High Performance Computing Applications in Remote Sensing Studies for Land Cover Dynamics

Global and regional land cover studies require the ability to apply complex models on selected subsets of large amounts of multi-sensor and multi-temporal data sets that have been derived from raw instrument measurements using widely accepted pre-processing algorithms. The computational and storage requirements of most such studies far exceed what is possible on a single workstation environment. We have been pursuing a new approach that couples scalable and open distributed heterogeneous hardware with the development of high performance software for processing, indexing, and organizing remotely sensed data. Hierarchical data management tools are used to ingest raw data, create metadata, and organize the archived data so as to automatically achieve computational load balancing among the available nodes and minimize I/O overheads. We illustrate our approach with four specific examples. The first is the development of the first fast operational scheme for the atmospheric correction of Landsat TM scenes, while the second example focuses on image segmentation using a novel hierarchical connected components algorithm. Retrieval of global BRDF (Bidirectional Reflectance Distribution Function) in the red and near infrared wavelengths using four years (1983 to 1986) of Pathfinder AVHRR Land (PAL) data set is the focus of our third example. The fourth example is the development of a hierarchical data organization scheme that allows on-demand processing and retrieval of regional and global AVHRR data sets. Our results show that substantial improvements in computational times can be achieved by using the high performance computing technology

Parallel Algorithms for Image Histogramming and Connected Components with an Experimental Study

This paper presents efficient and portable implementations of two useful primitives in image processing algorithms, histogramming and connected components. Our general framework is a single-address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. Our connected components algorithm uses a novel approach for parallel merging which performs drastically limited updating during iterative steps, and concludes with a total consistency update at the final step. The algorithms have been coded in Split-C and run on a variety of platforms. Our experimental results are consistent with the theoretical analysis and provide the best known execution times for these two primitives, even when compared with machine specific implementations. More efficient implementations of Split-C will likely result in even faster execution times. (Also cross-referenced as UMIACS-TR-94-133.

Procesamiento paralelo : Balance de carga dinámico en algoritmo de sorting

Algunas técnicas de sorting intentan balancear la carga mediante un muestreo inicial de los datos a ordenar y una distribución de los mismos de acuerdo a pivots. Otras redistribuyen listas parcialmente ordenadas de modo que cada procesador almacene un número aproximadamente igual de claves, y todos tomen parte del proceso de merge durante la ejecución. Esta Tesis presenta un nuevo método que balancea dinámicamente la carga basado en un enfoque diferente, buscando realizar una distribución del trabajo utilizando un estimador que permita predecir la carga de trabajo pendiente. El método propuesto es una variante de Sorting by Merging Paralelo, esto es, una técnica basada en comparación. Las ordenaciones en los bloques se realizan mediante el método de Burbuja o Bubble Sort con centinela. En este caso, el trabajo a realizar -en términos de comparaciones e intercambios- se encuentra afectada por el grado de desorden de los datos. Se estudió la evolución de la cantidad de trabajo en cada iteración del algoritmo para diferentes tipos de secuencias de entrada, n datos con valores de a n sin repetición, datos al azar con distribución normal, observándose que el trabajo disminuye en cada iteración. Esto se utilizó para obtener una estimación del trabajo restante esperado a partir de una iteración determinada, y basarse en el mismo para corregir la distribución de la carga. Con esta idea, el métoEs revisado por: http://sedici.unlp.edu.ar/handle/10915/9500Facultad de Ciencias Exacta