Vladimir Korkhov

VLAM-G: A Grid-based virtual laboratory

The Grid-based Virtual Laboratory AMsterdam (VLAM-G), provides a science portal for distributed analysis in applied scientific research. It offers scientists remote experiment control, data management facilities and access to distributed resources by providing cross-institutional integration of information and resources in a familiar environment. The main goal is to provide a unique integration of existing standards and software packages. This paper describes the design and prototype implementation of the VLAM-G platform. In this testbed we applied several recent technologies such as the Globus toolkit, enhanced federated database systems, and visualization and simulation techniques. Several domain specific case studies are described in some detail. Information management will be discussed separately in a forthcoming paper.

Dynamic workload balancing of parallel applications with user-level scheduling on the Grid

This paper suggests a hybrid resource management approach for efficient parallel distributed computing on the Grid. It operates on both application and system levels, combining user-level job scheduling with dynamic workload balancing algorithm that automatically adapts a parallel application to the heterogeneous resources, based on the actual resource parameters and estimated requirements of the application. The hybrid environment and the algorithm for automated load balancing are described, the influence of resource heterogeneity level is measured, and the speedup achieved with this technique is demonstrated for different types of applications and resources.

WS-VLAM: towards a scalable workflow system on the grid

Large scale scientific applications require extensive support from middleware and frameworks that provide the capabilities for distributed execution in the Grid environment. In particular, one of the examples of such frameworks is a Grid-enabled workflow management system. In this paper we present WS-VLAM workflow management system, describe its current design and the developments targeting to support efficient and scalable execution of large workflow applications on the Grid.

VLAM-G: a grid-based virtual laboratory

The Grid-based Virtual Laboratory AMsterdam (VLAM-G) provides a science portal for distributed analysis in applied scientific research. By facilitating access to distributed compute and information resources held by multiple organizations, and providing remote experiment control, data management and information retrieval capabilities, it allows scientists to better analyze their data. The ability to use data from multiple sources and correlating these data sets without in-depth domain expertise is a prime goal of the system. This paper describes the design and an implementation prototype of the VLAMG platform. The feasibility of the system is demonstrated by a generalized sample scenario from the chemo-physical analysis domain.

VLAM-G: Interactive data driven workflow engine for Grid-enabled resources

Grid brings the power of many computers to scientists. However, the development of Grid-enabled applications requires knowledge about Grid infrastructure and low-level API to Grid services. In turn, workflow management systems provide a high-level environment for rapid prototyping of experimental computing systems. Coupling Grid and workflow paradigms is important for the scientific community: it makes the power of the Grid easily available to the end user. The paradigm of data driven workflow execution is one of the ways to enable distributed workflow on the Grid. The work presented in this paper is carried out in the context of the Virtual Laboratory for e-Science project. We present the VLAM-G workflow management system and its core component: the Run-Time System (RTS). The RTS is a dataflow driven workflow engine which utilizes Grid resources, hiding the complexity of the Grid from a scientist. Special attention is paid to the concept of dataflow and direct data streaming between distributed workflow components. We present the architecture and components of the RTS, describe the features of VLAM-G workflow execution, and evaluate the system by performance measurements and a real life use case.

A Grid-based Virtual Reactor: Parallel performance and adaptive load balancing

We address the problem of porting parallel distributed applications from static homogeneous cluster environments to dynamic heterogeneous Grid resources. We introduce a generic technique for adaptive load balancing of parallel applications on heterogeneous resources and evaluate it using a case study application: a Virtual Reactor for simulation of plasma chemical vapour deposition. This application has a modular architecture with a number of loosely coupled components suitable for distribution over the Grid. It requires large parameter space exploration that allows using Grid resources for high-throughput computing. The Virtual Reactor contains a number of parallel solvers originally designed for homogeneous computer clusters that needed adaptation to the heterogeneity of the Grid. In this paper we study the performance of one of the parallel solvers, apply the technique developed for adaptive load balancing, evaluate the efficiency of this approach and outline an automated procedure for optimal utilization of heterogeneous Grid resources for high-performance parallel computing.

Symptom validity testing in memory clinics: Hippocampal-memory associations and relevance for diagnosing mild cognitive impairment

Patients with mild cognitive impairment (MCI) do not always convert to dementia. In such cases, abnormal neuropsychological test results may not validly reflect cognitive symptoms due to brain disease, and the usual brain–behavior relationships may be absent. This study examined symptom validity in a memory clinic sample and its effect on the associations between hippocampal volume and memory performance. Eleven of 170 consecutive patients (6.5%; 13% of patients younger than 65 years) referred to memory clinics showed noncredible performance on symptom validity tests (SVTs, viz. Word Memory Test and Test of Memory Malingering). They were compared to a demographically matched group (n = 57) selected from the remaining patients. Hippocampal volume, measured by an automated volumetric method (Freesurfer), was correlated with scores on six verbal memory tests. The median correlation was r = .49 in the matched group. However, the relation was absent (median r = −.11) in patients who failed SVTs. Memory clinic samples may include patients who show noncredible performance, which invalidates their MCI diagnosis. This underscores the importance of applying SVTs in evaluating patients with cognitive complaints that may signify a predementia stage, especially when these patients are relatively young.

The User-Level Scheduling of Divisible Load Parallel Applications With Resource Selection and Adaptive Workload Balancing on the Grid

This paper presents a hybrid resource management environment, operating on both application and system levels developed for minimizing the execution time of parallel applications with divisible workload on heterogeneous grid resources. The system is based on the adaptive workload balancing algorithm (AWLB) incorporated into the distributed analysis environment (DIANE) user-level scheduling (ULS) environment. The AWLB ensures optimal workload distribution based on the discovered application requirements and measured resource parameters. The ULS maintains the user-level resource pool, enables resource selection and controls the execution. We present the results of performance comparison of default self-scheduling used in DIANE with AWLB-based scheduling, evaluate dynamic resource pool and resource selection mechanisms, and examine dependencies of application performance on aggregate characteristics of selected resources and application profile.

Constructing Virtual Private Supercomputer Using Virtualization and Cloud Technologies

One of efficient ways to conduct experiments on HPC platforms is to create custom virtual computing environments tailored to the requirements of users and their applications. In this paper we investigate virtual private supercomputer, an approach based on virtualization, data consolidation, and cloud technologies. Virtualization is used to abstract applications from underlying hardware and operating system while data consolidation is applied to store data in a distributed storage system. Both virtualization and data consolidation layers offer APIs for distributed computations and data processing. Combined, these APIs shift the focus from supercomputing technologies to problems being solved. Based on these concepts, we propose an approach to construct virtual clusters with help of cloud computing technologies to be used as on-demand private supercomputers and evaluate performance of this solution.

WS-VLAM: A GT4 Based Workflow Management System

Generic Grid middleware, e.g., Globus Toolkit 4 (GT4), provides basic services for scientific workflow management systems to discover, store and integrate workflow components. Using the state of the art Grid services can advance the functionality of workflow engine in orchestrating distributed Grid resources. In this paper, we present our work on migrating VLAM-G, a Grid workflow engine based on GT2 to GT4. We discuss how we use the rich set of services provided by GT4 in the new design to realize the user interactivity, interoperability and monitoring. The experiment results show that use cases from previous systems can be migrated seamlessly into the new architecture.