Optimising UCNS3D, a High-Order finite-Volume WENO Scheme Code for arbitrary unstructured Meshes

UCNS3D is a computational-fluid-dynamics (CFD) code for the simulation of viscous flows on arbitrary unstructured meshes. It employs very high-order numerical schemes which inherently are easier to scale than lower-order numerical schemes due to the higher ratio of computation versus communication. In this white paper, we report on optimisations of the UCNS3D code implemented in the course of the PRACE Preparatory Access Type C project “HOVE” in the time frame of February to August 2016. Through the optimisation of dense linear algebra operations, in particular matrix-vector products, by formula rewriting, pre-computation and the usage of BLAS, significant speedups of the code by factors of 2 to 6 have been achieved for representative benchmark cases. Moreover, very good scalability up to the order of 10,000 CPU cores has been demonstrated

Deploying AI Frameworks on Secure HPC Systems with Containers

The increasing interest in the usage of Artificial Intelligence techniques (AI) from the research community and industry to tackle "real world" problems, requires High Performance Computing (HPC) resources to efficiently compute and scale complex algorithms across thousands of nodes. Unfortunately, typical data scientists are not familiar with the unique requirements and characteristics of HPC environments. They usually develop their applications with high-level scripting languages or frameworks such as TensorFlow and the installation process often requires connection to external systems to download open source software during the build. HPC environments, on the other hand, are often based on closed source applications that incorporate parallel and distributed computing API's such as MPI and OpenMP, while users have restricted administrator privileges, and face security restrictions such as not allowing access to external systems. In this paper we discuss the issues associated with the deployment of AI frameworks in a secure HPC environment and how we successfully deploy AI frameworks on SuperMUC-NG with Charliecloud.Comment: 6 pages, 2 figures, 2019 IEEE High Performance Extreme Computing Conferenc

Analyzing and Modeling the Performance of the HemeLB Lattice-Boltzmann Simulation Environment

We investigate the performance of the HemeLB lattice-Boltzmann simulator for cerebrovascular blood flow, aimed at providing timely and clinically relevant assistance to neurosurgeons. HemeLB is optimised for sparse geometries, supports interactive use, and scales well to 32,768 cores for problems with ~81 million lattice sites. We obtain a maximum performance of 29.5 billion site updates per second, with only an 11% slowdown for highly sparse problems (5% fluid fraction). We present steering and visualisation performance measurements and provide a model which allows users to predict the performance, thereby determining how to run simulations with maximum accuracy within time constraints.Comment: Accepted by the Journal of Computational Science. 33 pages, 16 figures, 7 table

Performance analysis of electronic structure codes on HPC systems: A case study of SIESTA

We report on scaling and timing tests of the SIESTA electronic structure code for ab initio molecular dynamics simulations using density-functional theory. The tests are performed on six large-scale supercomputers belonging to the PRACE Tier-0 network with four different architectures: Cray XE6, IBM BlueGene/Q, BullX, and IBM iDataPlex. We employ a systematic strategy for simultaneously testing weak and strong scaling, and propose a measure which is independent of the range of number of cores on which the tests are performed to quantify strong scaling efficiency as a function of simulation size. We find an increase in efficiency with simulation size for all machines, with a qualitatively different curve depending on the supercomputer topology, and discuss the connection of this functional form with weak scaling behaviour. We also analyze the absolute timings obtained in our tests, showing the range of system sizes and cores favourable for different machines. Our results can be employed as a guide both for running SIESTA on parallel architectures, and for executing similar scaling tests of other electronic structure codes.Comment: 9 pages, 9 figure