An Empirical Study of Dynamic Scheduling on Rings of Processors

The authors empirically analyze and compare two distributed low-overhead policies for scheduling dynamic tree-structured computations on rings of identical PEs. The experiments show that both policies give significant parallel speedup on large classes of computations, and that one yields almost optimal speedup on moderate size rings. They believe that the methodology of experiment design and analysis will prove useful in other such studies

High-performance computing for vision

Vision is a challenging application for high-performance computing (HPC). Many vision tasks have stringent latency and throughput requirements. Further, the vision process has a heterogeneous computational profile. Low-level vision consists of structured computations, with regular data dependencies. The subsequent, higher level operations consist of symbolic computations with irregular data dependencies. Over the years, many approaches to high-speed vision have been pursued. VLSI hardware solutions such as ASIC's and digital signal processors (DSP's) have provided good processing speeds on structured low-level vision tasks. Special purpose systems for vision have also been designed. Currently, there is growing interest in using general purpose parallel systems for vision problems. These systems offer advantages of higher performance, sofavare programmability, generality, and architectural flexibility over the earlier approaches. The choice of low-cost commercial-off-theshelf (COTS) components as building blocks for these systems leads to easy upgradability and increased system life. The main focus of the paper is on effectively using the COTSbased general purpose parallel computing platforms to realize high-speed implementations of vision tasks. Due to the successful use of the COTS-based systems in a variety of high performance applications, it is attractive to consider their use for vision applications as well. However, the irregular data dependencies in vision tasks lead to large communication overheads in the HPC systems. At the University of Southern California, our research efforts have been directed toward designing scalable parallel algorithms for vision tasks on the HPC systems. In our approach, we use the message passing programming model to develop portable code. Our algorithms are specified using C and MPI. In this paper, we summarize our efforts, and illustrate our approach using several example vision tasks. To facilitate the analysis and development of scalable algorithms, a realistic computational model of the parallel system must be used. Several such models have been proposed in the literature. We use the General-purpose Distributed Memory (GDM) model which is a simple but realistic model of state-of-theart parallel machines. Using the GDM model, generic algorithmic techniques such as data remapping, overlapping of communication with computation, message packing, asynchronous execution, and communication scheduling are developed. Using these techniques, we have developed scalable algorithms for many vision tasks. For instance, a scalable algorithm for linear approximation has been developed using the asynchronous execution technique. Using this algorithm, linear feature extraction can be performed in 0.065 s on a 64 node SP-2 for a 512 × 512 image. A serial implementation takes 3.45 s for the same task. Similarly, the communication scheduling and decomposition techniques lead to a scalable algorithm for the line grouping task. We believe that such an algorithmic approach can result in the development of scalable and portable solutions for vision tasks. © 1996 IEEE Publisher Item Identifier S 0018-9219(96)04992-4.published_or_final_versio

Any Load-Balancing Regimen for Evolving Tree Computations on Circulant Graphs Is Asymptotically Optimal

Abstract. We analyze evolving tree computations on circulant (rings with “regular ” chords) and related graphs. In an evolving α-ary tree computation, a complete tree grows level by level, i. e., every leaf gener-ates α new nodes that become the new leaves. The load balancing task is to spread the new nodes on a network of processors in the moment they were created in such a way that the accumulated number of nodes per processor, i. e., its load, is as close as possible to the average number of nodes per processor. Gao/Rosenberg [2] introduced evolving compu-tations and investigated the growth of complete binary trees on rings of processors. They showed that the so-called ks-regimen behaves opti-mally in the course of long computations. In this paper, we generalize evolving computations to trees of arbitrary degree and we generalize the regimen notion. We show that any regimen behaves optimally. For this purpose, we model the actual load distribution, the generation process, and the distribution regimen by formal infinite polynomials. Then we show that evaluating these polynomials for certain inputs leads to the analysis of these regimens on circulant and related graphs. It is shown that any regimen leads to a close to optimal load distribution.

Three Highly Parallel Computer Architectures and Their Suitability for Three Representative Artificial Intelligence Problems

Virtually all current Artificial Intelligence (AI) applications are designed to run on sequential (von Neumann) computer architectures. As a result, current systems do not scale up. As knowledge is added to these systems, a point is reached where their performance quickly degrades. The performance of a von Neumann machine is limited by the bandwidth between memory and processor (the von Neumann bottleneck). The bottleneck is avoided by distributing the processing power across the memory of the computer. In this scheme the memory becomes the processor (a smart memory ). This paper highlights the relationship between three representative AI application domains, namely knowledge representation, rule-based expert systems, and vision, and their parallel hardware realizations. Three machines, covering a wide range of fundamental properties of parallel processors, namely module granularity, concurrency control, and communication geometry, are reviewed: the Connection Machine (a fine-grained SIMD hypercube), DADO (a medium-grained MIMD/SIMD/MSIMD tree-machine), and the Butterfly (a coarse-grained MIMD Butterflyswitch machine)

NASA space station automation: AI-based technology review. Executive summary

Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Semiannual report

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science during the period 1 Oct. 1994 - 31 Mar. 1995

Multilevel Parallel Communications

The research reported in this thesis investigates the use of parallelism at multiple levels to realize high-speed networks that offer advantages in throughput, cost, reliability, and flexibility over alternative approaches. This research specifically considers use of parallelism at two levels: the upper level and the lower level. At the upper level, N protocol processors perform functions included in the transport and network layers. At the lower level, M channels provide data and physical layer functions. The resulting system provides very high bandwidth to an application. A key concept of this research is the use of replicated channels to provide a single, high bandwidth channel to a single application. The parallelism provided by the network is transparent to communicating applications, thus differentiating this strategy from schemes that provide a collection of disjoint channels between applications on different nodes. Another innovative aspect of this research is that parallelism is exploited at multiple layers of the network to provide high throughput not only at the physical layer, but also at upper protocol layers. Schedulers are used to distribute data from a single stream to multiple channels and to merge data from multiple channels to reconstruct a single coherent stream. High throughput is possible by providing the combined bandwidth of multiple channels to a single source and destination through use of parallelism at multiple protocol layers. This strategy is cost effective since systems can be built using standard technologies that benefit from the economies of a broad applications base. The exotic and revolutionary components needed in non-parallel approaches to build high speed networks are not required. The replicated channels can be used to achieve high reliability as well. Multilevel parallelism is flexible since the degree of parallelism provided at any level can be matched to protocol processing demands and application requirements

Crowd simulation and visualization

Large-scale simulation and visualization are essential topics in areas as different as sociology, physics, urbanism, training, entertainment among others. This kind of systems requires a vast computational power and memory resources commonly available in High Performance Computing HPC platforms. Currently, the most potent clusters have heterogeneous architectures with hundreds of thousands and even millions of cores. The industry trends inferred that exascale clusters would have thousands of millions. The technical challenges for simulation and visualization process in the exascale era are intertwined with difficulties in other areas of research, including storage, communication, programming models and hardware. For this reason, it is necessary prototyping, testing, and deployment a variety of approaches to address the technical challenges identified and evaluate the advantages and disadvantages of each proposed solution. The focus of this research is interactive large-scale crowd simulation and visualization. To exploit to the maximum the capacity of the current HPC infrastructure and be prepared to take advantage of the next generation. The project develops a new approach to scale crowd simulation and visualization on heterogeneous computing cluster using a task-based technique. Its main characteristic is hardware agnostic. It abstracts the difficulties that imply the use of heterogeneous architectures like memory management, scheduling, communications, and synchronization — facilitating development, maintenance, and scalability. With the goal of flexibility and take advantage of computing resources as best as possible, the project explores different configurations to connect the simulation with the visualization engine. This kind of system has an essential use in emergencies. Therefore, urban scenes were implemented as realistic as possible; in this way, users will be ready to face real events. Path planning for large-scale crowds is a challenge to solve, due to the inherent dynamism in the scenes and vast search space. A new path-finding algorithm was developed. It has a hierarchical approach which offers different advantages: it divides the search space reducing the problem complexity, it can obtain a partial path instead of wait for the complete one, which allows a character to start moving and compute the rest asynchronously. It can reprocess only a part if necessary with different levels of abstraction. A case study is presented for a crowd simulation in urban scenarios. Geolocated data are used, they were produced by mobile devices to predict individual and crowd behavior and detect abnormal situations in the presence of specific events. It was also address the challenge of combining all these individual’s location with a 3D rendering of the urban environment. The data processing and simulation approach are computationally expensive and time-critical, it relies thus on a hybrid Cloud-HPC architecture to produce an efficient solution. Within the project, new models of behavior based on data analytics were developed. It was developed the infrastructure to be able to consult various data sources such as social networks, government agencies or transport companies such as Uber. Every time there is more geolocation data available and better computation resources which allow performing analysis of greater depth, this lays the foundations to improve the simulation models of current crowds. The use of simulations and their visualization allows to observe and organize the crowds in real time. The analysis before, during and after daily mass events can reduce the risks and associated logistics costs.La simulación y visualización a gran escala son temas esenciales en áreas tan diferentes como la sociología, la física, el urbanismo, la capacitación, el entretenimiento, entre otros. Este tipo de sistemas requiere una gran capacidad de cómputo y recursos de memoria comúnmente disponibles en las plataformas de computo de alto rendimiento. Actualmente, los equipos más potentes tienen arquitecturas heterogéneas con cientos de miles e incluso millones de núcleos. Las tendencias de la industria infieren que los equipos en la era exascale tendran miles de millones. Los desafíos técnicos en el proceso de simulación y visualización en la era exascale se entrelazan con dificultades en otras áreas de investigación, incluidos almacenamiento, comunicación, modelos de programación y hardware. Por esta razón, es necesario crear prototipos, probar y desplegar una variedad de enfoques para abordar los desafíos técnicos identificados y evaluar las ventajas y desventajas de cada solución propuesta. El foco de esta investigación es la visualización y simulación interactiva de multitudes a gran escala. Aprovechar al máximo la capacidad de la infraestructura actual y estar preparado para aprovechar la próxima generación. El proyecto desarrolla un nuevo enfoque para escalar la simulación y visualización de multitudes en un clúster de computo heterogéneo utilizando una técnica basada en tareas. Su principal característica es que es hardware agnóstico. Abstrae las dificultades que implican el uso de arquitecturas heterogéneas como la administración de memoria, las comunicaciones y la sincronización, lo que facilita el desarrollo, el mantenimiento y la escalabilidad. Con el objetivo de flexibilizar y aprovechar los recursos informáticos lo mejor posible, el proyecto explora diferentes configuraciones para conectar la simulación con el motor de visualización. Este tipo de sistemas tienen un uso esencial en emergencias. Por lo tanto, se implementaron escenas urbanas lo más realistas posible, de esta manera los usuarios estarán listos para enfrentar eventos reales. La planificación de caminos para multitudes a gran escala es un desafío a resolver, debido al dinamismo inherente en las escenas y el vasto espacio de búsqueda. Se desarrolló un nuevo algoritmo de búsqueda de caminos. Tiene un enfoque jerárquico que ofrece diferentes ventajas: divide el espacio de búsqueda reduciendo la complejidad del problema, puede obtener una ruta parcial en lugar de esperar a la completa, lo que permite que un personaje comience a moverse y calcule el resto de forma asíncrona, puede reprocesar solo una parte si es necesario con diferentes niveles de abstracción. Se presenta un caso de estudio para una simulación de multitud en escenarios urbanos. Se utilizan datos geolocalizados producidos por dispositivos móviles para predecir el comportamiento individual y público y detectar situaciones anormales en presencia de eventos específicos. También se aborda el desafío de combinar la ubicación de todos estos individuos con una representación 3D del entorno urbano. Dentro del proyecto, se desarrollaron nuevos modelos de comportamiento basados ¿¿en el análisis de datos. Se creo la infraestructura para poder consultar varias fuentes de datos como redes sociales, agencias gubernamentales o empresas de transporte como Uber. Cada vez hay más datos de geolocalización disponibles y mejores recursos de cómputo que permiten realizar un análisis de mayor profundidad, esto sienta las bases para mejorar los modelos de simulación de las multitudes actuales. El uso de simulaciones y su visualización permite observar y organizar las multitudes en tiempo real. El análisis antes, durante y después de eventos multitudinarios diarios puede reducir los riesgos y los costos logísticos asociadosPostprint (published version