Towards a Mini-App for Smoothed Particle Hydrodynamics at Exascale

The smoothed particle hydrodynamics (SPH) technique is a purely Lagrangian method, used in numerical simulations of fluids in astrophysics and computational fluid dynamics, among many other fields. SPH simulations with detailed physics represent computationally-demanding calculations. The parallelization of SPH codes is not trivial due to the absence of a structured grid. Additionally, the performance of the SPH codes can be, in general, adversely impacted by several factors, such as multiple time-stepping, long-range interactions, and/or boundary conditions. This work presents insights into the current performance and functionalities of three SPH codes: SPHYNX, ChaNGa, and SPH-flow. These codes are the starting point of an interdisciplinary co-design project, SPH-EXA, for the development of an Exascale-ready SPH mini-app. To gain such insights, a rotating square patch test was implemented as a common test simulation for the three SPH codes and analyzed on two modern HPC systems. Furthermore, to stress the differences with the codes stemming from the astrophysics community (SPHYNX and ChaNGa), an additional test case, the Evrard collapse, has also been carried out. This work extrapolates the common basic SPH features in the three codes for the purpose of consolidating them into a pure-SPH, Exascale-ready, optimized, mini-app. Moreover, the outcome of this serves as direct feedback to the parent codes, to improve their performance and overall scalability.Comment: 18 pages, 4 figures, 5 tables, 2018 IEEE International Conference on Cluster Computing proceedings for WRAp1

Form-function relationships in dragonfly mandibles under an evolutionary perspective

© 2017 The Author(s). Functional requirements may constrain phenotypic diversification or foster it. For insect mouthparts, the quantification of the relationship between shape and function in an evolutionary framework remained largely unexplored. Here, the question of a functional influence on phenotypic diversification for dragonfly mandibles is assessed with a large-scale biomechanical analysis covering nearly all anisopteran families, using finite element analysis in combination with geometric morphometrics. A constraining effect of phylogeny could be found for shape, the mandibular mechanical advantage (MA), and certain mechanical joint parameters, while stresses and strains, the majority of joint parameters and size are influenced by shared ancestry. Furthermore, joint mechanics are correlated with neither strain nor mandibular MA and size effects have virtually play no role for shape or mechanical variation. The presence of mandibular strengthening ridges shows no phylogenetic signal except for one ridge peculiar to Libelluloidea, and ridge presence is also not correlated with each other. The results suggest that functional traits are more variable at this taxonomic level and that they are not influenced by shared ancestry. At the same time, the results contradict the widespread idea that mandibular morphology mainly reflects functional demands at least at this taxonomic level. The varying functional factors rather lead to the same mandibular performance as expressed by the MA, which suggests a many-to-one mapping of the investigated parameters onto the same narrow mandibular performance space

Assessing the role of mini-applications in predicting key performance characteristics of scientific and engineering applications

Computational science and engineering application programs are typically large, complex, and dynamic, and are often constrained by distribution limitations. As a means of making tractable rapid explorations of scientific and engineering application programs in the context of new, emerging, and future computing architectures, a suite of "miniapps" has been created to serve as proxies for full scale applications. Each miniapp is designed to represent a key performance characteristic that does or is expected to significantly impact the runtime performance of an application program. In this paper we introduce a methodology for assessing the ability of these miniapps to effectively represent these performance issues. We applied this methodology to three miniapps, examining the linkage between them and an application they are intended to represent. Herein we evaluate the fidelity of that linkage. This work represents the initial steps required to begin to answer the question, "Under what conditions does a miniapp represent a key performance characteristic in a full app?

Automated computation of materials properties

Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis techniques, underlying property trends can be identified, facilitating the formulation of new design rules. Such methods require large sets of consistently generated, programmatically accessible materials data. Computational materials design frameworks using standardized parameter sets are the ideal tools for producing such data. This work reviews the state-of-the-art in computational materials design, with a focus on these automated $\textit{ab-initio}$ frameworks. Features such as structural prototyping and automated error correction that enable rapid generation of large datasets are discussed, and the way in which integrated workflows can simplify the calculation of complex properties, such as thermal conductivity and mechanical stability, is demonstrated. The organization of large datasets composed of $\textit{ab-initio}$ calculations, and the tools that render them programmatically accessible for use in statistical learning applications, are also described. Finally, recent advances in leveraging existing data to predict novel functional materials, such as entropy stabilized ceramics, bulk metallic glasses, thermoelectrics, superalloys, and magnets, are surveyed.Comment: 25 pages, 7 figures, chapter in a boo

SiSeRHMap v1.0: A simulator for mapped seismic response using a hybrid model

SiSeRHMap is a computerized methodology capable of drawing up prediction maps of seismic response. It was realized on the basis of a hybrid model which combines different approaches and models in a new and non-conventional way. These approaches 5 and models are organized in a code-architecture composed of five interdependent modules. A GIS (Geographic Information System) Cubic Model (GCM), which is a layered computational structure based on the concept of lithodynamic units and zones, aims at reproducing a parameterized layered subsoil model. A metamodeling process confers a hybrid nature to the methodology. In this process, the one-dimensional linear 10 equivalent analysis produces acceleration response spectra of shear wave velocitythickness profiles, defined as trainers, which are randomly selected in each zone. Subsequently, a numerical adaptive simulation model (Spectra) is optimized on the above trainer acceleration response spectra by means of a dedicated Evolutionary Algorithm (EA) and the Levenberg–Marquardt Algorithm (LMA) as the final optimizer. In the fi15 nal step, the GCM Maps Executor module produces a serial map-set of a stratigraphic seismic response at different periods, grid-solving the calibrated Spectra model. In addition, the spectra topographic amplification is also computed by means of a numerical prediction model. This latter is built to match the results of the numerical simulations related to isolate reliefs using GIS topographic attributes. In this way, different sets 20 of seismic response maps are developed, on which, also maps of seismic design response spectra are defined by means of an enveloping technique

BookLeaf: An Unstructured Hydrodynamics Mini-application

With the age of Exascale computing causing a diversification away from traditional CPU-based homogeneous clusters, it is becoming increasingly difficult to ensure that computationally complex codes are able to run on these emerging architectures. This is especially important for large physics simulations that are themselves becoming increasingly complex and computationally expensive. One proposed solution to the problem of ensuring these applications can run on the desired architectures is to develop representative mini-applications that are simpler and so can be ported to new frameworks more easily, but which are also representative of the algorithmic and performance characteristics of the original applications. In this paper we present BookLeaf, an unstructured Arbitrary Lagrangian-Eulerian mini-application to add to the suite of representative applications developed and maintained by the UK Mini-App Consortium (UK-MAC). First, we outline the reference implementation of our application in Fortran. We then discuss a number of alternative implementations using a variety of parallel programming models and discuss the issues that arise when porting such an application to new architectures. To demonstrate our implementation, we present a study of the performance of BookLeaf on number of platforms using alternative designs, and we document a scaling study showing the behaviour of the application at scale

An Investigation into the Performance and Portability of SYCL Compiler Implementations

In June 2022, Frontier became the first Supercomputer to “officially” break the ExaFLOP/s barrier on LINPACK, achieving a peak performance of 1.1×10^18 floating-point operations per second using AMD Instinct accelerators. Developing high performance applications for such platforms typically requires the adoption of vendor-specific programming models, which in turn may limit portability. SYCL is a high-level, single-source language based on C++17, developed by the Khronos group to overcome the shortcomings of those vendor-specific HPC programming models. In this paper we present an initial study into the SYCL parallel programming model and its implementing compilers, to understand its performance and portability, and how this compares to other parallel programming models. We use three major SYCL implementations for our evaluation – Open SYCL (previously hipSYCL), DPC++, and ComputeCpp – on a range of CPU and GPU hardware from Intel, AMD, Fujitsu, Marvell, and NVIDIA. Our results show that for a simple finite difference mini-application, SYCL can offer competitive performance to native approaches, while for a more complex finite-element mini-application, significant performance degradation is observed. Our findings suggest that development work is required at the compiler- and application-level to ensure SYCL is competitive with alternative approaches