362 research outputs found

    REU Site: Supercomputing Undergraduate Program in Maine (SuperMe)

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    This award, for a new Research Experience for Undergraduates (REU) site, builds a Supercomputing Undergraduate Program in Maine (SuperMe). This new site provides ten-week summer research experiences at the University of Maine (UMaine) for ten undergraduates each year for three years. With integrated expertise of ten faculty researchers from both computer systems and domain applications, SuperMe allows each undergraduate to conduct meaningful research, such as developing supercomputing techniques and tools, and solving cutting-edge research problems through parallel computing and scientific visualization. Besides being actively involved in research groups, students attend weekly seminars given by faculty mentors, formally report and present their research experiences and results, conduct field trips, and interact with ITEST, RET and GK-12 participants. SuperMe provides scientific exploration ranging from engineering to sciences with a coherent intellectual focus on supercomputing. It consists of four computer systems projects that aim to improve techniques in grid computing, parallel I/O data accesses, high-resolution scientific visualization and information security, and five computer modeling projects that utilize world-class supercomputing and visualization facilities housed at UMaine to perform large, complex simulation experiments and data analysis in different science domains. SuperMe provides a diversity of cutting-edge research opportunities to students from under-represented groups or from universities in rural areas with limited research opportunities. Through interacting directly with the participant of existing programs at UMaine, including ITEST, RET and GK-12, REU students disseminates their research results and experiences to middle and high school students and teachers. This site is co-funded by the Department of Defense in partnership with the NSF REU Site program

    GPU Computing for Cognitive Robotics

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    This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.European Commission Seventh Framework Programm

    GPU optimizations for a production molecular docking code

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    Thesis (M.Sc.Eng.) -- Boston UniversityScientists have always felt the desire to perform computationally intensive tasks that surpass the capabilities of conventional single core computers. As a result of this trend, Graphics Processing Units (GPUs) have come to be increasingly used for general computation in scientific research. This field of GPU acceleration is now a vast and mature discipline. Molecular docking, the modeling of the interactions between two molecules, is a particularly computationally intensive task that has been the subject of research for many years. It is a critical simulation tool used for the screening of protein compounds for drug design and in research of the nature of life itself. The PIPER molecular docking program was previously accelerated using GPUs, achieving a notable speedup over conventional single core implementation. Since its original release the development of the CPU based PIPER has not ceased, and it is now a mature and fast parallel code. The GPU version, however, still contains many potential points for optimization. In the current work, we present a new version of GPU PIPER that attains a 3.3x speedup over a parallel MPI version of PIPER running on an 8 core machine and using the optimized Intel Math Kernel Library. We achieve this speedup by optimizing existing kernels for modern GPU architectures and migrating critical code segments to the GPU. In particular, we both improve the runtime of the filtering and scoring stages by more than an order of magnitude, and move all molecular data permanently to the GPU to improve data locality. This new speedup is obtained while retaining a computational accuracy virtually identical to the CPU based version. We also demonstrate that, due to the algorithmic dependencies of the PIPER algorithm on the 3D Fast Fourier Transform, our GPU PIPER will likely remain proportionally faster than equivalent CPU based implementations, and with little room for further optimizations. This new GPU accelerated version of PIPER is integrated as part of the ClusPro molecular docking and analysis server at Boston University. ClusPro has over 4000 registered users and more than 50000 jobs run over the past 4 years

    Parallel bio-inspired methods for model optimization and pattern recognition

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    Nature based computational models are usually inherently parallel. The collaborative intelligence in those models emerges from the simultaneous instruction processing by simple independent units (neurons, ants, swarm members, etc...). This dissertation investigates the benefits of such parallel models in terms of efficiency and accuracy. First, the viability of a parallel implementation of bio-inspired metaheuristics for function optimization on consumer-level graphic cards is studied in detail. Then, in an effort to expose those parallel methods to the research community, the metaheuristic implementations were abstracted and grouped in an open source parameter/function optimization library libCudaOptimize. The library was verified against a well known benchmark for mathematical function minimization, and showed significant gains in both execution time and minimization accuracy. Crossing more into the application side, a parallel model of the human neocortex was developed. This model is able to detect, classify, and predict patterns in time-series data in an unsupervised way. Finally, libCudaOptimize was used to find the best parameters for this neocortex model, adapting it to gesture recognition within publicly available datasets

    Overcoming Challenges in Predictive Modeling of Laser-Plasma Interaction Scenarios. The Sinuous Route from Advanced Machine Learning to Deep Learning

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    The interaction of ultrashort and intense laser pulses with solid targets and dense plasmas is a rapidly developing area of physics, this being mostly due to the significant advancements in laser technology. There is, thus, a growing interest in diagnosing as accurately as possible the numerous phenomena related to the absorption and reflection of laser radiation. At the same time, envisaged experiments are in high demand of increased accuracy simulation software. As laser-plasma interaction modelings are experiencing a transition from computationally-intensive to data-intensive problems, traditional codes employed so far are starting to show their limitations. It is in this context that predictive modelings of laser-plasma interaction experiments are bound to reshape the definition of simulation software. This chapter focuses an entire class of predictive systems incorporating big data, advanced machine learning algorithms and deep learning, with improved accuracy and speed. Making use of terabytes of already available information (literature as well as simulation and experimental data) these systems enable the discovery and understanding of various physical phenomena occurring during interaction, hence allowing researchers to set up controlled experiments at optimal parameters. A comparative discussion in terms of challenges, advantages, bottlenecks, performances and suitability of laser-plasma interaction predictive systems is ultimately provided

    Crowd simulation and visualization

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    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

    A Heterogeneous High Performance Computing Framework For Ill-Structured Spatial Join Processing

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    The frequently employed spatial join processing over two large layers of polygonal datasets to detect cross-layer polygon pairs (CPP) satisfying a join-predicate faces challenges common to ill-structured sparse problems, namely, that of identifying the few intersecting cross-layer edges out of the quadratic universe. The algorithmic engineering challenge is compounded by GPGPU SIMT architecture. Spatial join involves lightweight filter phase typically using overlap test over minimum bounding rectangles (MBRs) to discard majority of CPPs, followed by refinement phase to rigorously test the join predicate over the edges of the surviving CPPs. In this dissertation, we develop new techniques - algorithms, data structure, i/o, load balancing and system implementation - to accelerate the two-phase spatial-join processing. We present a new filtering technique, called Common MBR Filter (CMF), which changes the overall characteristic of the spatial join algorithms wherein the refinement phase is no longer the computational bottleneck. CMF is designed based on the insight that intersecting cross-layer edges must lie within the rectangular intersection of the MBRs of CPPs, their common MBRs (CMBR). We also address a key limitation of CMF for class of spatial datasets with either large or dense active CMBRs by extended CMF, called CMF-grid, that effectively employs both CMBR and grid techniques by embedding a uniform grid over CMBR of each CPP, but of suitably engineered sizes for different CPPs. To show efficiency of CMF-based filters, extensive mathematical and experimental analysis is provided. Then, two GPU-based spatial join systems are proposed based on two CMF versions including four components: 1) sort-based MBR filter, 2) CMF/CMF-grid, 3) point-in-polygon test, and, 4) edge-intersection test. The systems show two orders of magnitude speedup over the optimized sequential GEOS C++ library. Furthermore, we present a distributed system of heterogeneous compute nodes to exploit GPU-CPU computing in order to scale up the computation. A load balancing model based on Integer Linear Programming (ILP) is formulated for this system. We also provide three heuristic algorithms to approximate the ILP. Finally, we develop MPI-cuda-GIS system based on this heterogeneous computing model by integrating our CUDA-based GPU system into a newly designed distributed framework designed based on Message Passing Interface (MPI). Experimental results show good scalability and performance of MPI-cuda-GIS system

    Architectural explorations for streaming accelerators with customized memory layouts

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    El concepto básico de la arquitectura mono-nucleo en los procesadores de propósito general se ajusta bien a un modelo de programación secuencial. La integración de multiples núcleos en un solo chip ha permitido a los procesadores correr partes del programa en paralelo. Sin embargo, la explotación del enorme paralelismo disponible en muchas aplicaciones de alto rendimiento y de los datos correspondientes es difícil de conseguir usando unicamente multicores de propósito general. La aparición de aceleradores tipo streaming y de los correspondientes modelos de programación han mejorado esta situación proporcionando arquitecturas orientadas al proceso de flujos de datos. La idea básica detrás del diseño de estas arquitecturas responde a la necesidad de procesar conjuntos enormes de datos. Estos dispositivos de alto rendimiento orientados a flujos permiten el procesamiento rapido de datos mediante el uso eficiente de computación paralela y comunicación entre procesos. Los aceleradores streaming orientados a flujos, igual que en otros procesadores, consisten en diversos componentes micro-arquitectonicos como por ejemplo las estructuras de memoria, las unidades de computo, las unidades de control, los canales de Entrada/Salida y controles de Entrada/Salida, etc. Sin embargo, los requisitos del flujo de datos agregan algunas características especiales e imponen otras restricciones que afectan al rendimiento. Estos dispositivos, por lo general, ofrecen un gran número de recursos computacionales, pero obligan a reorganizar los conjuntos de datos en paralelo, maximizando la independiencia para alimentar los recursos de computación en forma de flujos. La disposición de datos en conjuntos independientes de flujos paralelos no es una tarea sencilla. Es posible que se tenga que cambiar la estructura de un algoritmo en su conjunto o, incluso, puede requerir la reescritura del algoritmo desde cero. Sin embargo, todos estos esfuerzos para la reordenación de los patrones de las aplicaciones de acceso a datos puede que no sean muy útiles para lograr un rendimiento óptimo. Esto es debido a las posibles limitaciones microarquitectonicas de la plataforma de destino para los mecanismos hardware de prefetch, el tamaño y la granularidad del almacenamiento local, y la flexibilidad para disponer de forma serial los datos en el interior del almacenamiento local. Las limitaciones de una plataforma de streaming de proposito general para el prefetching de datos, almacenamiento y demas procedimientos para organizar y mantener los datos en forma de flujos paralelos e independientes podría ser eliminado empleando técnicas a nivel micro-arquitectonico. Esto incluye el uso de memorias personalizadas especificamente para las aplicaciones en el front-end de una arquitectura streaming. El objetivo de esta tesis es presentar exploraciones arquitectónicas de los aceleradores streaming con diseños de memoria personalizados. En general, la tesis cubre tres aspectos principales de tales aceleradores. Estos aspectos se pueden clasificar como: i) Diseño de aceleradores de aplicaciones específicas con diseños de memoria personalizados, ii) diseño de aceleradores con memorias personalizadas basados en plantillas, y iii) exploraciones del espacio de diseño para dispositivos orientados a flujos con las memorias estándar y personalizadas. Esta tesis concluye con la propuesta conceptual de una Blacksmith Streaming Architecture (BSArc). El modelo de computación Blacksmith permite la adopción a nivel de hardware de un front-end de aplicación específico utilizando una GPU como back-end. Esto permite maximizar la explotación de la localidad de datos y el paralelismo a nivel de datos de una aplicación mientras que proporciona un flujo mayor de datos al back-end. Consideramos que el diseño de estos procesadores con memorias especializadas debe ser proporcionado por expertos del dominio de aplicación en la forma de plantillas.The basic concept behind the architecture of a general purpose CPU core conforms well to a serial programming model. The integration of more cores on a single chip helped CPUs in running parts of a program in parallel. However, the utilization of huge parallelism available from many high performance applications and the corresponding data is hard to achieve from these general purpose multi-cores. Streaming accelerators and the corresponding programing models improve upon this situation by providing throughput oriented architectures. The basic idea behind the design of these architectures matches the everyday increasing requirements of processing huge data sets. These high-performance throughput oriented devices help in high performance processing of data by using efficient parallel computations and streaming based communications. The throughput oriented streaming accelerators ¿ similar to the other processors ¿ consist of numerous types of micro-architectural components including the memory structures, compute units, control units, I/O channels and I/O controls etc. However, the throughput requirements add some special features and impose other restrictions for the performance purposes. These devices, normally, offer a large number of compute resources but restrict the applications to arrange parallel and maximally independent data sets to feed the compute resources in the form of streams. The arrangement of data into independent sets of parallel streams is not an easy and simple task. It may need to change the structure of an algorithm as a whole or even it can require to write a new algorithm from scratch for the target application. However, all these efforts for the re-arrangement of application data access patterns may still not be very helpful to achieve the optimal performance. This is because of the possible micro-architectural constraints of the target platform for the hardware pre-fetching mechanisms, the size and the granularity of the local storage and the flexibility in data marshaling inside the local storage. The constraints of a general purpose streaming platform on the data pre-fetching, storing and maneuvering to arrange and maintain it in the form of parallel and independent streams could be removed by employing micro-architectural level design approaches. This includes the usage of application specific customized memories in the front-end of a streaming architecture. The focus of this thesis is to present architectural explorations for the streaming accelerators using customized memory layouts. In general the thesis covers three main aspects of such streaming accelerators in this research. These aspects can be categorized as : i) Design of Application Specific Accelerators with Customized Memory Layout ii) Template Based Design Support for Customized Memory Accelerators and iii) Design Space Explorations for Throughput Oriented Devices with Standard and Customized Memories. This thesis concludes with a conceptual proposal on a Blacksmith Streaming Architecture (BSArc). The Blacksmith Computing allow the hardware-level adoption of an application specific front-end with a GPU like streaming back-end. This gives an opportunity to exploit maximum possible data locality and the data level parallelism from an application while providing a throughput natured powerful back-end. We consider that the design of these specialized memory layouts for the front-end of the device are provided by the application domain experts in the form of templates. These templates are adjustable according to a device and the problem size at the device's configuration time. The physical availability of such an architecture may still take time. However, simulation framework helps in architectural explorations to give insight into the proposal and predicts potential performance benefits for such an architecture.Postprint (published version
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