What does fault tolerant Deep Learning need from MPI?

Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed DL implementations which use MPI require days of training (model learning) time on commonly studied datasets. Long running DL applications become susceptible to faults - requiring development of a fault tolerant system infrastructure, in addition to fault tolerant DL algorithms. This raises an important question: What is needed from MPI for de- signing fault tolerant DL implementations? In this paper, we address this problem for permanent faults. We motivate the need for a fault tolerant MPI specification by an in-depth consideration of recent innovations in DL algorithms and their properties, which drive the need for specific fault tolerance features. We present an in-depth discussion on the suitability of different parallelism types (model, data and hybrid); a need (or lack thereof) for check-pointing of any critical data structures; and most importantly, consideration for several fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI and their applicability to fault tolerant DL implementations. We leverage a distributed memory implementation of Caffe, currently available under the Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches by ex- tending MaTEx-Caffe for using ULFM-based implementation. Our evaluation using the ImageNet dataset and AlexNet, and GoogLeNet neural network topologies demonstrates the effectiveness of the proposed fault tolerant DL implementation using OpenMPI based ULFM

09191 Abstracts Collection -- Fault Tolerance in High-Performance Computing and Grids

From June 4--8, 2009, the Dagstuhl Seminar 09191 ``Fault Tolerance in High-Performance Computing and Grids \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available. Slides of the talks and abstracts are available online at url{http://www.dagstuhl.de/Materials/index.en.phtml?09191}

Building Near-Real-Time Processing Pipelines with the Spark-MPI Platform

Advances in detectors and computational technologies provide new opportunities for applied research and the fundamental sciences. Concurrently, dramatic increases in the three Vs (Volume, Velocity, and Variety) of experimental data and the scale of computational tasks produced the demand for new real-time processing systems at experimental facilities. Recently, this demand was addressed by the Spark-MPI approach connecting the Spark data-intensive platform with the MPI high-performance framework. In contrast with existing data management and analytics systems, Spark introduced a new middleware based on resilient distributed datasets (RDDs), which decoupled various data sources from high-level processing algorithms. The RDD middleware significantly advanced the scope of data-intensive applications, spreading from SQL queries to machine learning to graph processing. Spark-MPI further extended the Spark ecosystem with the MPI applications using the Process Management Interface. The paper explores this integrated platform within the context of online ptychographic and tomographic reconstruction pipelines.Comment: New York Scientific Data Summit, August 6-9, 201

A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure

Parallel detrended fluctuation analysis for fast event detection on massive PMU data

("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl's Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment

Una visión general sobre la implementación de metaheurísticas paralelas en la nube

Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel implementation applying HPC techniques is a common approach for efficiently using available resources to reduce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuristics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.Las metaheurísticas son uno de los métodos más populares en muchas áreas de la ciencia y la ingeniera para la resolución de problemas de optimización global difíciles. Su implementación paralela, aplicando técnicas de HPC, es una aproximación común a la hora de reducir el tiempo necesario para obtener una solución lo suficientemente buena con un uso eficiente de los recursos disponibles. Paradigmas como MPI u OMP son las opciones habituales cuando se ejecutan en clústeres o supercomputadores. Además, la utilización generalizada de la computación en la nube y la aparición de modelos de programación como MapReduce o Spark, han generado un interés creciente por portar aplicaciones HPC a la nube, como ocurre en el caso de las metaheursticas paralelas. En este trabajo recogemos una visión general de nuestra experiencia con diferentes opciones a la hora de portar metaheursticas paralelas a la nube, proporcionando información útil al lector interesado, que hemos ido adquiriendo a través de nuestra experiencia practica.Facultad de Informátic

Una visión general sobre la implementación de metaheurísticas paralelas en la nube

Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel implementation applying HPC techniques is a common approach for efficiently using available resources to reduce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuristics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.Las metaheurísticas son uno de los métodos más populares en muchas áreas de la ciencia y la ingeniera para la resolución de problemas de optimización global difíciles. Su implementación paralela, aplicando técnicas de HPC, es una aproximación común a la hora de reducir el tiempo necesario para obtener una solución lo suficientemente buena con un uso eficiente de los recursos disponibles. Paradigmas como MPI u OMP son las opciones habituales cuando se ejecutan en clústeres o supercomputadores. Además, la utilización generalizada de la computación en la nube y la aparición de modelos de programación como MapReduce o Spark, han generado un interés creciente por portar aplicaciones HPC a la nube, como ocurre en el caso de las metaheursticas paralelas. En este trabajo recogemos una visión general de nuestra experiencia con diferentes opciones a la hora de portar metaheursticas paralelas a la nube, proporcionando información útil al lector interesado, que hemos ido adquiriendo a través de nuestra experiencia practica.Facultad de Informátic