Xavier Pennec

Flexible and Efficient Workflow Deployment of Data-Intensive Applications On Grids With MOTEUR

Workflows offer a powerful way to describe and deploy applications on grid infrastructures. Many workflow management systems have been proposed but there is still a lack of a system that would allow both a simple description of the dataflow of the application and an efficient execution on a grid platform. In this paper, we study the requirements of such a system, underlining the need for well-defined data composition strategies on the one hand and for a fully parallel execution on the other. As combining those features is not straightforward, we then propose algorithms to do so and we describe the design and implementation of MOTEUR, a workflow engine that fulfills those requirements. Performance results and overhead quantification are shown to evaluate MOTEUR with respect to existing comparable workflow systems on a production grid.

Grid-enabled workflows for data intensive medical applications

Data intensive medical image processing applications can easily benefit from grid capabilities. However, the setting up of complex medical experiments is not straight forward on current grid infrastructures. To ease such experiments we are developing a generic and grid-enabled workflow framework, relying on current standards. We show results on a concrete application to medical image registration assessment. We discuss the limitations induced by current standards and tools and how they were overcome for deploying the application.

Grid-enabling medical image analysis

Grids have emerged as a promising technology to handle the data and compute intensive requirements of many application areas. Digital medical image processing is a promising application area for grids. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. The research project AGIR (Grid Analysis of Radiological Data) presented in this paper addresses this challenge through a combined approach: on one hand, leveraging the grid middleware through core grid medical services which target the requirements of medical data processing applications; on the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical applications.

Performance evaluation of grid-enabled registration algorithms using bronze-standards

Evaluating registration algorithms is difficult due to the lack of gold standard in most clinical procedures. The bronze standard is a real-data based statistical method providing an alternative registration reference through a computationally intensive image database registration procedure. We propose in this paper an efficient implementation of this method through a grid-interfaced workflow enactor enabling the concurrent processing of hundreds of image registrations in a couple of hours only. The performances of two different grid infrastructures were compared. We computed the accuracy of 4 different rigid registration algorithms on longitudinal MRI images of brain tumors. Results showed an average subvoxel accuracy of 0.4 mm and 0.15 degrees in rotation.

Efficient services composition for grid-enabled data-intensive applications

Grids technologies enable the deployment of complex data-intensive scientific applications. Nonspecific scientific codes may benefit from grid computing capabilities by (i) assembling codes in workflows (code parallelism) and (ii) processing large amounts of data (data parallelism). We designed MOTEUR, a service-based workflow manager, to describe data-intensive scientific applications in a compact framework and to efficiently process the resulting computations by transparently exploiting different parallelism levels. Theoretical performances are analyzed and results are shown based on a real application to medical image databases processing

Probabilistic and dynamic optimization of job partitioning on a grid infrastructure

Production grids have a potential for parallel execution of a very large number of tasks but also introduce a high overhead that significantly impacts the execution of short tasks. In this work, we present a strategy to optimize the partitioning of jobs on a grid infrastructure. This method takes into account the variability and the difficulty to model a multi-user large-scale environment used for production. It is based on probabilistic estimations of the grid overhead. We first study analytically modeled environments and then we show results on a real grid infrastructure. We demonstrate that this method leads to a significant time speed-up and to a substantial saving of the number of submitted tasks with respect to a blind maximal partitioning strategy.

A Probabilistic Model to Analyse Workflow Performance on Production Grids

Production grids are complex and highly variable systems whose behavior is not well understood and difficult to anticipate. The goal of this study is to estimate the impact of the variability of those infrastructures on the performance of workflow-based applications. A probabilistic model of workflows execution time is proposed and evaluated. Results show that the variability of the EGEE grid infrastructure impacts the execution time of a particular medical image analysis application by a factor 2. The model gives interesting insights on the grid behavior for different application parallelization modes.

Workflow-Based Data Parallel Applications on the EGEE Production Grid Infrastructure

Setting up and deploying complex applications on a Grid infrastructure is still challenging and the programming models are rapidly evolving. Efficiently exploiting Grid parallelism is often not straight forward. In this paper, we report on the techniques used for deploying applications on the EGEE production Grid through four experiments coming from completely different scientific areas: nuclear fusion, astrophysics and medical imaging. These applications have in common the need for manipulating huge amounts of data and all are computationally intensive. All the cases studied show that the deployment of data intensive applications require the development of more or less elaborated application-level workload management systems on top of the gLite middleware to efficiently exploit the EGEE Grid resources. In particular, the adoption of high level workflow management systems eases the integration of large scale applications while exploiting Grid parallelism transparently. Different approaches for scientific workflow management are discussed. The MOTEUR workflow manager strategy to efficiently deal with complex data flows is more particularly detailed. Without requiring specific application development, it leads to very significant speed-ups.

A framework for evaluating the impact of compression on registration algorithms without gold standard

The impact of lossy compression has often been discussed in the medical area. In this study, an evaluation of the impact of lossy compression on the performance of rigid registration algorithms for medical images is proposed. The robustness, repeatability and accuracy of these algorithms is estimated through a statistical procedure for each compression ratio. Results are obtained thanks to a grid technology handling the computation cost of the method. Experiments reveal that the impact of compression is quite negligible below a significant compression ratio if the registration algorithm has a good multi-scale handling. Beyond this threshold, feature-based methods are highly penalized.