Effective Reproducible Research with Org-Mode and Git

International audienceIn this article we address the question of developing a lightweight and effective workflow for conducting experimental research on modern parallel computer systems in a reproducible way. Our workflowsimply builds on two well-known tools (Org-mode and Git) and enablesto address issues such as provenance tracking, experimental setup reconstruction, replicable analysis. Although this workflow is perfectible and cannot be seen as a final solution, we have been usingit for two years now and we have recently published a fully reproduciblearticle, which demonstrates the effectiveness of our proposal

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

How To Make A Pie: Reproducible Research for Empirical Economics & Econometrics

Empirical economics and econometrics (EEE) research now relies primarily on the application of code to datasets. Handling the workflow linking datasets, programs, results and finally manuscript(s) is essential if one wish to reproduce results, which is now increasingly required by journals and institutions. We underline here the importance of “reproducible research” in EEE and suggest three simple principles to follow. We illustrate these principles with good habits and tools, with particular focus on their implementation in most popular software and languages in applied economics

On benchmarking of deep learning systems: software engineering issues and reproducibility challenges

Since AlexNet won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012, Deep Learning (and Machine Learning/AI in general) gained an exponential interest. Nowadays, their adoption spreads over numerous sectors, like automotive, robotics, healthcare and finance. The ML advancement goes in pair with the quality improvement delivered by those solutions. However, those ameliorations are not for free: ML algorithms always require an increasing computational power, which pushes computer engineers to develop new devices capable of coping with this demand for performance. To foster the evolution of DSAs, and thus ML research, it is key to make it easy to experiment and compare them. This may be challenging since, even if the software built around these devices simplifies their usage, obtaining the best performance is not always straightforward. The situation gets even worse when the experiments are not conducted in a reproducible way. Even though the importance of reproducibility for the research is evident, it does not directly translate into reproducible experiments. In fact, as already shown by previous studies regarding other research fields, also ML is facing a reproducibility crisis. Our work addresses the topic of reproducibility of ML applications. Reproducibility in this context has two aspects: results reproducibility and performance reproducibility. While the reproducibility of the results is mandatory, performance reproducibility cannot be neglected because high-performance device usage causes cost. To understand how the ML situation is regarding reproducibility of performance, we reproduce results published for the MLPerf suite, which seems to be the most used machine learning benchmark. Because of the wide range of devices and frameworks used in different benchmark submissions, we focus on a subset of accuracy and performance results submitted to the MLPerf Inference benchmark, presenting a detailed analysis of the difficulties a scientist may find when trying to reproduce such a benchmark and a possible solution using our workflow tool for experiment reproducibility: PROVA!. We designed PROVA! to support the reproducibility in traditional HPC experiments, but we will show how we extended it to be used as a 'driver' for MLPerf benchmark applications. The PROVA! driver mode allows us to experiment with different versions of the MLPerf Inference benchmark switching among different hardware and software combinations and compare them in a reproducible way. In the last part, we will present the results of our reproducibility study, demonstrating the importance of having a support tool to reproduce and extend original experiments getting deeper knowledge about performance behaviours

Batsim: a Realistic Language-Independent Resources and Jobs Management Systems Simulator

International audienceAs large scale computation systems are growing to exascale, Resources and Jobs Management Systems (RJMS) need to evolve to manage this scale modification. However, their study is problematic since they are critical production systems, where experimenting is extremely costly due to downtime and energy costs. Meanwhile, many scheduling algorithms emerging from theoretical studies have not been transferred to production tools for lack of realistic experimental validation. To tackle these problems we propose Batsim, an extendable, language-independent and scalable RJMS simulator. It allows researchers and engineers to test and compare any scheduling algorithm, using a simple event-based communication interface, which allows different levels of realism. In this paper we show that Batsim's behaviour matches the one of the real RJMS OAR. Our evaluation process was made with reproducibility in mind and all the experiment material is freely available

How To Make A Pie: Reproducible Research for Empirical Economics & Econometrics

Empirical economics and econometrics (EEE) research now relies primarily on the application of code to datasets. Handling the workflow linking datasets, programs, results and finally manuscript(s) is essential if one wish to reproduce results, which is now increasingly required by journals and institutions. We underline here the importance of “reproducible research” in EEE and suggest three simple principles to follow. We illustrate these principles with good habits and tools, with particular focus on their implementation in most popular software and languages in applied economics

An Introduction to Programming for Bioscientists: A Python-based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables, numerous exercises, and 19 pages of Supporting Information; currently in press at PLOS Computational Biolog