Bartosz Bosak

Distributed multiscale computing with MUSCLE 2, the Multiscale Coupling Library and Environment

We present the Multiscale Coupling Library and Environment: MUSCLE 2. This multiscale component-based execution environment has a simple to use Java, C++, C, Python and Fortran API, compatible with MPI, OpenMP and threading codes. We demonstrate its local and distributed computing capabilities and compare its performance to MUSCLE 1, file copy, MPI, MPWide, and GridFTP. The local throughput of MPI is about two times higher, so very tightly coupled code should use MPI as a single submodel of MUSCLE 2; the distributed performance of GridFTP is lower, especially for small messages. We test the performance of a canal system model with MUSCLE 2, where it introduces an overhead as small as 5% compared to MPI.

A distributed multiscale computation of a tightly coupled model using the Multiscale Modeling Language

Abstract Nature is observed at all scales; with multiscale modeling, scientists bring together several scales for a holistic analysis of a phenomenon. The models on these different scales may require significant but also heterogeneous computational resources, creating the need for distributed multiscale computing. A particularly demanding type of multiscale models, tightly coupled, brings with it a number of theoretical and practical issues. In this contribution, a tightly coupled model of in-stent restenosis is first theoretically examined for its multiscale merits using the Multiscale Modeling Language (MML); this is aided by a toolchain consisting of MAPPER Memory (MaMe), the Multiscale Application Designer (MAD), and Gridspace Experiment Workbench. It is implemented and executed with the general Multiscale Coupling Library and Environment (MUSCLE). Finally, it is scheduled amongst heterogeneous infrastructures using the QCG-Broker. This marks the first occasion that a tightly coupled application uses distributed multiscale computing in such a general way.

New capabilities in qoscosgrid middleware for advanced job management, advance reservation and co-allocation of computing resources --- quantum chemistry application use case

In this chapter we present the new capabilities of QosCosGrid (QCG) middleware for advanced job and resource management in the grid environment. By connecting many computing clusters together, QosCosGrid offers easy-to-use mapping, execution and monitoring capabilities for a variety of complex computations, such as parameter sweep, workflows, MPI or hybrid MPI-OpenMP as well as multiscale simulations. Thanks to QosCosGrid, large-scale programming models written in Fortran, C, C++ or Java can be automatically distributed over a network of computing resources with guaranteed Quality of Service --- for example guaranteed startup time of a job. Consequently, applications can be run at specified periods with reduced execution time and waiting times. This enables more complex problem instances to be addressed. In order to prove the usefulness of the new functionality of QosCosGrid a detailed description of the system along with a real use case scenario from the quantum chemistry science domain will be presented in this chapter.

Multiscale Computing with the Multiscale Modeling Library and Runtime Environment

We introduce a software tool to simulate multiscale models: the Multiscale Coupling Library and Environment 2 (MUSCLE 2). MUSCLE 2 is a component-based modeling tool inspired by the multiscale modeling and simulation framework, with an easy-to-use API which supports Java, C++, C, and Fortran. We present MUSCLE 2s runtime features, such as its distributed computing capabilities, and its benefits to multiscale modelers. We also describe two multiscale models that use MUSCLE 2 to do distributed multiscale computing: an in-stent restenosis and a canal system model. We conclude that MUSCLE 2 is a notable improvement over the previous version of MUSCLE, and that it allows users to more flexibly deploy simulations of multiscale models, while improving their performance.

New QosCosGrid Middleware Capabilities andźItsźIntegration with European e-Infrastructure

QosCosGrid QCG is an integrated system offering leading job and resource management capabilities in order to deliver supercomputer-like performance and structure to end users. By combining many distributed computing resources together, QCG offers highly efficient mapping, execution and monitoring capabilities for a variety of applications, such as parameter sweep, workflows, multi-scale, MPI or hybrid MPI-OpenMP. The QosCosGrid middleware also provides ai¾źset of unique features, such as advance reservation, co-allocation of distributed computing resources, support for interactive tasks and monitoring of ai¾źprogress of running applications. The middleware is offered to end users by well-designed and easy-to-use client tools. At the time of writing, QosCosGrid is the most popular middleware within the PL-Grid Infrastructure. After its successful adoption within the Polish research communities, it has been integrated with the EGI infrastructure and through a release in UMD and EGI-AppDB it is also available at European level. In this article, we focus on the extensions that were introduced to QosCosGrid during the period of the PL-Grid and PLGrid Plus projects in order to support advanced user scenarios and to integrate the stack with the Polish and European e-Infrastructures.

MultiGrain/MAPPER: A distributed multiscale computing approach to modeling and simulating gene regulation networks

Abstract Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key task in this area is the automated inference or reverse-engineering of dynamic mechanistic GRN models from gene expression time-course data. Besides a lack of suitable data (in particular multi-condition data from the same system), one of the key challenges of this task is the computational complexity involved. The more genes in the GRN system and the more parameters a GRN model has, the higher the computational load. The computational challenge is likely to increase substantially in the near future when we tackle larger GRN systems. The goal of this study was to develop a distributed computing framework and system for reverse-engineering of GRN models. We present the resulting software called MultiGrain/MAPPER. This software is based on a new architecture and tools supporting multiscale computing in a distributed computing environment. A key feature of MultiGrain/MAPPER is the realization of GRN reverse-engineering based on the underlying distributed computing framework and multi-swarm particle swarm optimization. We demonstrate some of the features of MultiGrain/MAPPER and evaluate its performance using both real and artificial gene expression data.

Reservations for Compute Resources in Federated e-Infrastructure

This paper presents work done to prepare compute resource reservations in the PL-Grid Infrastructure. A compute resource reservation allows a user to allocate some fraction of resources for exclusive access, when reservation is prepared. That way the user is able to run his/her job without waiting for allocating resources in a batch system. In the PL-Grid Infrastructure reservations can be allocated up to amount negotiated in a PL-Grid grant. One way of getting reservation is allocation by a resource administrator. Another way is to use predefined pool of resources accessible by various middleware. In both approaches once obtained, reservations identifiers can be used by middleware during job submissions. Enabling reservations requires changes in middleware. The modifications needed in each middleware will be described. The possible extension of existing reservation model in the PL-Grid Infrastructure can be envisaged: reservation usage normalization and reservation accounting. The reservations are created and utilized in the users context, so there must be a way to pass the reservation details from the user-level tools to a batch system. Each of PL-Grid supported middleware, namely gLite, UNICORE and QosCosGrid, required adaptations to implement this goal.

Multiscale Modelling and Simulation, 14th International Workshop

Abstract Multiscale Modelling and Simulation (MMS) is a cornerstone in today’s research in computational science. Simulations containing multiple models, with each model operating at a different temporal or spatial scale, are a challenging setting that frequently require innovative approaches in areas such as scale bridging, code deployment, error quantification, and scientific analysis. The aim of the MMS workshop is to encourage and consolidate the progress in this multi-disciplinary research field, both in the areas of the scientific applications and the underlying infrastructures that enable these applications. Here we briefly introduce the scope of the workshop and highlight some of the key aspects of this year’s submissions.

Highly efficient parallel approach to the next-generation DNA sequencing

Due to the rapid development of the technology, next-generation sequencers can produce huge amount of short DNA fragments covering a genomic sequence of an organism in short time. There is a need for the time-efficient algorithms which could assembly these fragments together and reconstruct the examined DNA sequence. Previously proposed algorithm for de novo assembly, SR-ASM, produced results of high quality, but required a lot of time for computations. The proposed hybrid parallel programming strategy allows one to use the two-level hierarchy: computations in threads (on a single node with many cores) and computations on different nodes in a cluster. The tests carried out on real data of Prochloroccocus marinus coming from Roche sequencer showed, that the algorithm was speeded up 20 times in comparison to the sequential approach with the maintenance of the high accuracy and beating results of other algorithms.