Parallelization of kinetic Monte Carlo algorithm to simulate AL3Sc precipitation

The present paper reports the precipitation process of Al3Sc structures in an aluminum scandium alloy, which has been simulated with a synchronous parallel kinetic Monte Carlo (spkMC) algorithm. The spkMC implementation is based on the vacancy diffusion mechanism. To filter the raw data generated by the spkMC simulations, the density-based clustering with noise (DBSCAN) method has been employed. spkMC and DBSCAN algorithms were implemented in the C language and using MPI library. The simulations were conducted in the SeARCH cluster located at the University of Minho. The Al3Sc precipitation was successfully simulated at the atomistic scale with the spkMC. DBSCAN proved to be a valuable aid to identify the precipitates by performing a cluster analysis of the simulation results. The achieved simulations results are in good agreement with those reported in the literature under sequential kinetic Monte Carlo simulations (kMC). The parallel implementation of kMC has provided a 4x speedup over the sequential version

On Parallelization of Categorical Data Clustering

We study parallelization of categorical data clustering algorithms in an MPI platform. Clustering such data has been a daunting task even for sequential algorithms, mainly due to the challenges in finding suitable similarity/distance measures. We propose a parallel version of the k-modes algorithm, called PV3, which maintains the same clustering quality as produced by the sequential approach while achieving reasonable speed-ups. PV3 is programmed to ensure deterministic processing in a parallel environment. To produce better clustering results, we then develop an initialization method called Revised Density Method (RDM) based on the notion of density. Additionally, we develop variants of the RDM method to further enhance its performance. we then study effective ways to parallelize RDM and its variants. To further exploit parallelism opportunities, we develop an Ensemble Parallelizing Process (EPP) framework. This framework can be used with any desired initialization/clustering algorithms with different levels of parallelism. Using our different RDM initialization techniques along with the PV3 algorithm in the EPP framework, we then build an RDM realization of EPP, called RDM EPP. The result of our numerous experiments using benchmark categorical datasets indicate the quality metric of RDM EPP to be among the top three sequential k-modes based clustering algorithms. In terms of speed up, the results indicate to be 7 times faster for some datasets, though much larger datasets are required for a more comprehensive scalability study of RDM EPP

Simulating whole supercomputer applications

Architecture simulation tools are extremely useful not only to predict the performance of future system designs, but also to analyze and improve the performance of software running on well know architectures. However, since power and complexity issues stopped the progress of single-thread performance, simulation speed no longer scales with technology: systems get larger and faster, but simulators do not get any faster. Detailed simulation of full-scale applications running on large clusters with hundreds or thousands of processors is not feasible. In this paper we present a methodology that allows detailed simulation of large-scale MPI applications running on systems with thousands of processors with low resource cost. Our methodology allows detailed processor simulation, from the memory and cache hierarchy down to the functional units and the pipeline structure. This feature enables software performance analysis beyond what performance counters would allow. In addition, it enables performance prediction targeting non-existent architectures and systems, that is, systems for which no performance data can be used as a reference. For example, detailed analysis of the weather forecasting application WRF reveals that it is highly optimized for cache locality, and is strongly compute bound, with faster functional units having the greatest impact on its performance. Also, analysis of next-generation CMP clusters show that performance may start to decline beyond 8 processors per chip due to shared resource contention, regardless of the benefits of through-memory communication.Postprint (published version

Scalable Solutions for Automated Single Pulse Identification and Classification in Radio Astronomy

Data collection for scientific applications is increasing exponentially and is forecasted to soon reach peta- and exabyte scales. Applications which process and analyze scientific data must be scalable and focus on execution performance to keep pace. In the field of radio astronomy, in addition to increasingly large datasets, tasks such as the identification of transient radio signals from extrasolar sources are computationally expensive. We present a scalable approach to radio pulsar detection written in Scala that parallelizes candidate identification to take advantage of in-memory task processing using Apache Spark on a YARN distributed system. Furthermore, we introduce a novel automated multiclass supervised machine learning technique that we combine with feature selection to reduce the time required for candidate classification. Experimental testing on a Beowulf cluster with 15 data nodes shows that the parallel implementation of the identification algorithm offers a speedup of up to 5X that of a similar multithreaded implementation. Further, we show that the combination of automated multiclass classification and feature selection speeds up the execution performance of the RandomForest machine learning algorithm by an average of 54% with less than a 2% average reduction in the algorithm's ability to correctly classify pulsars. The generalizability of these results is demonstrated by using two real-world radio astronomy data sets.Comment: In Proceedings of the 47th International Conference on Parallel Processing (ICPP 2018). ACM, New York, NY, USA, Article 11, 11 page

Intelligent instrumentation techniques to improve the traces information-volume ratio

With ever more powerful machines being constantly deployed, it is crucial to manage the computational resources efficiently. This is important both from the point of view of the individual user, who expects fast results; and the supercomputing center hosting the whole infrastructure, that is interested in maximizing its overall productivity. Nevertheless, the real sustained performance achieved by the applications can be significantly lower than the theoretical peak performance of the machines. A key factor to bridge this performance gap is to understand how parallel computers behave. Performance analysis tools are essential not only to understand the behavior of parallel applications, but to identify why performance expectations might not have been met, serving as guidelines to improve the inefficiencies that caused poor performance, and driving both software and hardware optimizations. However, detailed analysis of the behavior of a parallel application requires to process a large amount of data that also grows extremely fast. Current large scale systems already comprise hundreds of thousands of cores, and upcoming exascale systems are expected to assemble more than a million processing elements. With such number of hardware components, the traditional analysis methodologies consisting in blindly collecting as much data as possible and then performing exhaustive lookups are no longer applicable, because the volume of performance data generated becomes absolutely unmanageable to store, process and analyze. The evolution of the tools suggests that more complex approaches are needed, incorporating intelligence to perform competently the challenging and important task of detailed analysis. In this thesis, we address the problem of scalability of performance analysis tools in large scale systems. In such scenarios, in-depth understanding of the interactions between all the system components is more compelling than ever for an effective use of the parallel resources. To this end, our work includes a thorough review of techniques that have been successfully applied to aid in the task of Big Data Analytics in fields like machine learning, data mining, signal processing and computer vision. We have leveraged these techniques to improve the analysis of large-scale parallel applications by automatically uncovering repetitive patterns, finding data correlations, detecting performance trends and further useful analysis information. Combinining their use, we have minimized the volume of performance data captured from an execution, while maximizing the benefit and insight gained from this data, and have proposed new and more effective methodologies for single and multi-experiment performance analysis.Con el incesante aumento de potencia y capacidad de los superordenadores, la habilidad de emplear de forma efectiva todos los recursos disponibles se ha convertido en un factor crucial. La necesidad de un uso eficiente radica tanto en la aspiración de los usuarios por obtener resultados en el menor tiempo posible, como en el interés del propio centro de cálculo que alberga la infraestructura computacional por maximizar la productividad de los recursos. Sin embargo, el rendimiento real que las aplicaciones son capaces de alcanzar suele ser significativamente menor que el rendimiento teórico de las máquinas. Y la clave para salvar esta distancia consiste en comprender el comportamiento de las máquinas paralelas. Las herramientas de análisis de rendimiento son instrumentos fundamentales no solo para entender como funcionan las aplicaciones paralelas, sino también para identificar los problemas por los que el rendimiento obtenido dista del esperado, sirviendo como guías para mejorar aquellas deficiencias software y/o hardware que son causas de degradación. No obstante, un análisis en detalle del comportamiento de una aplicación paralela requiere procesar una gran cantidad de datos que crece extremadamente rápido. Los sistemas actuales de gran escala ya comprenden cientos de miles de procesadores, y se espera que los inminentes sistemas exa-escala reunan millones de elementos de procesamiento. Con semejante número de componentes, las estrategias tradicionales de obtención indiscriminada de datos para mejorar la precisión de las herramientas de análisis caerán en desuso debido a las dificultades que entraña almacenarlos y procesarlos. En este aspecto, la evolución de las herramientas sugiere que son necesarios métodos más sofisticados, que incorporen inteligencia para desarrollar la tarea de análisis de manera más competente. Esta tesis aborda el problema de escalabilidad de las herramientas de análisis en sistemas de gran escala, donde es primordial el conocimiento detallado de las interacciones entre todos los componentes para emplear los recursos paralelos de la forma más óptima. Con este fin, esta investigación incluye una revisión exhaustiva de las técnicas que se han aplicado satisfactoriamente para extraer información de grandes volumenes de datos en otras áreas como aprendizaje automático, minería de datos y procesado de señal. Hemos adaptado estas técnicas para mejorar el análisis de aplicaciones paralelas de gran escala, detectando automáticamente patrones repetitivos, correlaciones de datos, tendencias de rendimiento, y demás información relevante. Combinando el uso de estas técnicas, se ha conseguido disminuir el volumen de datos generado durante una ejecución, a la vez que aumentar la cantidad de información útil que se puede extraer de los datos mediante la aplicación de nuevas y más efectivas metodologías de análisis para el estudio del rendimiento de experimentos individuales o en seri