Improving the scalability of parallel N-body applications with an event driven constraint based execution model

The scalability and efficiency of graph applications are significantly constrained by conventional systems and their supporting programming models. Technology trends like multicore, manycore, and heterogeneous system architectures are introducing further challenges and possibilities for emerging application domains such as graph applications. This paper explores the space of effective parallel execution of ephemeral graphs that are dynamically generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The workloads are expressed using the semantics of an Exascale computing execution model called ParalleX. For comparison, results using conventional execution model semantics are also presented. We find improved load balancing during runtime and automatic parallelism discovery improving efficiency using the advanced semantics for Exascale computing.Comment: 11 figure

PARALLEX FILE SYSTEM (PXFS): BRIDGING THE GAP BETWEEN EXASCALE PROCESSING CAPABILITIES AND I/O PERFORMANCE

Due to processors reaching the maximum performance allowable by current technology, architectural trends for computer systems continue to increase the number of cores per processing chip to maximize system performance. Most estimates suggest massively parallel systems will be available within the decade, containing millions of cores and capable of exaFlops of performance. New models of execution are necessary to maximize processor utilization and minimize power costs for these exascale systems. ParalleX is one such execution model, which attempts to address inefficiencies of current execution models by exposing fine-grained parallelism, increasing system utilization using asynchronous workflow, and resolving resource contention through the use of adaptive and dynamic resource scheduling. A particularly important aspect of these exascale execution models is the design of the I/O subsystem, which has seen limited performance increases compared to processor and network technologies. Parallel file systems have been designed to help alleviate the poor performance of storage technologies by distributing file data across multiple nodes of a parallel system to maximize the aggregate throughput attainable by file system clients. However, the design of parallel file systems needs to be modified to explicitly address the inherent high-latency of remote file system operations without degrading file system performance and scalability. We present modifications to OrangeFS, a high-performance, working model parallel file system geared towards the facilitation of research in the field of parallel I/O, to help address the inefficiencies of current file systems. We deem our resultant parallel file system implementation ParalleX File System (PXFS), as it attempts to support the features required by the I/O subsystem of the ParalleX execution model. Specifically, PXFS offers mechanisms for masking the latency of file system operations, defining meaningful computation to be overlapped with file system communication, and maintaining the high-performance and scalability exhibited by OrangeFS. Our results indicate PXFS successfully improves file system performance and supports the semantics of ParalleX with limited programmer intervention, potentially simplifying the design and increasing the performance of many ParalleX applications

Parallel Processes in HPX: Designing an Infrastructure for Adaptive Resource Management

Advancement in cutting edge technologies have enabled better energy efficiency as well as scaling computational power for the latest High Performance Computing(HPC) systems. However, complexity, due to hybrid architectures as well as emerging classes of applications, have shown poor computational scalability using conventional execution models. Thus alternative means of computation, that addresses the bottlenecks in computation, is warranted. More precisely, dynamic adaptive resource management feature, both from systems as well as application\u27s perspective, is essential for better computational scalability and efficiency. This research presents and expands the notion of Parallel Processes as a placeholder for procedure definitions, targeted at one or more synchronous domains, meta data for computation and resource management as well as infrastructure for dynamic policy deployment. In addition to this, the research presents additional guidelines for a framework for resource management in HPX runtime system. Further, this research also lists design principles for scalability of Active Global Address Space (AGAS), a necessary feature for Parallel Processes. Also, to verify the usefulness of Parallel Processes, a preliminary performance evaluation of different task scheduling policies is carried out using two different applications. The applications used are: Unbalanced Tree Search, a reference dynamic graph application, implemented by this research in HPX and MiniGhost, a reference stencil based application using bulk synchronous parallel model. The results show that different scheduling policies provide better performance for different classes of applications; and for the same application class, in certain instances, one policy fared better than the others, while vice versa in other instances, hence supporting the hypothesis of the need of dynamic adaptive resource management infrastructure, for deploying different policies and task granularities, for scalable distributed computing

A Persistent Storage Model for Extreme Computing

The continuing technological progress resulted in a dramatic growth in aggregate computational performance of the largest supercomputing systems. Unfortunately, these advances did not translate to the required extent into accompanying I/O systems and little more in terms of architecture or effective access latency. New classes of algorithms developed for massively parallel applications, that gracefully handle the challenges of asynchrony, heavily multi-threaded distributed codes, and message-driven computation, must be matched by similar advances in I/O methods and algorithms to produce a well performing and balanced supercomputing system. This dissertation proposes PXFS, a storage model for persistent objects inspired by the ParalleX model of execution that addresses many of these challenges. The PXFS model is designed to be asynchronous in nature to comply with ParalleX model and proposes an active TupleSpace concept to hold all kinds of metadata/meta-object for either storage objects or runtime objects. The new active TupleSpace can also register ParalleX actions to be triggered under certain tuple operations. An first implementation of PXFS utilizing a well-known Orange parallel file system as its back-end via asynchronous I/O layer and the implementation of TupleSpace component in HPX, the implementation of ParalleX. These details are also described along with the preliminary performance data. A house-made micro benchmark is developed to measure the disk I/O throughput of the PXFS asynchronous interface. The results show perfect scalability and 3x to 20x times speedup of I/O throughput performance comparing to OrangeFS synchronous user interface. Use cases of TupleSpace components are discussed for real-world applications including micro check-pointing. By utilizing TupleSpace in HPX applications for I/O, global barrier can be replaced with fine-grained parallelism to overlap more computation with communication and greatly boost the performance and efficiency. Also the dissertation showcases the distributed directory service in Orange file system which process directory entries in parallel and effectively improves the directory metada operations

Natural Disaster Detection Using Wavelet and Artificial Neural Network

Indonesia, by the location of its geographic and geologic, it have more potential encounters for natural disasters. This nation is traversed by three tectonic plates, namely: IndoAustralian, the Eurasian and the Pacific plates. One of the tools employed to detect danger and send an early disaster warning is sensor device for ocean waves, but it has drawbacks related to the very limited time gap between information/warnings obtained and the real disaster event, which is only less than 30 minutes. Natural disaster early detection information system is essential to prevent potential danger. The system can make use of the pattern recognition of satellite imagery sequences that take place before and during the natural disaster. This study is conducted to determine the right wavelet to compress the satellite image sequences and to perform the pattern recognition process of a natural disaster employing an artificial neural network. This study makes use of satellite imagery sequences of tornadoes and hurricanes

Modular space station detailed preliminary design. Volume 3: Sections 4.9 through 6

For abstract, see N72-21880

1986-1987 Louisiana Tech University Catalog

The Louisiana Tech University Catalog includes announcements and course descriptions for courses offered at Louisiana Tech University for the academic year of 1986-1987.https://digitalcommons.latech.edu/university-catalogs/1028/thumbnail.jp