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

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

An RMS Architecture for Efficiently Supporting Complex-Moldable Applications

International audienceHigh-performance scientific applications are becoming increasingly complex, in particular because of the coupling of parallel codes. This results in applications having a complex structure, characterized by multiple deploy-time parameters, such as the number of processes of each code. In order to optimize the performance of these applications, the parameters have to be carefully chosen, a process which is highly resource dependent. However, the abstractions provided by current Resource Management Systems (RMS) - either submitting rigid jobs or enumerating a list of moldable configurations - are insufficient to efficiently select resources for such applications. This paper introduces CooRM, an RMS architecture that delegates resource selection to applications while still keeping control over the resources. The proposed architecture is evaluated using a simulator which is then validated with a proof-of-concept implementation on Grid'5000. Results show that such a system is feasible and performs well with respect to scalability and fairness

AiiDA: Automated Interactive Infrastructure and Database for Computational Science

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.Comment: 30 pages, 7 figure

Contribution à la convergence d'infrastructure entre le calcul haute performance et le traitement de données à large échelle

The amount of produced data, either in the scientific community or the commercialworld, is constantly growing. The field of Big Data has emerged to handle largeamounts of data on distributed computing infrastructures. High-Performance Computing (HPC) infrastructures are traditionally used for the execution of computeintensive workloads. However, the HPC community is also facing an increasingneed to process large amounts of data derived from high definition sensors andlarge physics apparati. The convergence of the two fields -HPC and Big Data- iscurrently taking place. In fact, the HPC community already uses Big Data tools,which are not always integrated correctly, especially at the level of the file systemand the Resource and Job Management System (RJMS).In order to understand how we can leverage HPC clusters for Big Data usage, andwhat are the challenges for the HPC infrastructures, we have studied multipleaspects of the convergence: We initially provide a survey on the software provisioning methods, with a focus on data-intensive applications. We contribute a newRJMS collaboration technique called BeBiDa which is based on 50 lines of codewhereas similar solutions use at least 1000 times more. We evaluate this mechanism on real conditions and in simulated environment with our simulator Batsim.Furthermore, we provide extensions to Batsim to support I/O, and showcase thedevelopments of a generic file system model along with a Big Data applicationmodel. This allows us to complement BeBiDa real conditions experiments withsimulations while enabling us to study file system dimensioning and trade-offs.All the experiments and analysis of this work have been done with reproducibilityin mind. Based on this experience, we propose to integrate the developmentworkflow and data analysis in the reproducibility mindset, and give feedback onour experiences with a list of best practices.RésuméLa quantité de données produites, que ce soit dans la communauté scientifiqueou commerciale, est en croissance constante. Le domaine du Big Data a émergéface au traitement de grandes quantités de données sur les infrastructures informatiques distribuées. Les infrastructures de calcul haute performance (HPC) sont traditionnellement utilisées pour l’exécution de charges de travail intensives en calcul. Cependant, la communauté HPC fait également face à un nombre croissant debesoin de traitement de grandes quantités de données dérivées de capteurs hautedéfinition et de grands appareils physique. La convergence des deux domaines-HPC et Big Data- est en cours. En fait, la communauté HPC utilise déjà des outilsBig Data, qui ne sont pas toujours correctement intégrés, en particulier au niveaudu système de fichiers ainsi que du système de gestion des ressources (RJMS).Afin de comprendre comment nous pouvons tirer parti des clusters HPC pourl’utilisation du Big Data, et quels sont les défis pour les infrastructures HPC, nousavons étudié plusieurs aspects de la convergence: nous avons d’abord proposé uneétude sur les méthodes de provisionnement logiciel, en mettant l’accent sur lesapplications utilisant beaucoup de données. Nous contribuons a l’état de l’art avecune nouvelle technique de collaboration entre RJMS appelée BeBiDa basée sur 50lignes de code alors que des solutions similaires en utilisent au moins 1000 fois plus.Nous évaluons ce mécanisme en conditions réelles et en environnement simuléavec notre simulateur Batsim. En outre, nous fournissons des extensions à Batsimpour prendre en charge les entrées/sorties et présentons le développements d’unmodèle de système de fichiers générique accompagné d’un modèle d’applicationBig Data. Cela nous permet de compléter les expériences en conditions réellesde BeBiDa en simulation tout en étudiant le dimensionnement et les différentscompromis autours des systèmes de fichiers.Toutes les expériences et analyses de ce travail ont été effectuées avec la reproductibilité à l’esprit. Sur la base de cette expérience, nous proposons d’intégrerle flux de travail du développement et de l’analyse des données dans l’esprit dela reproductibilité, et de donner un retour sur nos expériences avec une liste debonnes pratiques

ASA -- The Adaptive Scheduling Algorithm

In High Performance Computing (HPC) infrastructures, the control of resources by batch systems can lead to prolonged queue waiting times and adverse effects on the overall execution times of applications, particularly in data-intensive and low-latency workflows where efficient processing hinges on resource planning and timely allocation. Allocating the maximum capacity upfront ensures the fastest execution but results in spare and idle resources, extended queue waits, and costly usage. Conversely, dynamic allocation based on workflow stage requirements optimizes resource usage but may negatively impact the total workflow makespan. To address these issues, we introduce ASA, the Adaptive Scheduling Algorithm. ASA is a novel, convergence-proven scheduling technique that minimizes jobs inter-stage waiting times by estimating the queue waiting times to proactively submit resource change requests ahead of time. It strikes a balance between exploration and exploitation, considering both learning (waiting times) and applying learnt insights. Real-world experiments over two supercomputers centers with scientific workflows demonstrate ASA's effectiveness, achieving near-optimal resource utilization and accuracy, with up to 10% and 2% reductions in average workflow queue waiting times and makespan, respectively