Massively Parallel Algorithm for Multiple Sequence Alignment Based on Artificial Bee Colony

In silico biological sequence processing is a key task in molecular biology. This scientific area requires powerful computing resources for exploring large sets of biological data. Parallel in silico simulations based on methods and algorithms for analysis of biological data using high-performance distributed computing is essential for accelerating the research and reducing the investment. Multiple sequence alignment is a widely used method for biological sequence processing. The goal of this method is DNA and protein sequences alignment. This paper presents an innovative parallel algorithm MSA_BG for multiple alignment of biological sequences that is highly scalable and locality aware. The MSA_BG algorithm we describe is iterative and is based on the concept of Artificial Bee Colony metaheuristics and the concept of algorithmic and architectural spaces correlation. The metaphor of the ABC metaheuristics has been constructed and the functionalities of the agents have been defined. The conceptual parallel model of computation has been designed and the algorithmic framework of the designed parallel algorithm constructed. Experimental simulations on the basis of parallel implementation of MSA_BG algorithm for multiple sequences alignment on heterogeneouc compact computer cluster and supercomputer BlueGene/P have been carried out for the case study of the influenza virus variability investigation. The performance estimation and profiling analyses have shown that the parallel system is well balanced both in respect to the workload and machine size

A scalable tool architecture for diagnosing wait states in massively parallel applications

When scaling message-passing applications to thousands of processors, their performance is often affected by wait states that occur when processes fail to reach synchronization points simultaneously. As a first step in reducing the performance impact, we have shown in our earlier work that wait states can be diagnosed by searching event traces for characteristic patterns. However, our initial sequential search method did not scale beyond several hundred processes. Here, we present a scalable approach, based on a parallel replay of the target application's communication behavior, that can efficiently identify wait states at the previously inaccessible scale of 65,536 processes and that has potential for even larger configurations. We explain how our new approach has been integrated into a comprehensive parallel tool architecture, which we use to demonstrate that wait states may consume a major fraction of the execution time at larger scales. (C) 2009 Elsevier B.V. All rights reserved

Méthodes efficaces de parallélisation de l'analyse de traces noyau

RÉSUMÉ Les architectures hautement parallèles sont de plus en plus répandues, non seulement dans les systèmes haute-performance, mais aussi dans les ordinateurs grand public. La détection et la résolution de problèmes lors de l’exécution parallèle de logiciels sur ces types de systèmes sont des tâches complexes auxquelles les outils classiques de débogage ne sont pas adaptés. Des études précédentes ont démontré que le traçage s’avère être une solution efficace à la résolution de problèmes dans des systèmes hautement parallèles. Cependant, l’augmentation du nombre d’unités parallèles dans les systèmes tracés cause aussi une augmentation de la quantité de données générées par le traçage. Les architectures distribuées ne font qu’exacerber ce problème puisque chaque nœud peut contenir plusieurs processeurs multicœurs. Les données de trace doivent être analysées par un outil d’analyse de traces afin de pouvoir extraire les métriques importantes qui permettront de résoudre les problèmes. Or, les outils d’analyse de traces disponibles sont conçus de manière à s’exécuter séquentiellement, sans tirer avantage des capacités d’exécution parallèle. Nous nous retrouvons donc face à une différence de plus en plus grande entre la quantité de données produite par le traçage et la vitesse à laquelle ces données peuvent être analysées. La présente recherche a pour but d’explorer l’utilisation du calcul parallèle afin d’accélérer l’analyse de traces. Nous proposons une méthode efficace de parallélisation de l’analyse de traces qui supporte la mise à l’échelle. Nous nous concentrons sur les traces en format CTF générées par le traceur LTTng sur Linux. La solution présentée prend en compte des facteurs clés de la parallélisation efficace, notamment un bon équilibrage de la charge, un minimum de synchronisation et une résolution efficace des dépendances de données. Notre solution se base sur des aspects clés du format de trace CTF afin de créer des charges de travail équilibrées et facilement parallélisables. Nous proposons aussi un algorithme permettant la détection et la résolution de dépendances de données, pendant l’analyse de traces, qui utilise au minimum le verrouillage et la synchronisation entre les fils d’exécution. Nous implémentons trois analyses de traces parallèles à l’aide de cette solution : la première permet de compter les événements d’une trace, la seconde de mesurer le temps CPU utilisé par processus et la troisième de mesurer la quantité de données lues et écrites par processus. Nous utilisons ces analyses afin de mesurer la mise à l’échelle possible de notre solution, en utilisant le concept d’efficacité parallèle. Puisque les traces peuvent être potentiellement très volumineuses, elles ne peuvent être gardées en mémoire et sont donc lues à partir du disque. Afin d’évaluer l’impact de la performance des périphériques de stockages sur notre implémentation parallèle, nous utilisons un programme simulant des charges de travail sur le CPU et sur le disque, typiques de l’analyse de traces. Nous évaluons ensuite la performance de ce programme sur plusieurs types de périphériques de stockage, tels que des disques durs et des disques SSD, afin de démontrer que la performance de l’analyse parallèle de traces n’est pas gravement limitée par les accès au disque, surtout avec des périphériques de stockage modernes. Nous utilisons aussi ce programme afin d’évaluer l’effet d’améliorations futures au décodage de la trace, sur la mise à l’échelle de l’analyse parallèle. Notre solution offre une efficacité parallèle au-dessus de 56% jusqu’à 32 cœurs, lors de l’exécution de l’analyse de traces parallèle, ce qui signifie une accélération de 18 fois par rapport au temps de traitement séquentiel. De plus, les résultats de performance obtenus à partir du programme de simulation confirment que l’efficacité parallèle n’est pas sérieusement affectée par les accès au disque lorsque des périphériques de type SSD sont utilisés. Cette observation tient d’ailleurs même lorsque le décodage de la trace est plus rapide. Certains facteurs qui nuisent à la mise à l’échelle sont dus au modèle séquentiel de la bibliothèque de lecture de traces et peuvent être réglés avec une refonte de celle-ci, tandis que d’autres proviennent de goulots d’étranglement au sein du module de gestion de la mémoire du noyau et pourraient être améliorés ou contournés.----------ABSTRACT Highly parallel computer architectures are now increasingly commonplace, whether in com- mercial or consumer-grade systems. Detecting and solving runtime problems, in software running in a parallel environment, is a complicated task, where classic debugging tools are of little help. Previous research has shown that tracing offers an efficient and scalable way to resolve these problems. However, as the number of parallel units in the traced system increases, so does the amount of data generated in the trace. This problem also compounds when tracing distributed systems, where each individual node may have many-core processors. Trace data has to be analyzed by a trace analysis tool, in order to extract significant metrics which can be used to resolve problems. However, the current trace analysis tools are designed for serial analysis on a single thread. We therefore have an ever widening gap between the amount of data produced in the trace and the speed at which we can analyse this data. This research explores the use of parallel processing in order to accelerate trace analysis. The aim is to develop an efficient and scalable parallel method for analyzing traces. We focus on traces in the CTF format, generated by the LTTng tracer on Linux. We present a solution which takes into account key factors of parallelization, such as good load balancing, low synchronization overhead and an efficient resolution of data dependencies. Our solution uses key aspects of the CTF trace format to create balanced, parallelizable workloads. We also propose an algorithm to detect and resolve data dependencies during trace analysis, with minimal locking and synchronization. Using this solution, we implement three trace analyses (counting events; measuring CPU time per-process; measuring amount of data read and written per-process) which we use in order to assess the scalability in terms of parallel efficiency. Traces, being potentially very large, are not kept entirely in memory and must be read from disk. In order to assess the effect of the speed of storage devices on the parallel implementation of trace analysis, we create a program that simulates the CPU and I/O workloads typical of trace analysis. We then benchmark this program on various storage devices (e.g. HDD, SSD, etc.) in order to show that parallel trace analysis is not seriously hindered by I/O-boundedness problems, especially with modern storage hardware. We also use this program in order to assess the effect of future improvements in trace decoding on the analysis. Our solution shows parallel efficiency above 56% up to 32 cores, when running the parallel trace analyses, which translates to a speedup of 18 times the serial speed. Furthermore, benchmarks on the simulation program confirm that these efficiencies are not seriously affected by disk I/O on solid state devices, even in the case of faster trace decoding. Some factors affecting scalability are found within the serial design of the tracing library and can be fixed by a re-design, while others come from bottlenecks within the memory management unit of the kernel which could be improved or worked around

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

The efficient parallel execution of scientific applications is a key challenge in high-performance computing (HPC). With growing parallelism and heterogeneity of compute resources as well as increasingly complex software, performance analysis has become an indispensable tool in the development and optimization of parallel programs. This thesis presents a framework for systematic performance analysis of scalable, heterogeneous applications. Based on event traces, it automatically detects the critical path and inefficiencies that result in waiting or idle time, e.g. due to load imbalances between parallel execution streams. As a prerequisite for the analysis of heterogeneous programs, this thesis specifies inefficiency patterns for computation offloading. Furthermore, an essential contribution was made to the development of tool interfaces for OpenACC and OpenMP, which enable a portable data acquisition and a subsequent analysis for programs with offload directives. At present, these interfaces are already part of the latest OpenACC and OpenMP API specification. The aforementioned work, existing preliminary work, and established analysis methods are combined into a generic analysis process, which can be applied across programming models. Based on the detection of wait or idle states, which can propagate over several levels of parallelism, the analysis identifies wasted computing resources and their root cause as well as the critical-path share for each program region. Thus, it determines the influence of program regions on the load balancing between execution streams and the program runtime. The analysis results include a summary of the detected inefficiency patterns and a program trace, enhanced with information about wait states, their cause, and the critical path. In addition, a ranking, based on the amount of waiting time a program region caused on the critical path, highlights program regions that are relevant for program optimization. The scalability of the proposed performance analysis and its implementation is demonstrated using High-Performance Linpack (HPL), while the analysis results are validated with synthetic programs. A scientific application that uses MPI, OpenMP, and CUDA simultaneously is investigated in order to show the applicability of the analysis

Structural Performance Comparison of Parallel Software Applications

With rising complexity of high performance computing systems and their parallel software, performance analysis and optimization has become essential in the development of efficient applications. The comparison of performance data is a key operation required in performance analysis. An analyst may conduct different types of comparisons in order to understand the performance properties of an application. One use case is comparing performance data from multiple measurements. Typical examples for such comparisons are before/after comparisons when applying optimizations or changing code versions. Besides comparing performance between multiple runs, also comparing performance characteristics across the parallel execution streams of an application is essential to detect performance problems. This is typically useful to detect imbalances, outliers, or changing runtime behavior during the execution of an application. While such comparisons are straightforward for the aggregated data in performance profiles, only limited solutions exist for comparing event traces. Trace-based analysis, i.e., the collection of fine-grained information on individual application events with timestamps and application context, has proven to be a powerful technique. The detailed performance information included in event traces make them very suitable for performance analysis. However, this level of detail also presents a challenge because it implies a large and overwhelming amount of data. Currently, users need to perform manual comparison of event traces, which is extremely challenging and time consuming because of the large volume of detailed data and the need to correctly line up trace events. To fill the gap of missing solutions for automatic comparison of event traces, this work proposes a set of techniques that automatically align traces. The alignment allows their structural comparison and the highlighting of differences between them. A set of novel metrics provide the user with an objective measure of the differences between traces, both in terms of differences in the event stream and timing differences across events. An additional important aspect of trace-based analysis is the visualization of performance data in event timelines. This has proven to be a powerful approach for the detection of various types of performance problems. However, visualization of large numbers of event timelines quickly hits the limits of available display resolution. Likewise, identifying performance problems is challenging in the large amount of visualized performance data. To alleviate these problems this work proposes two new approaches for event timeline visualization. First, novel folding strategies for event timelines facilitate visual scalability and provide powerful overviews of performance data at the same time. Second, this work presents an effective approach that automatically identifies and highlights several types of performance critical sections in an application run. This approach identifies time dominant functions of an application and subsequently uses them to analyze runtime imbalances throughout the application run. Intuitive visualizations present the resulting runtime variations and guide the analyst to performance hot spots. Evaluations with benchmarks and real-world applications assess all introduced techniques. The effectiveness of the comparison approaches is demonstrated by showing automatically detected performance issues and structural differences between different versions of applications and across parallel execution streams. Case studies showcase the capabilities of the event timeline visualization techniques by demonstrating scalable performance data visualizations and detecting performance problems and code inefficiencies in real-world applications