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Efficient execution of large-scale and extremely demanding computational scenarios is a challenge for both the infrastructure providers and end-users, usually scientists, that need to develop highly scalable computational codes. Nevertheless, at this time, on the eve of exa-scale supercomputers, the particular role has to be given also to the intermediate software that can help in the preparation of applications so they can be efficiently executed on the emerging HPC systems. The efficiency and scalability of such software can be seen as priorities, however, these are not the only elements that should be addressed. Equally important is to offer software that is elastic, portable between platforms of different sizes, and easy to use. Trying to fulfill all the above needs we present QCG-PilotJob, a tool designed to enable flexible execution of numerous potentially dynamic and interdependent computing tasks in a single allocation on a computing cluster. QCG-PilotJob is built on many years of collaboration with computational scientists representing various domains and it responses to the practical requirements of real scientific use-cases. In this paper, we focus on the recent integration of QCG-PilotJob with the EasyVVUQ library and its successful use for Uncertainty Quantification workflows of several complex multiscale applications being developed within the VECMA project. However, we believe that with a well-thought-out design that allows for fully user-space execution and straightforward installation, QCG-PilotJob may be easily exploited in many other application scenarios, even by inexperienced users.

In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International); and COSMO–EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU–GPU arrangements.

We describe our Multiscale Computing Patterns software for High Performance Multiscale Computing. Following a short review of Multiscale Computing Patterns, this paper introduces the Multiscale Computing Patterns Software, which consists of description, optimisation and execution components. First, the description component translates the task graph, representing a multiscale simulation, to a particular type of multiscale computing pattern. Second, the optimisation component selects and applies algorithms to find the most suitable mapping between submodels and available HPC resources. Third, the execution component which a middleware layer maps submodels to the number and type of physical resources based on the suggestions emanating from the optimisation part together with infrastructure-specific metrics such as queueing time and resource availability. The main purpose of the Multiscale Computing Patterns software is to leverage the Multiscale Computing Patterns to simplify and automate the execution of complex multiscale simulations on high performance computers, and to provide both application-specific and pattern-specific performance optimisation. We test the performance and the resource usage for three multiscale models, which are expressed in terms of two Multiscale Computing Patterns. In doing so, we demonstrate how the software automates resource selection and load balancing, and delivers performance benefits from both the end-user and the HPC system level perspectives.

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.

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.

This chapter focuses on two enhanced and widely used parallel computing environments: QosCosGrid-ProActive (QCG-ProActive) and QosCosGrid-OpenMPI (QCG-OMPI). QCG-OMPI is designed to enable parallel applications to run across geographically widely distributed computational resources owned and secured by different administrative organizations. QCG-ProActive was successfully integrated with the Repast Suite, a very popular Java-based, agent-based modeling and simulation platform. The chapter presents a short overview of validation scenarios. It discusses the main requirements of complex systems and other demanding parallel applications and classifies these into ?> templates. ?> The chapter introduces the QCG middleware services and their key capabilities relevant to end users. A number of useful libraries and frameworks for application developers and resource providers using QCG are described. The chapter provides additional Web-based monitoring and troubleshooting tools for QCG, which are already available on some production-level computational clusters and supercomputers. large-scale systems; middleware; modelling

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.

The growing demand for computational power causes that Grids are becoming mission-critical components in research and industry, offering sophisticated solutions in leveraging large-scale computing and storage resources. The nature a Grid in which resources are usually shared among multiple organizations offering resources under their control based on the "best effort" approach with no guarantee concerning the quality-of-service may be inadequate to support large-scale simulations. Requirements of such simulations often exceed capabilities of a single computing center causing the need to simultaneously allocate and synchronize resources belonging to many administrative domains whose functionality is missing in leading grid middlewares preventing researchers from executing large-scale simulations in grids. The paper presents tools and services that were designed to build multilayered infrastructure capable of dealing with computationally intensive large-scale simulations in the grid environment. The developed and deployed middleware enables computing clusters in different administrative domains to be virtually welded into a single powerful compute resource that can be treated as a quasi-opportunistic supercomputer. We describe the middleware developed in the QosCosGrid project and being enhanced under the PL-Grid national grid initiative, which provides advance reservation and resource co-allocation functionality as well as support for parallel large-scale applications based on OpenMPI (for C/C++ and Fortran) or ProActive for Java.

We describe a method for queue wait time prediction in supercomputing clusters. It was designed for use as a part of multi-criteria brokering mechanisms for resource selection in a multi-site High Performance Computing environment. The aim is to incorporate the time jobs stay queued in the scheduling system into the selection criteria. Our method can also be used by the end users to estimate the time to completion of their computing jobs. It uses historical data about the particular system to make predictions. It returns a list of probability estimates of the form ( t i , p i ), where p i is the probability that the job will start before time t i . Times t i can be chosen more or less freely when deploying the system. Compared to regression methods that only return a single number as a queue wait time estimate (usually without error bars) our prediction system provides more useful information. The probability estimates are calculated using the Bayes theorem with the naive assumption that the attributes describing the jobs are independent. They are further calibrated to make sure they are as accurate as possible, given available data. We describe our service and its REST API and the underlying methods in detail and provide empirical evidence in support of the method's efficacy. This article is part of the theme issue ‘Multiscale modelling, simulation and computing: from the desktop to the exascale’.