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In situ visualization on high-performance computing systems allows us to analyze simulation results that would otherwise be impossible, given the size of the simulation data sets and offline post-processing execution time. We develop an in situ adaptor for Paraview Catalyst and Nek5000, a massively parallel Fortran and C code for computational fluid dynamics. We perform a strong scalability test up to 2048 cores on KTH's Beskow Cray XC40 supercomputer and assess in situ visualization's impact on the Nek5000 performance. In our study case, a high-fidelity simulation of turbulent flow, we observe that in situ operations significantly limit the strong scalability of the code, reducing the relative parallel efficiency to only ≈ 21 % on 2048 cores (the relative efficiency of Nek5000 without in situ operations is ≈ 99 % ). Through profiling with Arm MAP, we identified a bottleneck in the image composition step (that uses the Radix-kr algorithm) where a majority of the time is spent on MPI communication. We also identified an imbalance of in situ processing time between rank 0 and all other ranks. In our case, better scaling and load-balancing in the parallel image composition would considerably improve the performance of Nek5000 with in situ capabilities. In general, the result of this study highlights the technical challenges posed by the integration of high-performance simulation codes and data-analysis libraries and their practical use in complex cases, even when efficient algorithms already exist for a certain application scenario. [ABSTRACT FROM AUTHOR]

One of the main hurdles of partitioned global address space (PGAS) approaches is the dominance of message passing interface (MPI), which as a de facto standard appears in the code basis of many applications. To take advantage of the PGAS APIs like global address space programming interface (GASPI) without a major change in the code basis, interoperability between MPI and PGAS approaches needs to be ensured. In this article, we consider an interoperable GASPI/MPI implementation for the communication/performance crucial parts of the Ludwig and iPIC3D applications. To address the discovered performance limitations, we develop a novel strategy for significantly improved performance and interoperability between both APIs by leveraging GASPI shared windows and shared notifications. First results with a corresponding implementation in the MiniGhost proxy application and the Allreduce collective operation demonstrate the viability of this approach. [ABSTRACT FROM AUTHOR]

Postprocessing and storage of large data sets represent one of the main computational bottlenecks in computational fluid dynamics. We assume that the accuracy necessary for computation is higher than needed for postprocessing. Therefore, in the current work we assess thresholds for data reduction as required by the most common data analysis tools used in the study of fluid flow phenomena, specifically wall-bounded turbulence. These thresholds are imposed a priori by the user in L2-norm, and we assess a set of parameters to identify the minimum accuracy requirements. The method considered in the present work is the discrete Legendre transform (DLT), which we evaluate in the computation of turbulence statistics, spectral analysis and resilience for cases highly-sensitive to the initial conditions. Maximum acceptable compression ratios of the original data have been found to be around 97%, depending on the application purpose. The new method outperforms downsampling, as well as the previously explored data truncation method based on discrete Chebyshev transform (DCT). [ABSTRACT FROM AUTHOR]

Task-based programming models for shared memory—such as Cilk Plus and OpenMP 3—are well established and documented. However, with the increase in parallel, many-core, and heterogeneous systems, a number of research-driven projects have developed more diversified task-based support, employing various programming and runtime features. Unfortunately, despite the fact that dozens of different task-based systems exist today and are actively used for parallel and high-performance computing (HPC), no comprehensive overview or classification of task-based technologies for HPC exists. In this paper, we provide an initial task-focused taxonomy for HPC technologies, which covers both programming interfaces and runtime mechanisms. We demonstrate the usefulness of our taxonomy by classifying state-of-the-art task-based environments in use today. [ABSTRACT FROM AUTHOR]

Background: Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are generally carried out on computer clusters or on large workstations in a brute force manner, by docking and scoring all available ligands. Contribution: In this study we propose a strategy that is based on iteratively docking a set of ligands to form a training set, training a ligand-based model on this set, and predicting the remainder of the ligands to exclude those predicted as 'low-scoring' ligands. Then, another set of ligands are docked, the model is retrained and the process is repeated until a certain model efficiency level is reached. Thereafter, the remaining ligands are docked or excluded based on this model. We use SVM and conformal prediction to deliver valid prediction intervals for ranking the predicted ligands, and Apache Spark to parallelize both the docking and the modeling. Results: We show on 4 different targets that conformal prediction based virtual screening (CPVS) is able to reduce the number of docked molecules by 62.61% while retaining an accuracy for the top 30 hits of 94% on average and a speedup of 3.7. The implementation is available as open source via GitHub (https://github.com/laeeq80/spark-cpvs) and can be run on high-performance computers as well as on cloud resources. [ABSTRACT FROM AUTHOR]

Objective: We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings.Materials and Methods: We describe an e-Science initiative in Sweden, e-Science for Cancer Prevention and Control (eCPC), which supports biomarker discovery and offers decision support for personalized intervention strategies. The generic eCPC contribution is a workflow with 4 nodes applied iteratively, and the concept of e-Science signifies systematic use of tools from the mathematical, statistical, data, and computer sciences.Results: The eCPC workflow is illustrated through 2 case studies. For prostate cancer, an in-house personalized screening tool, the Stockholm-3 model (S3M), is presented as an alternative to prostate-specific antigen testing alone. S3M is evaluated in a trial setting and plans for rollout in the population are discussed. For breast cancer, new biomarkers based on breast density and molecular profiles are developed and the US multicenter Women Informed to Screen Depending on Measures (WISDOM) trial is referred to for evaluation. While current eCPC data management uses a traditional data warehouse model, we discuss eCPC-developed features of a coherent data integration platform.Discussion and Conclusion: E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions. [ABSTRACT FROM AUTHOR]

Background: Structure-based virtual screening is an in-silico method to screen a target receptor against a virtual molecular library. Applying docking-based screening to large molecular libraries can be computationally expensive, however it constitutes a trivially parallelizable task. Most of the available parallel implementations are based on message passing interface, relying on low failure rate hardware and fast network connection. Google's MapReduce revolutionized large-scale analysis, enabling the processing of massive datasets on commodity hardware and cloud resources, providing transparent scalability and fault tolerance at the software level. Open source implementations of MapReduce include Apache Hadoop and the more recent Apache Spark. Results: We developed a method to run existing docking-based screening software on distributed cloud resources, utilizing the MapReduce approach. We benchmarked our method, which is implemented in Apache Spark, docking a publicly available target receptor against $$\sim $$ 2.2 M compounds. The performance experiments show a good parallel efficiency (87%) when running in a public cloud environment. Conclusion: Our method enables parallel Structure-based virtual screening on public cloud resources or commodity computer clusters. The degree of scalability that we achieve allows for trying out our method on relatively small libraries first and then to scale to larger libraries. Our implementation is named Spark-VS and it is freely available as open source from GitHub (). [ABSTRACT FROM AUTHOR]

We present a hybrid GPU implementation and performance analysis of Nekbone, which represents one of the core kernels of the incompressible Navier-Stokes solver Nek5000. The implementation is based on OpenACC and CUDA Fortran for local parallelization of the compute-intensive matrix-matrix multiplication part, which significantly minimizes the modification of the existing CPU code while extending the simulation capability of the code to GPU architectures. Our discussion includes the GPU results of OpenACC interoperating with CUDA Fortran and the gather-scatter operations with GPUDirect communication. We demonstrate performance of up to 552 Tflops on 16, 384 GPUs of the OLCF Cray XK7 Titan. [ABSTRACT FROM AUTHOR]

We present a case study of porting NekBone, a skeleton version of the Nek5000 code, to a parallel GPU-accelerated system. Nek5000 is a computational fluid dynamics code based on the spectral element method used for the simulation of incompressible flow. The original NekBone Fortran source code has been used as the base and enhanced by OpenACC directives. The profiling of NekBone provided an assessment of the suitability of the code for GPU systems, and indicated possible kernel optimizations. To port NekBone to GPU systems required little effort and a small number of additional lines of code (approximately one OpenACC directive per 1000 lines of code). The naïve implementation using OpenACC leads to little performance improvement: on a single node, from 16 Gflops obtained with the version without OpenACC, we reached 20 Gflops with the naïve OpenACC implementation. An optimized NekBone version leads to a 43 Gflop performance on a single node. In addition, we ported and optimized NekBone to parallel GPU systems, reaching a parallel efficiency of 79.9% on 1024 GPUs of the Titan XK7 supercomputer at the Oak Ridge National Laboratory. [ABSTRACT FROM AUTHOR]

In the vision of Global Research Data Infrastructures (GRDIs), data storage and management plays a crucial role. A successful GRDI will require a common globally interoperable distributed data system, formed out of data centres, that incorporates emerging technologies and new scientific data activities. The main challenge is to define common certification and auditing frameworks that will allow storage providers and data communities to build a viable partnership based on trust. To achieve this, it is necessary to find a long-term commitment model that will give financial, legal, and organisational guarantees of digital information preservation. In this article we discuss the state of the art in data storage and management for GRDIs and point out future research directions that need to be tackled to implement GRDIs. [ABSTRACT FROM AUTHOR]

A general-purpose Global Research Data Infrastructure (GRDI) for all sciences and research purposes is not conceivable for the next decade as there are too many discipline-specific modalities that currently prevail for such generalisation efforts to be effective. On the other hand, a more pragmatic approach is to start from what currently exists, identify best practices and key issues, and promote effective inter-domain collaboration among different components forming an ecosystem. This will promote interoperability, data exchange, data preservation, and distributed access (among others). This ecosystem of interoperable research data infrastructures will be composed of regional, disciplinary, and multidisciplinary components, such as libraries, archives, and data centres, offering data services for both primary datasets and publications. The ecosystem will support data-intensive science and research and stimulate the interaction among all its elements, thus promoting multidisciplinary and interdisciplinary science. This special issue includes a set of independent papers from renowned experts on organisational and technological issues related to GRDIs. These documents feed into and compliment the GRDI2020 roadmap, which supports a Global Research Data Infrastructure ecosystem. [ABSTRACT FROM AUTHOR]

In the vision of Global Research Data Infrastructures (GRDIs), data storage and management plays a crucial role. A successful GRDI will require a common globally interoperable distributed data system, formed out of data centres, that incorporates emerging technologies and new scientific data activities. The main challenge is to define common certification and auditing frameworks that will allow storage providers and data communities to build a viable partnership based on trust. To achieve this, it is necessary to find a long-term commitment model that will give financial, legal, and organisational guarantees of digital information preservation. In this article we discuss the state of the art in data storage and management for GRDIs and point out future research directions that need to be tackled to implement GRDIs. [ABSTRACT FROM AUTHOR]

A general-purpose Global Research Data Infrastructure (GRDI) for all sciences and research purposes is not conceivable for the next decade as there are too many discipline-specific modalities that currently prevail for such generalisation efforts to be effective. On the other hand, a more pragmatic approach is to start from what currently exists, identify best practices and key issues, and promote effective inter-domain collaboration among different components forming an ecosystem. This will promote interoperability, data exchange, data preservation, and distributed access (among others). This ecosystem of interoperable research data infrastructures will be composed of regional, disciplinary, and multidisciplinary components, such as libraries, archives, and data centres, offering data services for both primary datasets and publications. The ecosystem will support data-intensive science and research and stimulate the interaction among all its elements, thus promoting multidisciplinary and interdisciplinary science. This special issue includes a set of independent papers from renowned experts on organisational and technological issues related to GRDIs. These documents feed into and compliment the GRDI2020 roadmap, which supports a Global Research Data Infrastructure ecosystem. [ABSTRACT FROM AUTHOR]

The vision of Grid computing is to facilitate worldwide resource sharing among distributed collaborations. With the help of numerous national and international Grid projects, this vision is becoming reality and Grid systems are attracting an ever increasing user base. However, Grids are still quite complex software systems whose efficient use is a difficult and error-prone task. In this paper we present performance engineering techniques that aim to facilitate an efficient use of Grid systems, in particular systems that deal with the management of large-scale data sets in the tera- and petabyte range (also referred to as data Grids). These techniques are applicable at different layers of a Grid architecture and we discuss the tools required at each of these layers to implement them. Having discussed important performance engineering techniques, we investigate how major Grid projects deal with performance issues particularly related to data Grids and how they implement the techniques presented. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Within the European DataGrid project, Work Package 2 has designed and implemented a set of integrated replica management services for use by data intensive scientific applications. These services, based on the web services model, enable movement and replication of data at high speed from one geographical site to another, management of distributed replicated data, optimization of access to data, and the provision of a metadata management tool. In this paper we describe the architecture and implementation of these services and evaluate their performance under demanding Grid conditions. [ABSTRACT FROM AUTHOR]

Statistical software testing is an increasingly popular method in the software development cycle. An exact modeling of the usage profiles of a software system is an indispensable prerequisite for statistical testing. Recently, new techniques for obtaining optimal usage profiles even the presence of rarely used critical functions have been introduced. Although these techniques deliver unbiased dependability estimates with a single model (instead of using multiple models as it is the current practice) their applicability is hampered by their prohibitive computational complexity. In this paper we demonstrate that these techniques can be effectively employed even for large software systems by exploiting high performance computing. We discuss the parallelism potential within these techniques and present a parallel implementation based on a high level parallel language— High Performance Fortran. Our techniques are general enough to allow an efficient optimization of medium to large scale test plans on high-end parallel machines as well as on more cost effective PC-clusters. A real world software system is studied in detail to prove our claims. [ABSTRACT FROM AUTHOR]

Opus is a new programming language designed to assist in coordinating the execution of multiple, independent program modules. With the help of Opus, coarse grained task parallelism between data parallel modules can be expressed in a clean and structured way. In this paper we address the problems of how to build a compilation and runtime support system that can efficiently implement the Opus constructs. Our design considers the often-conflicting goals of efficiency and modular construction through software re-use. In particular, we present the system requirements for an efficient Opus implementation, the Opus runtime system, and describe how they work together to provide the underlying services that the Opus compiler needs for a broad class of machines. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The coordination language Opus is an object-based extension of High Performance Fortran (HPF) that supports the integration of coarse-grain task parallelism with HPF-style data parallelism. In this paper we discuss Opus in the Context of multidisciplinary applications (MDAs) which execute in a heterogencous environment. After outlining the major properties of such applications and a number of different approaches towards providing language and tool support for MDAs we describe the salientfeatures of Opus and its implementation, emphasizing the issues related to the coordination of data-parallel HPF programs in a heterogencous environment. [ABSTRACT FROM AUTHOR]

Background: Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues. Contributions: We present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library. The method uses conformal prediction to produce valid measures of prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical structures to the panel of targets with QuickVina on individual compound basis. Results: The docking procedure and resulting models were validated by docking well-known inhibitors for each of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking scores against an external validation set. The implementation as publicly available microservices on Kubernetes ensures resilience, scalability, and extensibility. [ABSTRACT FROM AUTHOR]

We carried out a 3D fully kinetic simulation of Earth's magnetotail magnetic reconnection to study the dynamics of energetic particles. We developed and implemented a new relativistic particle mover in iPIC3D, an implicit Particle-in-Cell code, to correctly model the dynamics of energetic particles. Before the onset of magnetic reconnection, energetic electrons are found localized close to current sheet and accelerated by lower hybrid drift instability. During magnetic reconnection, energetic particles are found in the reconnection region along the x-line and in the separatrices region. The energetic electrons are first present in localized stripes of the separatrices and finally cover all the separatrix surfaces. Along the separatrices, regions with strong electron deceleration are found. In the reconnection region, two categories of electron trajectory are identified. First, part of the electrons are trapped in the reconnection region, bouncing a few times between the outflow jets. Second, part of the electrons pass the reconnection region without being trapped. Different from electrons, energetic ions are localized on the reconnection fronts of the outflow jets. [ABSTRACT FROM PUBLISHER]