DISTRIBUTED REPRODUCIBLE RESEARCH USING CACHED COMPUTATIONS

The ability to make scientific findings reproducible is increasingly important in areas where substantive results are the product of complex statistical computations. Reproducibility can allow others to verify the published findings and conduct alternate analyses of the same data. A question that arises naturally is how can one conduct and distribute reproducible research? This question is relevant from the point of view of both the authors who want to make their research reproducible and readers who want to reproduce relevant findings reported in the scientific literature. We present a framework in which reproducible research can be conducted and distributed via cached computations and describe specific tools for both authors and readers. As a prototype implementation we introduce three software packages written in the R language. The cacheSweave and stashR packages together provide tools for caching computational results in a key-value style database which can be published to a public repository for readers to download. The SRPM package provides tools for generating and interacting with shared reproducibility packages (SRPs) which can facilitate the distribution of the data and code. As a case study we demonstrate the use of the toolkit on a national study of air pollution exposure and mortality

Caching and Distributing Statistical Analyses in R

We present the cacher package for R, which provides tools for caching statistical analyses and for distributing these analyses to others in an efficient manner. The cacher package takes objects created by evaluating R expressions and stores them in key-value databases. These databases of cached objects can subsequently be assembled into packages for distribution over the web. The cacher package also provides tools to help readers examine the data and code in a statistical analysis and reproduce, modify, or improve upon the results. In addition, readers can easily conduct alternate analyses of the data. We describe the design and implementation of the cacher package and provide two examples of how the package can be used for reproducible research.

A REPRODUCIBLE RESEARCH TOOLKIT FOR R

We present a collection of R packages for conducting and distributing reproducible research using R, Sweave, and LaTeX. The collection consists of the cacheSweave, stashR, and SRPM packages which allow for the caching of computations in Sweave documents and the distribution of those cached computations via remotely accessible key-value databases. We describe the caching mechanism used by the cacheSweave package and tools that we have developed for authors and readers for the purposes of creating and interacting with reproducible documents

Caching and Visualizing Statistical Analyses

We present the cacher and CodeDepends packages for R, which provide tools for (1) caching and analyzing the code for statistical analyses and (2) distributing these analyses to others in an efficient manner over the web. The cacher package takes objects created by evaluating R expressions and stores them in key-value databases. These databases of cached objects can subsequently be assembled into “cache packages” for distribution over the web. The cacher package also provides tools to help readers examine the data and code in a statistical analysis and reproduce, modify, or improve upon the results. In addition, readers can easily conduct alternate analyses of the data. The CodeDepends package provides complementary tools for analyzing and visualizing the code for a statistical analysis and this functionality has been integrated into the cacher package. In this chapter we describe the cacher and CodeDepends packages and provide examples of how they can be used for reproducible research

Caching and Distributing Statistical Analyses in R

We present the cacher package for R, which provides tools for caching statistical analyses and for distributing these analyses to others in an efficient manner. The cacher package takes objects created by evaluating R expressions and stores them in key-value databases. These databases of cached objects can subsequently be assembled into packages for distribution over the web. The cacher package also provides tools to help readers examine the data and code in a statistical analysis and reproduce, modify, or improve upon the results. In addition, readers can easily conduct alternate analyses of the data. We describe the design and implementation of the cacher package and provide two examples of how the package can be used for reproducible research

Transparent Orchestration of Task-based Parallel Applications in Containers Platforms

This paper presents a framework to easily build and execute parallel applications in container-based distributed computing platforms in a user-transparent way. The proposed framework is a combination of the COMP Superscalar (COMPSs) programming model and runtime, which provides a straightforward way to develop task-based parallel applications from sequential codes, and containers management platforms that ease the deployment of applications in computing environments (as Docker, Mesos or Singularity). This framework provides scientists and developers with an easy way to implement parallel distributed applications and deploy them in a one-click fashion. We have built a prototype which integrates COMPSs with different containers engines in different scenarios: i) a Docker cluster, ii) a Mesos cluster, and iii) Singularity in an HPC cluster. We have evaluated the overhead in the building phase, deployment and execution of two benchmark applications compared to a Cloud testbed based on KVM and OpenStack and to the usage of bare metal nodes. We have observed an important gain in comparison to cloud environments during the building and deployment phases. This enables better adaptation of resources with respect to the computational load. In contrast, we detected an extra overhead during the execution, which is mainly due to the multi-host Docker networking.This work is partly supported by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316 project, by the Generalitat de Catalunya under contracts 2014-SGR-1051 and 2014-SGR-1272, and by the European Union through the Horizon 2020 research and innovation program under grant 690116 (EUBra-BIGSEA Project). Results presented in this paper were obtained using the Chameleon testbed supported by the National Science Foundation.Peer ReviewedPostprint (author's final draft