Toward High-Performance Computing and Big Data Analytics Convergence: The Case of Spark-DIY

Convergence between high-performance computing (HPC) and big data analytics (BDA) is currently an established research area that has spawned new opportunities for unifying the platform layer and data abstractions in these ecosystems. This work presents an architectural model that enables the interoperability of established BDA and HPC execution models, reflecting the key design features that interest both the HPC and BDA communities, and including an abstract data collection and operational model that generates a unified interface for hybrid applications. This architecture can be implemented in different ways depending on the process- and data-centric platforms of choice and the mechanisms put in place to effectively meet the requirements of the architecture. The Spark-DIY platform is introduced in the paper as a prototype implementation of the architecture proposed. It preserves the interfaces and execution environment of the popular BDA platform Apache Spark, making it compatible with any Spark-based application and tool, while providing efficient communication and kernel execution via DIY, a powerful communication pattern library built on top of MPI. Later, Spark-DIY is analyzed in terms of performance by building a representative use case from the hydrogeology domain, EnKF-HGS. This application is a clear example of how current HPC simulations are evolving toward hybrid HPC-BDA applications, integrating HPC simulations within a BDA environment.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness under Grant TIN2016-79637-P(toward Unification of HPC and Big Data Paradigms), in part by the Spanish Ministry of Education under Grant FPU15/00422 TrainingProgram for Academic and Teaching Staff Grant, in part by the Advanced Scientific Computing Research, Office of Science, U.S.Department of Energy, under Contract DE-AC02-06CH11357, and in part by the DOE with under Agreement DE-DC000122495,Program Manager Laura Biven

Standart-konformes Snapshotting für SystemC Virtuelle Plattformen

The steady increase in complexity of high-end embedded systems goes along with an increasingly complex design process. We are currently still in a transition phase from Hardware-Description Language (HDL) based design towards virtual-platform-based design of embedded systems. As design complexity rises faster than developer productivity a gap forms. Restoring productivity while at the same time managing increased design complexity can also be achieved through focussing on the development of new tools and design methodologies. In most application areas, high-level modelling languages such as SystemC are used in early design phases. In modern software development Continuous Integration (CI) is used to automatically test if a submitted piece of code breaks functionality. Application of the CI concept to embedded system design and testing requires fast build and test execution times from the virtual platform framework. For this use case the ability to save a specific state of a virtual platform becomes necessary. The saving and restoring of specific states of a simulation requires the ability to serialize all data structures within the simulation models. Improving the frameworks and establishing better methods will only help to narrow the design gap, if these changes are introduced with the needs of the engineers and developers in mind. Ultimately, it is their productivity that shall be improved. The ability to save the state of a virtual platform enables developers to run longer test campaigns that can even contain randomized test stimuli. If the saved states are modifiable the developers can inject faulty states into the simulation models. This work contributes an extension to the SoCRocket virtual platform framework to enable snapshotting. The snapshotting extension can be considered a reference implementation as the utilization of current SystemC/TLM standards makes it compatible to other frameworkds. Furthermore, integrating the UVM SystemC library into the framework enables test driven development and fast validation of SystemC/TLM models using snapshots. These extensions narrow the design gap by supporting designers, testers and developers to work more efficiently.Die stetige Steigerung der Komplexität eingebetteter Systeme geht einher mit einer ebenso steigenden Komplexität des Entwurfsprozesses. Wir befinden uns momentan in der Übergangsphase vom Entwurf von eingebetteten Systemen basierend auf Hardware-Beschreibungssprachen hin zum Entwurf ebendieser basierend auf virtuellen Plattformen. Da die Entwurfskomplexität rasanter steigt als die Produktivität der Entwickler, entsteht eine Kluft. Die Produktivität wiederherzustellen und gleichzeitig die gesteigerte Entwurfskomplexität zu bewältigen, kann auch erreicht werden, indem der Fokus auf die Entwicklung neuer Werkzeuge und Entwurfsmethoden gelegt wird. In den meisten Anwendungsgebieten werden Modellierungssprachen auf hoher Ebene, wie zum Beispiel SystemC, in den frühen Entwurfsphasen benutzt. In der modernen Software-Entwicklung wird Continuous Integration (CI) benutzt um automatisiert zu überprüfen, ob eine eingespielte Änderung am Quelltext bestehende Funktionalitäten beeinträchtigt. Die Anwendung des CI-Konzepts auf den Entwurf und das Testen von eingebetteten Systemen fordert schnelle Bau- und Test-Ausführungszeiten von dem genutzten Framework für virtuelle Plattformen. Für diesen Anwendungsfall wird auch die Fähigkeit, einen bestimmten Zustand der virtuellen Plattform zu speichern, erforderlich. Das Speichern und Wiederherstellen der Zustände einer Simulation erfordert die Serialisierung aller Datenstrukturen, die sich in den Simulationsmodellen befinden. Das Verbessern von Frameworks und Etablieren besserer Methodiken hilft nur die Entwurfs-Kluft zu verringern, wenn diese Änderungen mit Berücksichtigung der Bedürfnisse der Entwickler und Ingenieure eingeführt werden. Letztendlich ist es ihre Produktivität, die gesteigert werden soll. Die Fähigkeit den Zustand einer virtuellen Plattform zu speichern, ermöglicht es den Entwicklern, längere Testkampagnen laufen zu lassen, die auch zufällig erzeugte Teststimuli beinhalten können oder, falls die gespeicherten Zustände modifizierbar sind, fehlerbehaftete Zustände in die Simulationsmodelle zu injizieren. Mein mit dieser Arbeit geleisteter Beitrag beinhaltet die Erweiterung des SoCRocket Frameworks um Checkpointing Funktionalität im Sinne einer Referenzimplementierung. Weiterhin ermöglicht die Integration der UVM SystemC Bibliothek in das Framework die Umsetzung der testgetriebenen Entwicklung und schnelle Validierung von SystemC/TLM Modellen mit Hilfe von Snapshots

Flexible multi-layer virtual machine design for virtual laboratory in distributed systems and grids.

We propose a flexible Multi-layer Virtual Machine (MVM) design intended to improve efficiencies in distributed and grid computing and to overcome the known current problems that exist within traditional virtual machine architectures and those used in distributed and grid systems. This thesis presents a novel approach to building a virtual laboratory to support e-science by adapting MVMs within the distributed systems and grids, thereby providing enhanced flexibility and reconfigurability by raising the level of abstraction. The MVM consists of three layers. They are OS-level VM, queue VMs, and components VMs. The group of MVMs provides the virtualized resources, virtualized networks, and reconfigurable components layer for virtual laboratories. We demonstrate how our reconfigurable virtual machine can allow software designers and developers to reuse parallel communication patterns. In our framework, the virtual machines can be created on-demand and their applications can be distributed at the source-code level, compiled and instantiated in runtime. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .K56. Source: Masters Abstracts International, Volume: 44-03, page: 1405. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Computing at massive scale: Scalability and dependability challenges

Large-scale Cloud systems and big data analytics frameworks are now widely used for practical services and applications. However, with the increase of data volume, together with the heterogeneity of workloads and resources, and the dynamic nature of massive user requests, the uncertainties and complexity of resource management and service provisioning increase dramatically, often resulting in poor resource utilization, vulnerable system dependability, and user-perceived performance degradations. In this paper we report our latest understanding of the current and future challenges in this particular area, and discuss both existing and potential solutions to the problems, especially those concerned with system efficiency, scalability and dependability. We first introduce a data-driven analysis methodology for characterizing the resource and workload patterns and tracing performance bottlenecks in a massive-scale distributed computing environment. We then examine and analyze several fundamental challenges and the solutions we are developing to tackle them, including for example incremental but decentralized resource scheduling, incremental messaging communication, rapid system failover, and request handling parallelism. We integrate these solutions with our data analysis methodology in order to establish an engineering approach that facilitates the optimization, tuning and verification of massive-scale distributed systems. We aim to develop and offer innovative methods and mechanisms for future computing platforms that will provide strong support for new big data and IoE (Internet of Everything) applications

Big Data Application and System Co-optimization in Cloud and HPC Environment

The emergence of big data requires powerful computational resources and memory subsystems that can be scaled efficiently to accommodate its demands. Cloud is a new well-established computing paradigm that can offer customized computing and memory resources to meet the scalable demands of big data applications. In addition, the flexible pay-as-you-go pricing model offers opportunities for using large scale of resources with low cost and no infrastructure maintenance burdens. High performance computing (HPC) on the other hand also has powerful infrastructure that has potential to support big data applications. In this dissertation, we explore the application and system co-optimization opportunities to support big data in both cloud and HPC environments. Specifically, we explore the unique features of both application and system to seek overlooked optimization opportunities or tackle challenges that are difficult to be addressed by only looking at the application or system individually. Based on the characteristics of the workloads and their underlying systems to derive the optimized deployment and runtime schemes, we divide the workflow into four categories: 1) memory intensive applications; 2) compute intensive applications; 3) both memory and compute intensive applications; 4) I/O intensive applications.When deploying memory intensive big data applications to the public clouds, one important yet challenging problem is selecting a specific instance type whose memory capacity is large enough to prevent out-of-memory errors while the cost is minimized without violating performance requirements. In this dissertation, we propose two techniques for efficient deployment of big data applications with dynamic and intensive memory footprint in the cloud. The first approach builds a performance-cost model that can accurately predict how, and by how much, virtual memory size would slow down the application and consequently, impact the overall monetary cost. The second approach employs a lightweight memory usage prediction methodology based on dynamic meta-models adjusted by the application's own traits. The key idea is to eliminate the periodical checkpointing and migrate the application only when the predicted memory usage exceeds the physical allocation. When applying compute intensive applications to the clouds, it is critical to make the applications scalable so that it can benefit from the massive cloud resources. In this dissertation, we first use the Kirchhoff law, which is one of the most widely used physical laws in many engineering principles, as an example workload for our study. The key challenge of applying the Kirchhoff law to real-world applications at scale lies in the high, if not prohibitive, computational cost to solve a large number of nonlinear equations. In this dissertation, we propose a high-performance deep-learning-based approach for Kirchhoff analysis, namely HDK. HDK employs two techniques to improve the performance: (i) early pruning of unqualified input candidates which simplify the equation and select a meaningful input data range; (ii) parallelization of forward labelling which execute steps of the problem in parallel. When it comes to both memory and compute intensive applications in clouds, we use blockchain system as a benchmark. Existing blockchain frameworks exhibit a technical barrier for many users to modify or test out new research ideas in blockchains. To make it worse, many advantages of blockchain systems can be demonstrated only at large scales, which are not always available to researchers. In this dissertation, we develop an accurate and efficient emulating system to replay the execution of large-scale blockchain systems on tens of thousands of nodes in the cloud. For I/O intensive applications, we observe one important yet often neglected side effect of lossy scientific data compression. Lossy compression techniques have demonstrated promising results in significantly reducing the scientific data size while guaranteeing the compression error bounds, but the compressed data size is often highly skewed and thus impact the performance of parallel I/O. Therefore, we believe it is critical to pay more attention to the unbalanced parallel I/O caused by lossy scientific data compression

The Anatomy of the Grid - Enabling Scalable Virtual Organizations

"Grid" computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and, in some cases, high-performance orientation. In this article, we define this new field. First, we review the "Grid problem," which we define as flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources-what we refer to as virtual organizations. In such settings, we encounter unique authentication, authorization, resource access, resource discovery, and other challenges. It is this class of problem that is addressed by Grid technologies. Next, we present an extensible and open Grid architecture, in which protocols, services, application programming interfaces, and software development kits are categorized according to their roles in enabling resource sharing. We describe requirements that we believe any such mechanisms must satisfy, and we discuss the central role played by the intergrid protocols that enable interoperability among different Grid systems. Finally, we discuss how Grid technologies relate to other contemporary technologies, including enterprise integration, application service provider, storage service provider, and peer-to-peer computing. We maintain that Grid concepts and technologies complement and have much to contribute to these other approaches.Comment: 24 pages, 5 figure

Recovery-oriented software architecture for grid applications (ROSA-Grids)

Grids are distributed systems that dynamically coordinate a large number of heterogeneous resources to execute large-scale projects. Examples of grid resources include high-performance computers, massive data stores, high bandwidth networking, telescopes, and synchrotrons. Failure in grids is arguably inevitable due to the massive scale and the heterogeneity of grid resources, the distribution of these resources over unreliable networks, the complexity of mechanisms that are needed to integrate such resources into a seamless utility, and the dynamic nature of the grid infrastructure that allows continuous changes to happen. To make matters worse, grid applications are generally long running, and these runs repeatedly require coordinated use of many resources at the same time. In this thesis, we propose the Recovery-Aware Components (RAC) approach. The RAC approach enables a grid application to handle failure reactively and proactively at the level of the smallest and independent execution unit of the application. The approach also combines runtime prediction with a proactive fault tolerance strategy. The RAC approach aims at improving the reliability of the grid application with the least overhead possible. Moreover, to allow a grid fault tolerance manager fine-tuned control and trading off of reliability gained and overhead paid, this thesis offers an architecture-aware modelling and simulation of reliability and overhead. The thesis demonstrates for a few of a dozen or so classes of application architecture already identified in prior research, that the typical architectural structure of the class can be captured in a few parameters. The work shows that these parameters suffice to achieve significant insight into, and control of, such tradeoffs. The contributions of our research project are as follows. We defined the RAC approach. We showed the usage of the RAC approach for improving the reliability of MapReduce and Combinational Logic grid applications. We provided Markov models that represent the execution behaviour of these applications for reliability and overhead analyses. We analysed the sensitivity of the reliability-overhead tradeoff of the RAC approach to the type of fault tolerance strategy, the parameters of a fault tolerance strategy, prediction interval and a predictor’s accuracy. The final contribution of our research is an experiment testbed that enables a grid fault tolerance expert to evaluate diverse fault tolerance support configurations, and then choose the one that will satisfy the reliability and cost requirements