High-Throughput Computing on High-Performance Platforms: A Case Study

The computing systems used by LHC experiments has historically consisted of the federation of hundreds to thousands of distributed resources, ranging from small to mid-size resource. In spite of the impressive scale of the existing distributed computing solutions, the federation of small to mid-size resources will be insufficient to meet projected future demands. This paper is a case study of how the ATLAS experiment has embraced Titan---a DOE leadership facility in conjunction with traditional distributed high- throughput computing to reach sustained production scales of approximately 52M core-hours a years. The three main contributions of this paper are: (i) a critical evaluation of design and operational considerations to support the sustained, scalable and production usage of Titan; (ii) a preliminary characterization of a next generation executor for PanDA to support new workloads and advanced execution modes; and (iii) early lessons for how current and future experimental and observational systems can be integrated with production supercomputers and other platforms in a general and extensible manner

Adaptive Parallelism for Coupled, Multithreaded Message-Passing Programs

Hybrid parallel programming models that combine message passing (MP) and shared- memory multithreading (MT) are becoming more popular, especially with applications requiring higher degrees of parallelism and scalability. Consequently, coupled parallel programs, those built via the integration of independently developed and optimized software libraries linked into a single application, increasingly comprise message-passing libraries with differing preferred degrees of threading, resulting in thread-level heterogeneity. Retroactively matching threading levels between independently developed and maintained libraries is difficult, and the challenge is exacerbated because contemporary middleware services provide only static scheduling policies over entire program executions, necessitating suboptimal, over-subscribed or under-subscribed, configurations. In coupled applications, a poorly configured component can lead to overall poor application performance, suboptimal resource utilization, and increased time-to-solution. So it is critical that each library executes in a manner consistent with its design and tuning for a particular system architecture and workload. Therefore, there is a need for techniques that address dynamic, conflicting configurations in coupled multithreaded message-passing (MT-MP) programs. Our thesis is that we can achieve significant performance improvements over static under-subscribed approaches through reconfigurable execution environments that consider compute phase parallelization strategies along with both hardware and software characteristics. In this work, we present new ways to structure, execute, and analyze coupled MT- MP programs. Our study begins with an examination of contemporary approaches used to accommodate thread-level heterogeneity in coupled MT-MP programs. Here we identify potential inefficiencies in how these programs are structured and executed in the high-performance computing domain. We then present and evaluate a novel approach for accommodating thread-level heterogeneity. Our approach enables full utilization of all available compute resources throughout an application’s execution by providing programmable facilities with modest overheads to dynamically reconfigure runtime environments for compute phases with differing threading factors and affinities. Our performance results show that for a majority of the tested scientific workloads our approach and corresponding open-source reference implementation render speedups greater than 50 % over the static under-subscribed baseline. Motivated by our examination of reconfigurable execution environments and their memory overhead, we also study the memory attribution problem: the inability to predict or evaluate during runtime where the available memory is used across the software stack comprising the application, reusable software libraries, and supporting runtime infrastructure. Specifically, dynamic adaptation requires runtime intervention, which by its nature introduces additional runtime and memory overhead. To better understand the latter, we propose and evaluate a new way to quantify component-level memory usage from unmodified binaries dynamically linked to a message-passing communication library. Our experimental results show that our approach and corresponding implementation accurately measure memory resource usage as a function of time, scale, communication workload, and software or hardware system architecture, clearly distinguishing between application and communication library usage at a per-process level

Parallel programming paradigms and frameworks in big data era

With Cloud Computing emerging as a promising new approach for ad-hoc parallel data processing, major companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. We have entered the Era of Big Data. The explosion and profusion of available data in a wide range of application domains rise up new challenges and opportunities in a plethora of disciplines-ranging from science and engineering to biology and business. One major challenge is how to take advantage of the unprecedented scale of data-typically of heterogeneous nature-in order to acquire further insights and knowledge for improving the quality of the offered services. To exploit this new resource, we need to scale up and scale out both our infrastructures and standard techniques. Our society is already data-rich, but the question remains whether or not we have the conceptual tools to handle it. In this paper we discuss and analyze opportunities and challenges for efficient parallel data processing. Big Data is the next frontier for innovation, competition, and productivity, and many solutions continue to appear, partly supported by the considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. We review various parallel and distributed programming paradigms, analyzing how they fit into the Big Data era, and present modern emerging paradigms and frameworks. To better support practitioners interesting in this domain, we end with an analysis of on-going research challenges towards the truly fourth generation data-intensive science.Peer ReviewedPostprint (author's final draft

Research and development of accounting system in grid environment

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The Grid has been recognised as the next-generation distributed computing paradigm by seamlessly integrating heterogeneous resources across administrative domains as a single virtual system. There are an increasing number of scientific and business projects that employ Grid computing technologies for large-scale resource sharing and collaborations. Early adoptions of Grid computing technologies have custom middleware implemented to bridge gaps between heterogeneous computing backbones. These custom solutions form the basis to the emerging Open Grid Service Architecture (OGSA), which aims at addressing common concerns of Grid systems by defining a set of interoperable and reusable Grid services. One of common concerns as defined in OGSA is the Grid accounting service. The main objective of the Grid accounting service is to ensure resources to be shared within a Grid environment in an accountable manner by metering and logging accurate resource usage information. This thesis discusses the origins and fundamentals of Grid computing and accounting service in the context of OGSA profile. A prototype was developed and evaluated based on OGSA accounting-related standards enabling sharing accounting data in a multi-Grid environment, the World-wide Large Hadron Collider Grid (WLCG). Based on this prototype and lessons learned, a generic middleware solution was also implemented as a toolkit that eases migration of existing accounting system to be standard compatible.Engineering and Physical Sciences Research Council (EPSRC), Stanford Universit

Design and Evaluation of Low-Latency Communication Middleware on High Performance Computing Systems

[Resumen]El interés en Java para computación paralela está motivado por sus interesantes características, tales como su soporte multithread, portabilidad, facilidad de aprendizaje,alta productividad y el aumento significativo en su rendimiento omputacional. No obstante, las aplicaciones paralelas en Java carecen generalmente de mecanismos de comunicación eficientes, los cuales utilizan a menudo protocolos basados en sockets incapaces de obtener el máximo provecho de las redes de baja latencia, obstaculizando la adopción de Java en computación de altas prestaciones (High Per- formance Computing, HPC). Esta Tesis Doctoral presenta el diseño, implementación y evaluación de soluciones de comunicación en Java que superan esta limitación. En consecuencia, se desarrollaron múltiples dispositivos de comunicación a bajo nivel para paso de mensajes en Java (Message-Passing in Java, MPJ) que aprovechan al máximo el hardware de red subyacente mediante operaciones de acceso directo a memoria remota que proporcionan comunicaciones de baja latencia. También se incluye una biblioteca de paso de mensajes en Java totalmente funcional, FastMPJ, en la cual se integraron los dispositivos de comunicación. La evaluación experimental ha mostrado que las primitivas de comunicación de FastMPJ son competitivas en comparación con bibliotecas nativas, aumentando significativamente la escalabilidad de aplicaciones MPJ. Por otro lado, esta Tesis analiza el potencial de la computación en la nube (cloud computing) para HPC, donde el modelo de distribución de infraestructura como servicio (Infrastructure as a Service, IaaS) emerge como una alternativa viable a los sistemas HPC tradicionales. La evaluación del rendimiento de recursos cloud específicos para HPC del proveedor líder, Amazon EC2, ha puesto de manifiesto el impacto significativo que la virtualización impone en la red, impidiendo mover las aplicaciones intensivas en comunicaciones a la nube. La clave reside en un soporte de virtualización apropiado, como el acceso directo al hardware de red, junto con las directrices para la optimización del rendimiento sugeridas en esta Tesis.[Resumo]O interese en Java para computación paralela está motivado polas súas interesantes características, tales como o seu apoio multithread, portabilidade, facilidade de aprendizaxe, alta produtividade e o aumento signi cativo no seu rendemento computacional. No entanto, as aplicacións paralelas en Java carecen xeralmente de mecanismos de comunicación e cientes, os cales adoitan usar protocolos baseados en sockets que son incapaces de obter o máximo proveito das redes de baixa latencia, obstaculizando a adopción de Java na computación de altas prestacións (High Performance Computing, HPC). Esta Tese de Doutoramento presenta o deseño, implementaci ón e avaliación de solucións de comunicación en Java que superan esta limitación. En consecuencia, desenvolvéronse múltiples dispositivos de comunicación a baixo nivel para paso de mensaxes en Java (Message-Passing in Java, MPJ) que aproveitan ao máaximo o hardware de rede subxacente mediante operacións de acceso directo a memoria remota que proporcionan comunicacións de baixa latencia. Tamén se inclúe unha biblioteca de paso de mensaxes en Java totalmente funcional, FastMPJ, na cal foron integrados os dispositivos de comunicación. A avaliación experimental amosou que as primitivas de comunicación de FastMPJ son competitivas en comparación con bibliotecas nativas, aumentando signi cativamente a escalabilidade de aplicacións MPJ. Por outra banda, esta Tese analiza o potencial da computación na nube (cloud computing) para HPC, onde o modelo de distribución de infraestrutura como servizo (Infrastructure as a Service, IaaS) xorde como unha alternativa viable aos sistemas HPC tradicionais. A ampla avaliación do rendemento de recursos cloud específi cos para HPC do proveedor líder, Amazon EC2, puxo de manifesto o impacto signi ficativo que a virtualización impón na rede, impedindo mover as aplicacións intensivas en comunicacións á nube. A clave atópase no soporte de virtualización apropiado, como o acceso directo ao hardware de rede, xunto coas directrices para a optimización do rendemento suxeridas nesta Tese.[Abstract]The use of Java for parallel computing is becoming more promising owing to its appealing features, particularly its multithreading support, portability, easy-tolearn properties, high programming productivity and the noticeable improvement in its computational performance. However, parallel Java applications generally su er from inefficient communication middleware, most of which use socket-based protocols that are unable to take full advantage of high-speed networks, hindering the adoption of Java in the High Performance Computing (HPC) area. This PhD Thesis presents the design, development and evaluation of scalable Java communication solutions that overcome these constraints. Hence, we have implemented several lowlevel message-passing devices that fully exploit the underlying network hardware while taking advantage of Remote Direct Memory Access (RDMA) operations to provide low-latency communications. Moreover, we have developed a productionquality Java message-passing middleware, FastMPJ, in which the devices have been integrated seamlessly, thus allowing the productive development of Message-Passing in Java (MPJ) applications. The performance evaluation has shown that FastMPJ communication primitives are competitive with native message-passing libraries, improving signi cantly the scalability of MPJ applications. Furthermore, this Thesis has analyzed the potential of cloud computing towards spreading the outreach of HPC, where Infrastructure as a Service (IaaS) o erings have emerged as a feasible alternative to traditional HPC systems. Several cloud resources from the leading IaaS provider, Amazon EC2, which speci cally target HPC workloads, have been thoroughly assessed. The experimental results have shown the signi cant impact that virtualized environments still have on network performance, which hampers porting communication-intensive codes to the cloud. The key is the availability of the proper virtualization support, such as the direct access to the network hardware, along with the guidelines for performance optimization suggested in this Thesis

Application-level Fault Tolerance and Resilience in HPC Applications

Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 524V01[Resumo] As necesidades computacionais das distintas ramas da ciencia medraron enormemente nos últimos anos, o que provocou un gran crecemento no rendemento proporcionado polos supercomputadores. Cada vez constrúense sistemas de computación de altas prestacións de maior tamaño, con máis recursos hardware de distintos tipos, o que fai que as taxas de fallo destes sistemas tamén medren. Polo tanto, o estudo de técnicas de tolerancia a fallos eficientes é indispensábel para garantires que os programas científicos poidan completar a súa execución, evitando ademais que se dispare o consumo de enerxía. O checkpoint/restart é unha das técnicas máis populares. Sen embargo, a maioría da investigación levada a cabo nas últimas décadas céntrase en estratexias stop-and-restart para aplicacións de memoria distribuída tralo acontecemento dun fallo-parada. Esta tese propón técnicas checkpoint/restart a nivel de aplicación para os modelos de programación paralela roáis populares en supercomputación. Implementáronse protocolos de checkpointing para aplicacións híbridas MPI-OpenMP e aplicacións heteroxéneas baseadas en OpenCL, en ámbolos dous casos prestando especial coidado á portabilidade e maleabilidade da solución. En canto a aplicacións de memoria distribuída, proponse unha solución de resiliencia que pode ser empregada de forma xenérica en aplicacións MPI SPMD, permitindo detectar e reaccionar a fallos-parada sen abortar a execución. Neste caso, os procesos fallidos vólvense a lanzar e o estado da aplicación recupérase cunha volta atrás global. A maiores, esta solución de resiliencia optimizouse implementando unha volta atrás local, na que só os procesos fallidos volven atrás, empregando un protocolo de almacenaxe de mensaxes para garantires a consistencia e o progreso da execución. Por último, propónse a extensión dunha librería de checkpointing para facilitares a implementación de estratexias de recuperación ad hoc ante conupcións de memoria. En moitas ocasións, estos erros poden ser xestionados a nivel de aplicación, evitando desencadear un fallo-parada e permitindo unha recuperación máis eficiente.[Resumen] El rápido aumento de las necesidades de cómputo de distintas ramas de la ciencia ha provocado un gran crecimiento en el rendimiento ofrecido por los supercomputadores. Cada vez se construyen sistemas de computación de altas prestaciones mayores, con más recursos hardware de distintos tipos, lo que hace que las tasas de fallo del sistema aumenten. Por tanto, el estudio de técnicas de tolerancia a fallos eficientes resulta indispensable para garantizar que los programas científicos puedan completar su ejecución, evitando además que se dispare el consumo de energía. La técnica checkpoint/restart es una de las más populares. Sin embargo, la mayor parte de la investigación en este campo se ha centrado en estrategias stop-and-restart para aplicaciones de memoria distribuida tras la ocurrencia de fallos-parada. Esta tesis propone técnicas checkpoint/restart a nivel de aplicación para los modelos de programación paralela más populares en supercomputación. Se han implementado protocolos de checkpointing para aplicaciones híbridas MPI-OpenMP y aplicaciones heterogéneas basadas en OpenCL, prestando en ambos casos especial atención a la portabilidad y la maleabilidad de la solución. Con respecto a aplicaciones de memoria distribuida, se propone una solución de resiliencia que puede ser usada de forma genérica en aplicaciones MPI SPMD, permitiendo detectar y reaccionar a fallosparada sin abortar la ejecución. En su lugar, se vuelven a lanzar los procesos fallidos y se recupera el estado de la aplicación con una vuelta atrás global. A mayores, esta solución de resiliencia ha sido optimizada implementando una vuelta atrás local, en la que solo los procesos fallidos vuelven atrás, empleando un protocolo de almacenaje de mensajes para garantizar la consistencia y el progreso de la ejecución. Por último, se propone una extensión de una librería de checkpointing para facilitar la implementación de estrategias de recuperación ad hoc ante corrupciones de memoria. Muchas veces, este tipo de errores puede gestionarse a nivel de aplicación, evitando desencadenar un fallo-parada y permitiendo una recuperación más eficiente.[Abstract] The rapid increase in the computational demands of science has lead to a pronounced growth in the performance offered by supercomputers. As High Performance Computing (HPC) systems grow larger, including more hardware components of different types, the system's failure rate becomes higher. Efficient fault tolerance techniques are essential not only to ensure the execution completion but also to save energy. Checkpoint/restart is one of the most popular fault tolerance techniques. However, most of the research in this field is focused on stop-and-restart strategies for distributed-memory applications in the event of fail-stop failures. Thís thesis focuses on the implementation of application-level checkpoint/restart solutions for the most popular parallel programming models used in HPC. Hence, we have implemented checkpointing solutions to cope with fail-stop failures in hybrid MPI-OpenMP applications and OpenCL-based programs. Both strategies maximize the restart portability and malleability, ie., the recovery can take place on machines with different CPU / accelerator architectures, and/ or operating systems, and can be adapted to the available resources (number of cores/accelerators). Regarding distributed-memory applications, we propose a resilience solution that can be generally applied to SPMD MPI programs. Resilient applications can detect and react to failures without aborting their execution upon fail-stop failures. Instead, failed processes are re-spawned, and the application state is recovered through a global rollback. Moreover, we have optimized this resilience proposal by implementing a local rollback protocol, in which only failed processes rollback to a previous state, while message logging enables global consistency and further progress of the computation. Finally, we have extended a checkpointing library to facilitate the implementation of ad hoc recovery strategies in the event of soft errors) caused by memory corruptions. Many times, these errors can be handled at the software-Ievel, tIms, avoiding fail-stop failures and enabling a more efficient recovery

Distributed Computing in a Pandemic: A Review of Technologies Available for Tackling COVID-19

The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks.Comment: 21 pages (15 excl. refs), 2 figures, 3 table