A well-designed high-performance computing (HPC) course not only presents theoretical parallelism concepts but also includes practical work on parallel systems. Today's machine models are diverse and as a consequence multiple programming models exist. The challenge for HPC course lecturers is to decide what to include and what to exclude, respectively. We have experience in teaching HPC in a multi-paradigm style. The practical course parts include message-passing programming using MPI, directive-based shared memory programming using OpenMP, partitioned global address space based programming using Chapel, and domain-specific programming using a high-level framework. If these models are taught in an isolated mode, students would have problems in assessing the strengths and weaknesses of the approaches presented. We propose a projectbased approach which introduces a specific problem to be solved (in our case a stencil computation) and asks for solutions using the programming approaches introduced. Our course has been successfully taught several times but a major problem has always been checking the individual student solutions, especially to decide which performance results reported one can trust. In order to overcome these deficiencies, we have built a pedagogical tool which enhances the trust in students' work. In the paper we present the infrastructure and tools that make student experiments easily reproducible by lecturers. We introduce a taxonomy for general benchmark experiments, describe the distributed architecture of our development and analysis environment, and, as a case study, discuss performance experiments when solving a stencil problem in multiple programming models.

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For decades, the majority of the experiments on parallel computers have been reported at conferences and in journals usually without the possibility to verify the results presented. Thus, one of the major principles of science, reproducible results as a kind of correctness proof, has been neglected in the field of experimental high-performance computing. While this is still the state-of-the-art, current research targets for solutions to this problem. We discuss early results regarding reproducibility from a benchmark case study we did. In our experiments we explore the class of stencil calculations that are part of many scientific kernels and compare the performance results of four stencil compilers. In order to make these experiments reproducible from remote, a first prototype of an replication engine has been developed that can be accessed via the internet.

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 computationallydemanding 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 SPHflow. These codes are the starting point of an interdisciplinary co-design project, SPH-EXA, for the development of an Exascale-ready SPH miniapp. 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, Exascaleready, optimized, mini-app. Moreover, the outcome of this serves as direct feedback to the parent codes, to improve their performance and overall scalability.arXiv:1809.08013v1 [physics.comp-ph]

The data in this article are related to the research article titled “Reproducible Stencil Compiler Benchmarks Using PROVA!”. Stencil kernels have been implemented using a naïve OpenMP (OpenMP Architecture Review Board, 2016) [1] parallelization and then using the stencil compilers PATUS (Christen et al., 2011) [2] and (Bondhugula et al., 2008) PLUTO [3]. Performance experiments have been run on different architectures, by using PROVA! (Guerrera et al., 2017) [4], a distributed workflow and system management tool to conduct reproducible research in computational sciences. Information like version of the compiler, compilation flags, configurations, experiment parameters and raw results are fundamental contextual information for the reproducibility of an experiment. All this information is automatically stored by PROVA! and, for the experiments presented in this paper, are available at https://github.com/sguera/FGCS17.