146 research outputs found
Evaluation and optimization of Big Data Processing on High Performance Computing Systems
Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 524V01[Resumo]
Hoxe en día, moitas organizacións empregan tecnoloxías Big Data para extraer
información de grandes volumes de datos. A medida que o tamaño destes volumes
crece, satisfacer as demandas de rendemento das aplicacións de procesamento
de datos masivos faise máis difícil. Esta Tese céntrase en avaliar e optimizar estas
aplicacións, presentando dúas novas ferramentas chamadas BDEv e Flame-MR. Por
unha banda, BDEv analiza o comportamento de frameworks de procesamento Big
Data como Hadoop, Spark e Flink, moi populares na actualidade. BDEv xestiona
a súa configuración e despregamento, xerando os conxuntos de datos de entrada
e executando cargas de traballo previamente elixidas polo usuario. Durante cada
execución, BDEv extrae diversas métricas de avaliación que inclúen rendemento,
uso de recursos, eficiencia enerxética e comportamento a nivel de microarquitectura.
Doutra banda, Flame-MR permite optimizar o rendemento de aplicacións Hadoop
MapReduce. En xeral, o seu deseño baséase nunha arquitectura dirixida por eventos
capaz de mellorar a eficiencia dos recursos do sistema mediante o solapamento da
computación coas comunicacións. Ademais de reducir o número de copias en memoria
que presenta Hadoop, emprega algoritmos eficientes para ordenar e mesturar os
datos. Flame-MR substitúe o motor de procesamento de datos MapReduce de xeito
totalmente transparente, polo que non é necesario modificar o código de aplicacións
xa existentes. A mellora de rendemento de Flame-MR foi avaliada de maneira exhaustiva
en sistemas clúster e cloud, executando tanto benchmarks estándar coma
aplicacións pertencentes a casos de uso reais. Os resultados amosan unha redución
de entre un 40% e un 90% do tempo de execución das aplicacións. Esta Tese proporciona
aos usuarios e desenvolvedores de Big Data dúas potentes ferramentas
para analizar e comprender o comportamento de frameworks de procesamento de
datos e reducir o tempo de execución das aplicacións sen necesidade de contar con
coñecemento experto para elo.[Resumen]
Hoy en día, muchas organizaciones utilizan tecnologías Big Data para extraer
información de grandes volúmenes de datos. A medida que el tamaño de estos volúmenes
crece, satisfacer las demandas de rendimiento de las aplicaciones de procesamiento
de datos masivos se vuelve más difícil. Esta Tesis se centra en evaluar y
optimizar estas aplicaciones, presentando dos nuevas herramientas llamadas BDEv
y Flame-MR. Por un lado, BDEv analiza el comportamiento de frameworks de procesamiento
Big Data como Hadoop, Spark y Flink, muy populares en la actualidad.
BDEv gestiona su configuración y despliegue, generando los conjuntos de datos de
entrada y ejecutando cargas de trabajo previamente elegidas por el usuario. Durante
cada ejecución, BDEv extrae diversas métricas de evaluación que incluyen rendimiento,
uso de recursos, eficiencia energética y comportamiento a nivel de microarquitectura.
Por otro lado, Flame-MR permite optimizar el rendimiento de aplicaciones
Hadoop MapReduce. En general, su diseño se basa en una arquitectura dirigida por
eventos capaz de mejorar la eficiencia de los recursos del sistema mediante el solapamiento
de la computación con las comunicaciones. Además de reducir el número
de copias en memoria que presenta Hadoop, utiliza algoritmos eficientes para ordenar
y mezclar los datos. Flame-MR reemplaza el motor de procesamiento de datos
MapReduce de manera totalmente transparente, por lo que no se necesita modificar
el código de aplicaciones ya existentes. La mejora de rendimiento de Flame-MR ha
sido evaluada de manera exhaustiva en sistemas clúster y cloud, ejecutando tanto
benchmarks estándar como aplicaciones pertenecientes a casos de uso reales. Los
resultados muestran una reducción de entre un 40% y un 90% del tiempo de ejecución
de las aplicaciones. Esta Tesis proporciona a los usuarios y desarrolladores de
Big Data dos potentes herramientas para analizar y comprender el comportamiento
de frameworks de procesamiento de datos y reducir el tiempo de ejecución de las
aplicaciones sin necesidad de contar con conocimiento experto para ello.[Abstract]
Nowadays, Big Data technologies are used by many organizations to extract
valuable information from large-scale datasets. As the size of these datasets increases,
meeting the huge performance requirements of data processing applications
becomes more challenging. This Thesis focuses on evaluating and optimizing these
applications by proposing two new tools, namely BDEv and Flame-MR. On the one
hand, BDEv allows to thoroughly assess the behavior of widespread Big Data processing
frameworks such as Hadoop, Spark and Flink. It manages the configuration
and deployment of the frameworks, generating the input datasets and launching the
workloads specified by the user. During each workload, it automatically extracts
several evaluation metrics that include performance, resource utilization, energy efficiency
and microarchitectural behavior. On the other hand, Flame-MR optimizes
the performance of existing Hadoop MapReduce applications. Its overall design is
based on an event-driven architecture that improves the efficiency of the system
resources by pipelining data movements and computation. Moreover, it avoids redundant
memory copies present in Hadoop, while also using efficient sort and merge
algorithms for data processing. Flame-MR replaces the underlying MapReduce data
processing engine in a transparent way and thus the source code of existing applications
does not require to be modified. The performance benefits provided by Flame-
MR have been thoroughly evaluated on cluster and cloud systems by using both
standard benchmarks and real-world applications, showing reductions in execution
time that range from 40% to 90%. This Thesis provides Big Data users with powerful
tools to analyze and understand the behavior of data processing frameworks and
reduce the execution time of the applications without requiring expert knowledge
Adding Storage Simulation Capacities to the SimGrid Toolkit: Concepts, Models, and API
International audienceFor each kind of distributed computing infrastructures, i.e., clusters, grids, clouds, data centers, or supercomputers, storage is a essential component to cope with the tremendous increase in scientific data production and the ever-growing need for data analysis and preservation. Understanding the performance of a storage subsystem or dimensioning it properly is an important concern for which simulation can help by allowing for fast, fully repeatable, and configurable experiments for arbitrary hypothetical scenarios. However, most simulation frameworks tailored for the study of distributed systems offer no or little abstractions or models of storage resources.In this paper, we detail the extension of SimGrid, a versatile toolkit for the simulation of large-scale distributed computing systems, with storage simulation capacities. We first define the required abstractions and propose a new API to handle storage components and their contents in SimGrid-based simulators. Then we characterize the performance of the fundamental storage component that are disks and derive models of these resources. Finally we list several concrete use cases of storage simulations in clusters, grids, clouds, and data centers for which the proposed extension would be beneficial
Scale-Out Processors
Global-scale online services, such as Google’s Web search and Facebook’s social networking, run in large-scale datacenters. Due to their massive scale, these services are designed to scale out (or distribute) their respective loads and datasets across thousands of servers in datacenters. The growing demand for online services forced service providers to build networks of datacenters, which require an enormous capital outlay for infrastructure, hardware, and power consumption. Consequently, efficiency has become a major concern in the design and operation of such datacenters, with processor efficiency being of, utmost importance, due to the significant contribution of processors to the overall datacenter performance and cost. Scale-out workloads, which are behind today’s online services, serve independent requests, and have large instruction footprints and little data locality. As such, they benefit from processor designs that feature many cores and a modestly sized Last-Level Cache (LLC), a fast access path to the LLC, and high-bandwidth interfaces to memory. Existing server-class processors with large LLCs and a handful of aggressive out-of-order cores are inefficient in executing scale-out workloads. Moreover, the scaling trajectory for these processors leads to even lower efficiency in future technology nodes. This thesis presents a family of throughput-optimal processors, called Scale-Out Processors, for the efficient execution of scale-out workloads. A unique feature of Scale-Out Processors is that they consist of multiple stand-alone modules, called pods, wherein each module is a server running an operating system and a full software stack. To design a throughput-optimal processor, we developed a methodology based on performance density, defined as throughput per unit area, to quantify how effectively an architecture uses the silicon real estate. The proposed methodology derives a performance-density optimal processor building block (i.e., pod), which tightly couples a number of cores to a small LLC via a fast interconnect. Scale-Out Processors simply consist of multiple pods with no inter-pod connectivity or coherence. Moreover, they deliver the highest throughput in today’s technology and afford near-ideal scalability as process technology advances. We demonstrate that Scale-Out Processors improve datacenters’ efficiency by 4.4x-7.1x over datacenters designed using existing server-class processors
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