Large scale scientific workflows are typically run using a task-based distributed workflow system. Many contemporary workflow systems are organized in a layered or modular fashion, with one component managing the DAG or workflow graph structure, and another component acting as the scheduler or executor. These components communicate and regulate the flow of tasks from the application to the remote execution site. Scientific workflows perform significant amounts of I/O, typically using an HPC parallel filesystem. Node-Local storage technologies are often used as an effective supplement to parallel filesystems, relieving them of the large amount of bandwidth consumed by intermediate data reads and writes which would otherwise cause I/O bottlenecks. Node-Local storage techniques depend upon effective scheduling to place tasks close to their necessary data, thus benefiting from data locality. Effective locality based scheduling is a challenge however. The conventional layered architecture results in the scheduler considering tasks on an individual basis with a narrow view of the greater DAG. We present a modified architecture of a workflow system which allows the efficient construction of dependency-based task groups which are passed through the DAG manager, scheduler, and finally to the remote worker node where improved use of data locality can be achieved. These modifications were implemented using the Parsl parallel library and TaskVine execution engine. We evaluate this implementation with a benchmark application and a Montage workflow. We compare the results between a conventional data-aware scheduler, our task grouping implementation, and a workflow system using only shared storage. We find that task grouping achieves a significantly greater degree of data locality, thereby improving performance and reducing total data movement in the cluster when compared to the other two methods.

Workflow systems provide a convenient way for users to write large-scale applications by composing independent tasks into large graphs that can be executed concurrently on high-performance clusters. In many newer workflow systems, tasks are often expressed as a combination of function invocations in a high-level language. Because necessary code and data are not statically known prior to execution, they must be moved into the cluster at runtime. An obvious way of doing this is to translate function invocations into self-contained executable programs and run them as usual, but this brings a hefty performance penalty: a function invocation now needs to piggyback its context with extra code and data to a remote node, and the remote node needs to take extra time to reconstruct the invocation's context before executing it, both detrimental to lightweight short-running functions.

Model-checking is a methodology developed in computer science to automatically assess the dynamics of discrete systems, by checking if a system modelled as a state-transition graph satisfies a dynamical property written as a temporal logic formula. The dynamics of ecosystems have been drawn as state-transition graphs for more than a century, ranging from state-and-transition models to assembly graphs. Model-checking can provide insights into both empirical data and theoretical models, as long as they sum up into state-transition graphs. While model-checking proved to be a valuable tool in systems biology, it remains largely underused in ecology apart from precursory applications. This article proposes to address this situation, through an inventory of existing ecological STGs and an accessible presentation of the model-checking methodology. This overview is illustrated by the application of model-checking to assess the dynamics of a vegetation pathways model. We select management scenarios by model-checking Computation Tree Logic formulas representing management goals and built from a proposed catalogue of patterns. In discussion, we sketch bridges between existing studies in ecology and available model-checking frameworks. In addition to the automated analysis of ecological state-transition graphs, we believe that defining ecological concepts with temporal logics could help clarify and compare them.

Since the beginning of the 1990s, stable desert sites have been used for the calibration monitoring of many different sensors. Many attempts at sensor intercalibration have been also conducted using these stable desert sites. As a result, site characterization techniques and the quality of intercalibration techniques have gradually improved over the years. More recently, the Committee on Earth Observation Satellites has recommended a list of reference pseudo-invariant calibration sites for frequent image acquisition by multiple agencies. In general, intercalibration should use well-known or spectrally flat reference. The reflectance profile of desert sites, however, might not be flat or well characterized (from a fine spectral point of view). The aim of this paper is to assess the expected accuracy that can be reached when using desert sites for intercalibration. In order to have a well-mastered estimation of different errors or error sources, this study is performed with simulated data from a hyperspectral sensor. Earth Observing-1 Hyperion images are chosen to provide the simulation input data. Two different cases of intercalibration are considered, namely, Landsat 7 Enhanced Thematic Mapper Plus with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Environmental Satellite MEdium Resolution Imaging Spectrometer (MERIS) with Aqua MODIS. The simulation results have confirmed that intercalibration accuracy of 1% to 2% can be achieved between sensors, provided there are a sufficient number of available measurements. The simulated intercalibrations allow explaining results obtained during real intercalibration exercises and to establish some recommendations for the use of desert sites for intercalibration.

This paper deals with the in-the-field absolute calibration of airborne or spaceborne passive optical sensors in the reflective domain. One way to proceed is to image an area whose radiance is known and to have information about atmosphere. Usually, in order to limit the sensitivity to unwell-known parameters, areas are chosen uniform, large, flat, with stability of their reflectance and their atmospheric context. This leads for spaceborne sensors to desert areas, African deserts or Antarctica for instance. For high resolution sensors such as airborne sensors, it is necessary to find closer areas. This paper presents a calibration method relying on a dark and bright target. The use of two reflectances simplifies the assessment of the radiance entering in the camera and probably reduces the sensitivity to the environment of the target. This method is tested for the ONERA airborne camera named PELICAN.