all shared objects of applications are allocated. The Mome coupling library accesses the data through mappings of the DSM segments on the memories of the communication threads. The parallel communication threads are distributed on the computation nodes and exploit the communication capacity of each processor. The data are moved directly between the DSM segments and the transfers do not rely on any knowledge on the application use of these segments

Fast data parallel polygon rendering

Journal ArticleThis paper describes a data parallel method for polygon rendering on a massively parallel machine. This method, based on a simple shading model, is targeted for applications which require very fast rendering for extremely large sets of polygons. Such sets are found in many scienti c visualization applications. The renderer can handle arbitrarily complex polygons which need not be meshed. Issues involving load balancing are addressed and a data parallel load balancing algorithm is presented. The rendering and load balancing algorithms are implemented on both the CM-200 and the CM-5. Experimental results are presented. This rendering toolkit enables a scientist to display 3D shaded polygons directly from a parallel machine avoiding the transmission of huge amounts of data to a post-processing rendering system

スケジューリング遅延に基づいたタスク並列ランタイムシステムの性能差の解析

学位の種別: 課程博士審査委員会委員 : （主査）東京大学准教授 豊田 正史, 東京大学教授 田浦 健次朗, 東京大学准教授 入江 英嗣, 東京大学教授 中島 研吾, 理化学研究所チームリーダ 佐藤 三久, 東京工業大学准教授 横田 理央University of Tokyo(東京大学

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

The efficient parallel execution of scientific applications is a key challenge in high-performance computing (HPC). With growing parallelism and heterogeneity of compute resources as well as increasingly complex software, performance analysis has become an indispensable tool in the development and optimization of parallel programs. This thesis presents a framework for systematic performance analysis of scalable, heterogeneous applications. Based on event traces, it automatically detects the critical path and inefficiencies that result in waiting or idle time, e.g. due to load imbalances between parallel execution streams. As a prerequisite for the analysis of heterogeneous programs, this thesis specifies inefficiency patterns for computation offloading. Furthermore, an essential contribution was made to the development of tool interfaces for OpenACC and OpenMP, which enable a portable data acquisition and a subsequent analysis for programs with offload directives. At present, these interfaces are already part of the latest OpenACC and OpenMP API specification. The aforementioned work, existing preliminary work, and established analysis methods are combined into a generic analysis process, which can be applied across programming models. Based on the detection of wait or idle states, which can propagate over several levels of parallelism, the analysis identifies wasted computing resources and their root cause as well as the critical-path share for each program region. Thus, it determines the influence of program regions on the load balancing between execution streams and the program runtime. The analysis results include a summary of the detected inefficiency patterns and a program trace, enhanced with information about wait states, their cause, and the critical path. In addition, a ranking, based on the amount of waiting time a program region caused on the critical path, highlights program regions that are relevant for program optimization. The scalability of the proposed performance analysis and its implementation is demonstrated using High-Performance Linpack (HPL), while the analysis results are validated with synthetic programs. A scientific application that uses MPI, OpenMP, and CUDA simultaneously is investigated in order to show the applicability of the analysis

Enhancing functionality and performance in the PVM network computing system

The research funded by this grant is part of an ongoing research project in heterogeneous distributed computing with the PVM system, at Emory as well as at Oak Ridge Labs and the University of Tennessee. This grant primarily supports research at Emory that continues to evolve new concepts and systems in distributed computing, but it also includes the PI`s ongoing interaction with the other groups in terms of collaborative research as well as software systems development and maintenance. We have continued our second year efforts (July 1995 - June 1996), on the same topics as during the first year, namely (a) visualization of PVM programs to complement XPVM displays; (b) I/O and generalized distributed computing in PVM; and (c) evolution of a multithreaded concurrent computing model. 12 refs

Enhanced clustering analysis pipeline for performance analysis of parallel applications

Clustering analysis is widely used to stratify data in the same cluster when they are similar according to the specific metrics. We can use the cluster analysis to group the CPU burst of a parallel application, and the regions on each process in-between communication calls or calls to the parallel runtime. The resulting clusters obtained are the different computational trends or phases that appear in the application. These clusters are useful to understand the behavior of the computation part of the application and focus the analyses on those that present performance issues. Although density-based clustering algorithms are a powerful and efficient tool to summarize this type of information, their traditional user-guided clustering methodology has many shortcomings and deficiencies in dealing with the complexity of data, the diversity of data structures, high-dimensionality of data, and the dramatic increase in the amount of data. Consequently, the majority of DBSCAN-like algorithms have weaknesses to handle high-dimensionality and/or Multi-density data, and they are sensitive to their hyper-parameter configuration. Furthermore, extracting insight from the obtained clusters is an intuitive and manual task. To mitigate these weaknesses, we have proposed a new unified approach to replace the user-guided clustering with an automated clustering analysis pipeline, called Enhanced Cluster Identification and Interpretation (ECII) pipeline. To build the pipeline, we propose novel techniques including Robust Independent Feature Selection, Feature Space Curvature Map, Organization Component Analysis, and hyper-parameters tuning to feature selection, density homogenization, cluster interpretation, and model selection which are the main components of our machine learning pipeline. This thesis contributes four new techniques to the Machine Learning field with a particular use case in Performance Analytics field. The first contribution is a novel unsupervised approach for feature selection on noisy data, called Robust Independent Feature Selection (RIFS). Specifically, we choose a feature subset that contains most of the underlying information, using the same criteria as the Independent component analysis. Simultaneously, the noise is separated as an independent component. The second contribution of the thesis is a parametric multilinear transformation method to homogenize cluster densities while preserving the topological structure of the dataset, called Feature Space Curvature Map (FSCM). We present a new Gravitational Self-organizing Map to model the feature space curvature by plugging the concepts of gravity and fabric of space into the Self-organizing Map algorithm to mathematically describe the density structure of the data. To homogenize the cluster density, we introduce a novel mapping mechanism to project the data from the non-Euclidean curved space to a new Euclidean flat space. The third contribution is a novel topological-based method to study potentially complex high-dimensional categorized data by quantifying their shapes and extracting fine-grain insights from them to interpret the clustering result. We introduce our Organization Component Analysis (OCA) method for the automatic arbitrary cluster-shape study without an assumption about the data distribution. Finally, to tune the DBSCAN hyper-parameters, we propose a new tuning mechanism by combining techniques from machine learning and optimization domains, and we embed it in the ECII pipeline. Using this cluster analysis pipeline with the CPU burst data of a parallel application, we provide the developer/analyst with a high-quality SPMD computation structure detection with the added value that reflects the fine grain of the computation regions.El análisis de conglomerados se usa ampliamente para estratificar datos en el mismo conglomerado cuando son similares según las métricas específicas. Nosotros puede usar el análisis de clúster para agrupar la ráfaga de CPU de una aplicación paralela y las regiones en cada proceso intermedio llamadas de comunicación o llamadas al tiempo de ejecución paralelo. Los clusters resultantes obtenidos son las diferentes tendencias computacionales o fases que aparecen en la solicitud. Estos clusters son útiles para entender el comportamiento de la parte de computación del aplicación y centrar los análisis en aquellos que presenten problemas de rendimiento. Aunque los algoritmos de agrupamiento basados en la densidad son una herramienta poderosa y eficiente para resumir este tipo de información, su La metodología tradicional de agrupación en clústeres guiada por el usuario tiene muchas deficiencias y deficiencias al tratar con la complejidad de los datos, la diversidad de estructuras de datos, la alta dimensionalidad de los datos y el aumento dramático en la cantidad de datos. En consecuencia, el La mayoría de los algoritmos similares a DBSCAN tienen debilidades para manejar datos de alta dimensionalidad y/o densidad múltiple, y son sensibles a su configuración de hiperparámetros. Además, extraer información de los clústeres obtenidos es una forma intuitiva y tarea manual Para mitigar estas debilidades, hemos propuesto un nuevo enfoque unificado para reemplazar el agrupamiento guiado por el usuario con un canalización de análisis de agrupamiento automatizado, llamada canalización de identificación e interpretación de clúster mejorada (ECII). para construir el tubería, proponemos técnicas novedosas que incluyen la selección robusta de características independientes, el mapa de curvatura del espacio de características, Análisis de componentes de la organización y ajuste de hiperparámetros para la selección de características, homogeneización de densidad, agrupación interpretación y selección de modelos, que son los componentes principales de nuestra canalización de aprendizaje automático. Esta tesis aporta cuatro nuevas técnicas al campo de Machine Learning con un caso de uso particular en el campo de Performance Analytics. La primera contribución es un enfoque novedoso no supervisado para la selección de características en datos ruidosos, llamado Robust Independent Feature. Selección (RIFS).Específicamente, elegimos un subconjunto de funciones que contiene la mayor parte de la información subyacente, utilizando el mismo criterios como el análisis de componentes independientes. Simultáneamente, el ruido se separa como un componente independiente. La segunda contribución de la tesis es un método de transformación multilineal paramétrica para homogeneizar densidades de clústeres mientras preservando la estructura topológica del conjunto de datos, llamado Mapa de Curvatura del Espacio de Características (FSCM). Presentamos un nuevo Gravitacional Mapa autoorganizado para modelar la curvatura del espacio característico conectando los conceptos de gravedad y estructura del espacio en el Algoritmo de mapa autoorganizado para describir matemáticamente la estructura de densidad de los datos. Para homogeneizar la densidad del racimo, introducimos un mecanismo de mapeo novedoso para proyectar los datos del espacio curvo no euclidiano a un nuevo plano euclidiano espacio. La tercera contribución es un nuevo método basado en topología para estudiar datos categorizados de alta dimensión potencialmente complejos mediante cuantificando sus formas y extrayendo información detallada de ellas para interpretar el resultado de la agrupación. presentamos nuestro Método de análisis de componentes de organización (OCA) para el estudio automático de forma arbitraria de conglomerados sin una suposición sobre el distribución de datos.Postprint (published version