Design and Evaluation of a Collective IO Model for Loosely Coupled Petascale Programming

Loosely coupled programming is a powerful paradigm for rapidly creating higher-level applications from scientific programs on petascale systems, typically using scripting languages. This paradigm is a form of many-task computing (MTC) which focuses on the passing of data between programs as ordinary files rather than messages. While it has the significant benefits of decoupling producer and consumer and allowing existing application programs to be executed in parallel with no recoding, its typical implementation using shared file systems places a high performance burden on the overall system and on the user who will analyze and consume the downstream data. Previous efforts have achieved great speedups with loosely coupled programs, but have done so with careful manual tuning of all shared file system access. In this work, we evaluate a prototype collective IO model for file-based MTC. The model enables efficient and easy distribution of input data files to computing nodes and gathering of output results from them. It eliminates the need for such manual tuning and makes the programming of large-scale clusters using a loosely coupled model easier. Our approach, inspired by in-memory approaches to collective operations for parallel programming, builds on fast local file systems to provide high-speed local file caches for parallel scripts, uses a broadcast approach to handle distribution of common input data, and uses efficient scatter/gather and caching techniques for input and output. We describe the design of the prototype model, its implementation on the Blue Gene/P supercomputer, and present preliminary measurements of its performance on synthetic benchmarks and on a large-scale molecular dynamics application.Comment: IEEE Many-Task Computing on Grids and Supercomputers (MTAGS08) 200

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Resilient gossip-inspired all-reduce algorithms for high-performance computing - Potential, limitations, and open questions

We investigate the usefulness of gossip-based reduction algorithms in a high-performance computing (HPC) context. We compare them to state-of-the-art deterministic parallel reduction algorithms in terms of fault tolerance and resilience against silent data corruption (SDC) as well as in terms of performance and scalability. New gossip-based reduction algorithms are proposed, which significantly improve the state-of-the-art in terms of resilience against SDC. Moreover, a new gossip-inspired reduction algorithm is proposed, which promises a much more competitive runtime performance in an HPC context than classical gossip-based algorithms, in particular for low accuracy requirements.This work has been partially funded by the Spanish Ministry of Science and Innovation [contract TIN2015-65316]; by the Government of Catalonia [contracts 2014-SGR-1051, 2014-SGR-1272]; by the RoMoL ERC Advanced Grant [grant number GA 321253] and by the Vienna Science and Technology Fund (WWTF) through project ICT15-113.Peer ReviewedPostprint (author's final draft

Remote sensing big data computing: challenges and opportunities

As we have entered an era of high resolution earth observation, the RS data are undergoing an explosive growth. The proliferation of data also give rise to the increasing complexity of RS data, like the diversity and higher dimensionality characteristic of the data. RS data are regarded as RS ‘‘Big Data’’. Fortunately, we are witness the coming technological leapfrogging. In this paper, we give a brief overview on the Big Data and data-intensive problems, including the analysis of RS Big Data, Big Data challenges, current techniques and works for processing RS Big Data

A cloudification methodology for high performance simulations

Mención Internacional en el título de doctorMany scientific areas make extensive use of computer simulations to study complex real-world processes. These computations are typically very resource-intensive and present scalability issues as experiments get larger, even in dedicated supercomputers since they are limited by their own hardware resources. Cloud computing raises as an option to move forward into the ideal unlimited scalability by providing virtually infinite resources, yet applications must be adapted to this paradigm. The major goal of this thesis is to analyze the suitability of performing simulations in clouds by performing a paradigm shift, from classic parallel approaches to data-centric models, in those applications where that is possible. The aim is to maintain the scalability achieved in traditional HPC infrastructures, while taking advantage of Cloud Computing paradigm features. The thesis also explores the characteristics that make simulators suitable or unsuitable to be deployed on HPC or Cloud infrastructures, defining a generic architecture and extracting common elements present among the majority of simulators. As result, we propose a generalist cloudification methodology based on the MapReduce paradigm to migrate high performance simulations into the cloud to provide greater scalability. We analysed its viability by applying it to a real engineering simulator and running the resulting implementation on HPC and cloud environments. Our evaluations will aim to show that the cloudified application is highly scalable and there is still a large margin to improve the theoretical model and its implementations, and also to extend it to a wider range of simulations.Muchas áreas de investigación hacen uso extensivo de simulaciones informáticas para estudiar procesos complejos del mundo real. Estas simulaciones suelen hacer uso intensivo de recursos, y presentan problemas de escalabilidad conforme los experimentos aumentan en tamaño incluso en clústeres, ya que estos están limitados por sus propios recursos hardware. Cloud Computing (computación en la nube) surge como alternativa para avanzar hacia el ideal de escalabilidad ilimitada mediante el aprovisionamiento de infinitos recursos (de forma virtual). No obstante, las aplicaciones deben ser adaptadas a este nuevo paradigma. La principal meta de esta tesis es analizar la idoneidad de realizar simulaciones en la nube mediante un cambio de paradigma, de las clásicas aproximaciones paralelas a nuevos modelos centrados en los datos, en aquellas aplicaciones donde esto sea posible. El objetivo es mantener la escalabilidad alcanzada en las tradicionales infraestructuras HPC, mientras se explotan las ventajas del paradigma de computación en la nube. La tesis explora las características que hacen a los simuladores ser o no adecuados para ser desplegados en infraestructuras clúster o en la nube, definiendo una arquitectura genérica y extrayendo elementos comunes presentes en la mayoría de los simuladores. Como resultado, proponemos una metodología genérica de cloudificación, basada en el paradigma MapReduce, para migrar simulaciones de alto rendimiento a la nube con el fin de proveer mayor escalabilidad. Analizamos su viabilidad aplicándola a un simulador real de ingeniería, y ejecutando la implementación resultante en entornos clúster y en la nube. Nuestras evaluaciones pretenden mostrar que la aplicación cloudificada es altamente escalable, y que existe un amplio margen para mejorar el modelo teórico y sus implementaciones, y para extenderlo a un rango más amplio de simulaciones.- Administrador de Infraestructuras Ferroviarias (ADIF), Estudio y realización de programas de cálculo de pórticos rígidos de catenaria (CALPOR) y de sistema de simulación de montaje de agujas aéreas de línea aérea de contacto (SIA), JM/RS 3.6/4100.0685-9/00100 – Administrador de Infraestructuras Ferroviarias (ADIF), Proyecto para la Investigación sobre la aplicación de las TIC a la innovación de las diferentes infraestructuras correspondientes a las instalaciones de electrificación y suministro de energía (SIRTE), JM/RS 3.9/1500.0009/0-00000 – Spanish Ministry of Education, TIN2010-16497, Scalable Input/Output techniques for high-performance distributed and parallel computing environments – Spanish Ministry of Economics and Competitiveness, TIN2013-41350-P, Técnicas de gestión escalable de datos para high-end computing systems – European Union, COST Action IC1305, ”Network for Sustainable Ultrascale Computing Platforms” (NESUS) – European Union, COST Action IC0805, ”Open European Network for High Performance Computing on Complex Environments” – Spanish Ministry of Economics and Competitiveness, TIN2011-15734-E, Red de Computación de Altas Prestaciones sobre Arquitecturas Paralelas Heterogéneas (CAPAP-H)Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Domenica Talia.- Presidente: José Daniel García Sánchez.- Secretario: José Manuel Moya Fernánde

Agentless robust load sharing strategy for utilising hetero-geneous resources over wide area network

Resource monitoring and performance prediction services have always been regarded as important keys to improving the performance of load sharing strategy. However, the traditional methodologies usually require specific performance information, which can only be collected by installing proprietary agents on all participating resources. This requirement of implementing a single unified monitoring service may not be feasible because of the differences in the underlying systems and organisation policies. To address this problem, we define a new load sharing strategy which bases the load decision on a simple performance estimation that can be measured easily at the coordinator node. Our proposed strategy relies on a stage-based dynamic task allocation to handle the imprecision of our performance estimation and to correct load distribution on-the-fly. The simulation results showed that the performance of our strategy is comparable or better than traditional strategies, especially when the performance information from the monitoring service is not accurate

Performance analysis and optimization of in-situ integration of simulation with data analysis: zipping applications up

This paper targets an important class of applications that requires combining HPC simulations with data analysis for online or real-time scientific discovery. We use the state-of-the-art parallel-IO and data-staging libraries to build simulation-time data analysis workflows, and conduct performance analysis with real-world applications of computational fluid dynamics (CFD) simulations and molecular dynamics (MD) simulations. Driven by in-depth performance inefficiency analysis, we design an end-to-end application-level approach to eliminating the interlocks and synchronizations existent in the present methods. Our new approach employs both task parallelism and pipeline parallelism to reduce synchronizations effectively. In addition, we design a fully asynchronous, fine-grain, and pipelining runtime system, which is named Zipper. Zipper is a multi-threaded distributed runtime system and executes in a layer below the simulation and analysis applications. To further reduce the simulation application's stall time and enhance the data transfer performance, we design a concurrent data transfer optimization that uses both HPC network and parallel file system for improved bandwidth. The scalability of the Zipper system has been verified by a performance model and various empirical large scale experiments. The experimental results on an Intel multicore cluster as well as a Knight Landing HPC system demonstrate that the Zipper based approach can outperform the fastest state-of-the-art I/O transport library by up to 220% using 13,056 processor cores