Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Towards Modern, Accessible and Dynamic HPC Using Container-based Virtual Clusters

In this thesis, a novel Virtual Container Cluster (VCC) framework is presented. Despite the growing popularity of container virtualisation in order to increase the flexi-bility of the software stack, run time environment virtualisation still poses significant portability challenges; by depending on the underlying cluster execution paradigm,a niche class of HPC only containers has emerged. This trend is detrimental to reusability, reproducibility, and encouraging new communities to HPC. Traditional virtualisation techniques have a rich history within HPC, and have been demonstrated to offer much more than software flexibility. A Virtual Machine by nature requires an OS and full stack environment akin to a physical machine, and this allows it to be instantiated regardless of the underlying machine and what services it provides. This capability is essential in order to implement job forwarding and spanning - where the burden of an entire job can be transferred or shared between hetero-geneous cluster systems - with a high level of confidence that the environments will be compatible. In turn, this brings improvements to global resource performance, reducing the job turnaround time and increasing cluster utilization. The VCC is an innovative solution that combines the full stack and container virtualisation approaches. Therefore, it offers both the flexibility of containers with the improved portability, performance and scalability of the full stack approach. In order to maintain the same accessibility and lower barrier of entry as the run time environment approach, the design incorporates an autonomous configuration and contextualisation mechanism, along with a Software Defined Networking technology, to ensure the full stack container does not place an additional burden on the user. The usefulness and performance is validated through benchmarking and two case studies: virtual clusters in the classroom and inter-institutional spanning

Building interactive distributed processing applications at a global scale

Along with the continuous engagement with technology, many latency-sensitive interactive applications have emerged, e.g., global content sharing in social networks, adaptive lights/temperatures in smart buildings, and online multi-user games. These applications typically process a massive amount of data at a global scale. In this cases, distributing storage and processing is key to handling the large scale. Distribution necessitates handling two main aspects: a) the placement of data/processing and b) the data motion across the distributed locations. However, handling the distribution while meeting latency guarantees at large scale comes with many challenges around hiding heterogeneity and diversity of devices and workload, handling dynamism in the environment, providing continuous availability despite failures, and supporting persistent large state. In this thesis, we show how latency-driven designs for placement and data-motion can be used to build production infrastructures for interactive applications at a global scale, while also being able to address myriad challenges on heterogeneity, dynamism, state, and availability. We demonstrate a latency-driven approach is general and applicable at all layers of the stack: from storage, to processing, down to networking. We designed and built four distinct systems across the spectrum. We have developed Ambry (collaboration with LinkedIn), a geo-distributed storage system for interactive data sharing across the globe. Ambry is LinkedIn's mainstream production system for all its media content running across 4 datacenters and over 500 million users. Ambry minimizes user perceived latency via smart data placement and propagation. Second, we have built two processing systems, a traditional model, Samza, and the avant-garde model, Steel. Samza (collaboration with LinkedIn) is a production stream processing framework used at 15 companies (including LinkedIn, Uber, Netflix, and TripAdvisor), powering >200 pipelines at LinkedIn alone. Samza minimizes the impact of data motion on the end-to-end latency, thus, enabling large persistent state (100s of TB) along with processing. Steel (collaboration with Microsoft) extends processing to the emerging edge. Integrated with Azure, Steel dynamically optimizes placement and data-motion across the entire edge-cloud environment. Finally, we have designed FreeFlow, a high performance networking mechanisms for containers. Using the container placement, FreeFlow opportunistically bypasses networking layers, minimizing data motion and reducing latency (up to 3 orders of magnitude)

Virtualization services: scalable methods for virtualizing multicore systems

Multi-core technology is bringing parallel processing capabilities from servers to laptops and even handheld devices. At the same time, platform support for system virtualization is making it easier to consolidate server and client resources, when and as needed by applications. This consolidation is achieved by dynamically mapping the virtual machines on which applications run to underlying physical machines and their processing cores. Low cost processor and I/O virtualization methods efficiently scaled to different numbers of processing cores and I/O devices are key enablers of such consolidation. This dissertation develops and evaluates new methods for scaling virtualization functionality to multi-core and future many-core systems. Specifically, it re-architects virtualization functionality to improve scalability and better exploit multi-core system resources. Results from this work include a self-virtualized I/O abstraction, which virtualizes I/O so as to flexibly use different platforms' processing and I/O resources. Flexibility affords improved performance and resource usage and most importantly, better scalability than that offered by current I/O virtualization solutions. Further, by describing system virtualization as a service provided to virtual machines and the underlying computing platform, this service can be enhanced to provide new and innovative functionality. For example, a virtual device may provide obfuscated data to guest operating systems to maintain data privacy; it could mask differences in device APIs or properties to deal with heterogeneous underlying resources; or it could control access to data based on the ``trust' properties of the guest VM. This thesis demonstrates that extended virtualization services are superior to existing operating system or user-level implementations of such functionality, for multiple reasons. First, this solution technique makes more efficient use of key performance-limiting resource in multi-core systems, which are memory and I/O bandwidth. Second, this solution technique better exploits the parallelism inherent in multi-core architectures and exhibits good scalability properties, in part because at the hypervisor level, there is greater control in precisely which and how resources are used to realize extended virtualization services. Improved control over resource usage makes it possible to provide value-added functionalities for both guest VMs and the platform. Specific instances of virtualization services described in this thesis are the network virtualization service that exploits heterogeneous processing cores, a storage virtualization service that provides location transparent access to block devices by extending the functionality provided by network virtualization service, a multimedia virtualization service that allows efficient media device sharing based on semantic information, and an object-based storage service with enhanced access control.Ph.D.Committee Chair: Schwan, Karsten; Committee Member: Ahamad, Mustaq; Committee Member: Fujimoto, Richard; Committee Member: Gavrilovska, Ada; Committee Member: Owen, Henry; Committee Member: Xenidis, Jim

Computer Science Research Institute 2004 annual report of activities.

This report summarizes the activities of the Computer Science Research Institute (CSRI) at Sandia National Laboratories during the period January 1, 2004 to December 31, 2004. During this period the CSRI hosted 166 visitors representing 81 universities, companies and laboratories. Of these 65 were summer students or faculty. The CSRI partially sponsored 2 workshops and also organized and was the primary host for 4 workshops. These 4 CSRI sponsored workshops had 140 participants--74 from universities, companies and laboratories, and 66 from Sandia. Finally, the CSRI sponsored 14 long-term collaborative research projects and 5 Sabbaticals

Communication patterns abstractions for programming SDN to optimize high-performance computing applications

Orientador : Luis Carlos Erpen de BonaCoorientadores : Magnos Martinello; Marcos Didonet Del FabroTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 04/09/2017Inclui referências : f. 95-113Resumo: A evolução da computação e das redes permitiu que múltiplos computadores fossem interconectados, agregando seus poderes de processamento para formar uma computação de alto desempenho (HPC). As aplicações que são executadas nesses ambientes processam enormes quantidades de informação, podendo levar várias horas ou até dias para completar suas execuções, motivando pesquisadores de varias áreas computacionais a estudar diferentes maneiras para acelerá-las. Durante o processamento, essas aplicações trocam grandes quantidades de dados entre os computadores, fazendo que a rede se torne um gargalo. A rede era considerada um recurso estático, não permitindo modificações dinâmicas para otimizar seus links ou dispositivos. Porém, as redes definidas por software (SDN) emergiram como um novo paradigma, permitindoa ser reprogramada de acordo com os requisitos dos usuários. SDN já foi usado para otimizar a rede para aplicações HPC específicas mas nenhum trabalho tira proveito dos padrões de comunicação expressos por elas. Então, o principal objetivo desta tese é pesquisar como esses padrões podem ser usados para ajustar a rede, criando novas abstrações para programá-la, visando acelerar as aplicações HPC. Para atingir esse objetivo, nós primeiramente pesquisamos todos os níveis de programabilidade do SDN. Este estudo resultou na nossa primeira contribuição, a criação de uma taxonomia para agrupar as abstrações de alto nível oferecidas pelas linguagens de programação SDN. Em seguida, nós investigamos os padrões de comunicação das aplicações HPC, observando seus comportamentos espaciais e temporais através da análise de suas matrizes de tráfego (TMs). Concluímos que as TMs podem representar as comunicações, além disso, percebemos que as aplicações tendem a transmitir as mesmas quantidades de dados entre os mesmos nós computacionais. A segunda contribuição desta tese é o desenvolvimento de um framework que permite evitar os fatores da rede que podem degradar o desempenho das aplicações, tais como, sobrecarga imposta pela topologia, o desbalanceamento na utilização dos links e problemas introduzidos pela programabilidade do SDN. O framework disponibiliza uma API e mantém uma base de dados de TMs, uma para cada padrão de comunicação, anotadas com restrições de largura de banda e latência. Essas informações são usadas para reprogramar os dispositivos da rede, alocando uniformemente as comunicações nos caminhos da rede. Essa abordagem reduziu o tempo de execução de benchmarks e aplicações reais em até 26.5%. Para evitar que o código da aplicação fosse modificado, como terceira contribuição, desenvolvemos um método para identificar automaticamente os padrões de comunicação. Esse método gera texturas visuais di_erentes para cada TM e, através de técnicas de aprendizagem de máquina (ML), identifica as aplicações que estão usando a rede. Em nossos experimentos, o método conseguiu uma taxa de acerto superior a 98%. Finalmente, nós incorporamos esse método ao framework, criando uma abstração que permite programar a rede sem a necessidade de alterar as aplicações HPC, diminuindo em média 15.8% seus tempos de execução. Palavras-chave: Redes Definidas por Software, Padrões de Comunicação, Aplicações HPC.Abstract: The evolution of computing and networking allowed multiple computers to be interconnected, aggregating their processing powers to form a high-performance computing (HPC). Applications that run in these computational environments process huge amounts of information, taking several hours or even days to complete their executions, motivating researchers from various computational fields to study different ways for accelerating them. During the processing, these applications exchange large amounts of data among the computers, causing the network to become a bottleneck. The network was considered a static resource, not allowing dynamic adjustments for optimizing its links or devices. However, Software-Defined Networking (SDN) emerged as a new paradigm, allowing the network to be reprogrammed according to users' requirements. SDN has already been used to optimize the network for specific HPC applications, but no existing work takes advantage of the communication patterns expressed by those applications. So, the main objective of this thesis is to research how these patterns can be used for tuning the network, creating new abstractions for programming it, aiming to speed up HPC applications. To achieve this goal, we first surveyed all SDN programmability levels. This study resulted in our first contribution, the creation of a taxonomy for grouping the high-level abstractions offered by SDN programming languages. Next, we investigated the communication patterns of HPC applications, observing their spatial and temporal behaviors by analyzing their traffic matrices (TMs). We conclude that TMs can represent the communications, furthermore, we realize that the applications tend to transmit the same amount of data among the same computational nodes. The second contribution of this thesis is the development of a framework for avoiding the network factors that can degrade the performance of applications, such as topology overhead, unbalanced links, and issues introduced by the SDN programmability. The framework provides an API and maintains a database of TMs, one for each communication pattern, annotated with bandwidth and latency constraints. This information is used to reprogram network devices, evenly placing the communications on the network paths. This approach reduced the execution time of benchmarks and real applications up to 26.5%. To prevent the application's source code to be modified, as a third contribution of our work, we developed a method to automatically identify the communication patterns. This method generates different visual textures for each TM and, through machine learning (ML) techniques, identifies the applications using the network. In our experiments the method succeeded with an accuracy rate over 98%. Finally, we incorporate this method into the framework, creating an abstraction that allows programming the network without changing the HPC applications, reducing on average 15.8% their execution times. Keywords: Software-Defined Networking, Communication Patterns, HPC Applications

Enhancing the information content of geophysical data for nuclear site characterisation

Our knowledge and understanding to the heterogeneous structure and processes occurring in the Earth’s subsurface is limited and uncertain. The above is true even for the upper 100m of the subsurface, yet many processes occur within it (e.g. migration of solutes, landslides, crop water uptake, etc.) are important to human activities. Geophysical methods such as electrical resistivity tomography (ERT) greatly improve our ability to observe the subsurface due to their higher sampling frequency (especially with autonomous time-lapse systems), larger spatial coverage and less invasive operation, in addition to being more cost-effective than traditional point-based sampling. However, the process of using geophysical data for inference is prone to uncertainty. There is a need to better understand the uncertainties embedded in geophysical data and how they translate themselves when they are subsequently used, for example, for hydrological or site management interpretations and decisions. This understanding is critical to maximize the extraction of information in geophysical data. To this end, in this thesis, I examine various aspects of uncertainty in ERT and develop new methods to better use geophysical data quantitatively. The core of the thesis is based on two literature reviews and three papers. In the first review, I provide a comprehensive overview of the use of geophysical data for nuclear site characterization, especially in the context of site clean-up and leak detection. In the second review, I survey the various sources of uncertainties in ERT studies and the existing work to better quantify or reduce them. I propose that the various steps in the general workflow of an ERT study can be viewed as a pipeline for information and uncertainty propagation and suggested some areas have been understudied. One of these areas is measurement errors. In paper 1, I compare various methods to estimate and model ERT measurement errors using two long-term ERT monitoring datasets. I also develop a new error model that considers the fact that each electrode is used to make multiple measurements. In paper 2, I discuss the development and implementation of a new method for geoelectrical leak detection. While existing methods rely on obtaining resistivity images through inversion of ERT data first, the approach described here estimates leak parameters directly from raw ERT data. This is achieved by constructing hydrological models from prior site information and couple it with an ERT forward model, and then update the leak (and other hydrological) parameters through data assimilation. The approach shows promising results and is applied to data from a controlled injection experiment in Yorkshire, UK. The approach complements ERT imaging and provides a new way to utilize ERT data to inform site characterisation. In addition to leak detection, ERT is also commonly used for monitoring soil moisture in the vadose zone, and increasingly so in a quantitative manner. Though both the petrophysical relationships (i.e., choices of appropriate model and parameterization) and the derived moisture content are known to be subject to uncertainty, they are commonly treated as exact and error‐free. In paper 3, I examine the impact of uncertain petrophysical relationships on the moisture content estimates derived from electrical geophysics. Data from a collection of core samples show that the variability in such relationships can be large, and they in turn can lead to high uncertainty in moisture content estimates, and they appear to be the dominating source of uncertainty in many cases. In the closing chapters, I discuss and synthesize the findings in the thesis within the larger context of enhancing the information content of geophysical data, and provide an outlook on further research in this topic