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Electronic structure computations of atoms and ions have a long tradition in physics with applications in basic research, spectroscopy, life sciences and technology. Various theoretical methods (and codes) have therefore been developed to account for the many-particle structure of atoms, from simple semi-empirical estimates to accurate predictions of selected data, and up to highly advanced time-independent and time-dependent numerical techniques. — Here, I present a fresh concept and implementation of (relativistic) atomic structure theory that supports the computation of interaction amplitudes, properties as well as a large number of excitation and decay processes for open-shell atoms and ions across the whole periodic table. This implementation will facilitate also studies on atomic cascades, responses as well as the time-evolution of atoms and ions. It is based on Julia, a new programming language for scientific computing, and provides an easy-to-use but powerful platform to extent atomic theory towards new applications.

Software to analyse very large sets of experimental data often relies on a pipeline of irregular computational tasks with decisions to remove irrelevant data from further processing. A user-centred framework was designed and deployed, HEP-Frame, which aids domain experts to develop applications for scientific data analyses and to monitor and control their efficient execution. The key feature of HEP-Frame is the performance portability of the code across different heterogeneous platforms, due to a novel adaptive multi-layer scheduler, seamlessly integrated into the tool, an approach not available in competing frameworks. The multi-layer scheduler transparently allocates parallel data/tasks across the available heterogeneous resources, dynamically balances threads among data input and computational tasks, adaptively reorders in run-time the parallel execution of the pipeline stages for each data stream, respecting data dependencies, and efficiently manages the execution of library functions in accelerators. Each layer implements a specific scheduling strategy: one balances the execution of the computational stages of the pipeline, distributing the execution of the stages of the same or different dataset elements among the available computing threads; another controls the order of the pipeline stages execution, so that most data is filtered out earlier and later stages execute the computationally heavy tasks; yet another adaptively balances the automatically created threads among data input and the computational tasks, taking into account the requirements of each application. Simulated data analyses from sensors in the ATLAS Experiment at CERN evaluated the scheduler efficiency, on dual multicore Xeon servers with and without accelerators, and on servers with the many-core Intel KNL. Experimental results show significant improved performance of these data analyses due to HEP-Frame features and the codes scaled well on multiple servers. Results also show the improved HEP-Frame scheduler performance over the key competitor, the HEFT list scheduler. The best overall performance improvement over a real fine tuned sequential data analysis was impressive in both homogeneous and heterogeneous multicore servers and in many-core servers: 81x faster in the homogeneous 24+24 core Skylake server, 86x faster in the heterogeneous 12+12 core Ivy Bridge server with the Kepler GPU, and 252x faster in the 64-core KNL server. Program summary Program Title: HEP-Frame CPC Library link to program files: https://doi.org/10.17632/m2jwxshtfz.1 Licencing provisions: GPLv3 Programming language: C++. Supplementary material: The current HEP-Frame public release available at https://bitbucket.org/ampereira/hep-frame/wiki/Home . Nature of problem: Scientific data analysis applications are often developed to process large amounts of data obtained through experimental measurements or Monte Carlo simulations, aiming to identify patterns in the data or to test and/or validate theories. These large inputs are usually processed by a pipeline of computational tasks that may filter out irrelevant data (a task and its filter is addressed as a proposition in this communication), preventing it from being processed by subsequent tasks in the pipeline. This data filtering, coupled with the fact that propositions may have different computational intensities, contribute to the irregularity of the pipeline execution. This can lead to scientific data analyses I/O-, memory-, or compute-bound performance limitations, depending on the implemented algorithms and input data. To allow scientists to process more data with more accurate results their code and data structures should be optimized for the computing resources they can access. Since the main goal of most scientists is to obtain results relevant to their scientific fields, often within strict deadlines, optimizing the performance of their applications is very time consuming and is usually overlooked. Scientists require a software framework to aid the design and development of efficient applications and to control their parallel execution on distinct computing platforms. Solution method: This work proposes HEP-Frame, a framework to aid the development and efficient execution of pipelined scientific analysis applications on homogeneous and heterogeneous servers. HEP-Frame is a user-centred framework to aid scientists to develop applications to analyse data from a large number of dataset elements, with a flexible pipeline of propositions. It not only stresses the interface to domain experts so that code is more robust and is developed faster, but it also aims high-performance portability across different types of parallel computing platforms and desirable sustainability features. This framework aims to provide efficient parallel code execution without requiring user expertise in parallel computing. Frameworks to aid the design and deployment of scientific code usually fall into two categories: (i) resource-centred, closer to the computing platforms, where execution efficiency and performance portability are the main goals, but forces developers to adapt their code to strict framework constraints; (ii) user-centred, which stresses the interface to domain experts to improve their code development speed and robustness, aiming to provide desirable sustainability features but disregarding the execution performance. There are also a set of frameworks that merge these two categories (Liu et al., 2015 [1] ; Deelman et al., 2015 [2] ) for scientific computing. While they do not have steep learning curves, concessions have to be made to their ease of use to allow for their broader scope of targeted applications. HEP-Frame attempts to merge this gap, placing itself between a fully user- or resource-centred framework, so that users develop code quickly and do not have to worry about the computational efficiency of the code It handles (i) by ensuring efficient execution of applications according to their computational requirements and the available resources on the server through a multi-layer scheduler, while (ii) is addressed by automatically generating code skeletons and transparently managing the data structure and automating repetitive tasks. Additional comments: An early stage proof-of-concept was published in a conference proceedings (Pereira et al., 2015). However, the HEP-Frame version presented in this communication only shares a very small portion of the code related to the skeleton generation (less than 5% of the overall code), while the rest of the user interface, multi-layer scheduler, and parallelization strategies were completely redesigned and re-implemented.

In recent years graphical processing units (GPUs) have become a powerful tool in scientific computing. Their potential to speed up highly parallel applications brings the power of high performance computing to a wider range of users. However, programming these devices and integrating their use in existing applications is still a challenging task. In this paper we examined the potential of GPUs for two different applications. The first application, created at Paul Scherrer Institut (PSI), is used for parameter fitting during data analysis of μ SR (muon spin rotation, relaxation and resonance) experiments. The second application, developed at ETH, is used for PET (Positron Emission Tomography) image reconstruction and analysis. Applications currently in use were examined to identify parts of the algorithms in need of optimization. Efficient GPU kernels were created in order to allow applications to use a GPU, to speed up the previously identified parts. Benchmarking tests were performed in order to measure the achieved speedup. During this work, we focused on single GPU systems to show that real time data analysis of these problems can be achieved without the need for large computing clusters. The results show that the currently used application for parameter fitting, which uses OpenMP to parallelize calculations over multiple CPU cores, can be accelerated around 40 times through the use of a GPU. The speedup may vary depending on the size and complexity of the problem. For PET image analysis, the obtained speedups of the GPU version were more than × 40 larger compared to a single core CPU implementation. The achieved results show that it is possible to improve the execution time by orders of magnitude.

Nonequispaced discrete Fourier transformation (NDFT) is widely applied in all aspects of computational science and engineering. The computational efficiency and accuracy of NDFT has always been a critical issue in hindering its comprehensive applications both in intensive and in extensive aspects of scientific computing. In our previous work (2018, S.-C. Yang et al., Appl. Comput. Harmon. Anal. 44, 273), a CUNFFT method was proposed and it shown outstanding performance in handling NDFT at intermediate scale based on CUDA (Compute Unified Device Architecture) technology. In the current work, we further improved the computational efficiency of the CUNTTF method using an efficient MPI-CUDA hybrid parallelization (HP) scheme of NFFT to achieve a cutting-edge treatment of NDFT at super extended scale. Within this HP-NFFT method, the spatial domain of NDFT is decomposed into several parts according to the accumulative feature of NDFT and the detailed number of CPU and GPU nodes. These decomposed NDFT subcells are independently calculated on different CPU nodes using a MPI process-level parallelization mode, and on different GPU nodes using a CUDA threadlevel parallelization mode and CUNFFT algorithm. A massive benchmarking of the HP-NFFT method indicates that this method exhibit a dramatic improvement in computational efficiency for handling NDFT at super extended scale without loss of computational precision. Furthermore, the HP-NFFT method is validated via the calculation of Madelung constant of fluorite crystal structure, and thereafter verified that this method is robust for the calculation of electrostatic interactions between charged ions in molecular dynamics simulation systems., 16 pages, 16 figures

Recent developments in European supercomputing are reviewed covering both the latest hardware trends and the increasing difficulties faced by scientists in utilising these machines to perform large-scale numerical simulations. These challenges are reflected in the large number of international initiatives which have come into being over the last few years, founded in anticipation of exascale hardware which is foreseen within the next decade. The role of a key institution in supercomputing within these programmes is described using the example of the Julich Supercomputing Centre (JSC), and progress in setting up its own community-oriented support units for scientific computing – Simulation Laboratories – is reported on. Finally, an assessment is made of some common grand challenges and their suitability for scaling to exaflop-scale computation.

Many simulations of stochastic processes require colored noises: here I describe a small program library that generates samples with a tunable power-law spectral density: the algorithm can be modified to generate more general colored noises, and is exact for all time steps, even when they are unevenly spaced (as may often happen in the case of astronomical data, see e.g. [N.R. Lomb, Astrophys. Space Sci. 39 (1976) 447]. The method is exact in the sense that it reproduces a process that is theoretically guaranteed to produce a range-limited power-law spectrum 1 / f 1 + β with − 1 β ⩽ 1 . The algorithm has a well-behaved computational complexity, it produces a nearly perfect Gaussian noise, and its computational efficiency depends on the required degree of noise Gaussianity. Program summary Title of program: PLNoise Catalogue identifier: ADXV_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADXV_v1_0.html Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Licensing provisions: none Programming language used: ANSI C Computer: Any computer with an ANSI C compiler: the package has been tested with gcc version 3.2.3 on Red Hat Linux 3.2.3-52 and gcc version 4.0.0 and 4.0.1 on Apple Mac OS X-10.4 Operating system: All operating systems capable of running an ANSI C compiler No. of lines in distributed program, including test data, etc.: 6238 No. of bytes in distributed program, including test data, etc.: 52 387 Distribution format: tar.gz RAM: The code of the test program is very compact (about 50 Kbytes), but the program works with list management and allocates memory dynamically; in a typical run (like the one discussed in Section 4 in the long write-up) with average list length 2 ⋅ 10 4 , the RAM taken by the list is 200 Kbytes. External routines: The package needs external routines to generate uniform and exponential deviates. The implementation described here uses the random number generation library ranlib freely available from Netlib [B.W. Brown, J. Lovato, K. Russell, ranlib , available from Netlib, http://www.netlib.org/random/index.html , select the C version ranlib.c ], but it has also been successfully tested with the random number routines in Numerical Recipes [W.H. Press, S.A. Teulkolsky, W.T. Vetterling, B.P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, second ed., Cambridge Univ. Press, Cambridge, 1992, pp. 274–290]. Notice that ranlib requires a pair of routines from the linear algebra package LINPACK , and that the distribution of ranlib includes the C source of these routines, in case LINPACK is not installed on the target machine. Nature of problem: Exact generation of different types of Gaussian colored noise. Solution method: Random superposition of relaxation processes [E. Milotti, Phys. Rev. E 72 (2005) 056701]. Unusual features: The algorithm is theoretically guaranteed to be exact, and unlike all other existing generators it can generate samples with uneven spacing. Additional comments: The program requires an initialization step; for some parameter sets this may become rather heavy. Running time: Running time varies widely with different input parameters, however in a test run like the one in Section 4 in this work, the generation routine took on average about 7 ms for each sample.