BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//WSSP - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:WSSP
X-ORIGINAL-URL:https://www.wssp.hlrs.de
X-WR-CALDESC:Events for WSSP
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20230326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20231029T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20241027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20251026T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240617
DTEND;VALUE=DATE:20240619
DTSTAMP:20260923T201939
CREATED:20240527T091834Z
LAST-MODIFIED:20240618T075544Z
UID:292-1718582400-1718755199@www.wssp.hlrs.de
SUMMARY:37th Workshop on Sustained Simulation Performance
DESCRIPTION:Agenda\nAll times are given in Central European Summer Time (CEST).  \n\n\n\n\n\nMonday\, June 17th \, 2024\n\n\n\n\n10:00 – 10:15\nWelcome & Introduction\nMichael Resch\, High-Performance Computing Center Stuttgart\, University of Stuttgart\n\n\n10:15 – 10:45\nOperational experience of the latest-generation SX-Aurora TSUBASA system\, AOBA-S\nHiroyuki Takizawa\, Cyberscience Center\, Tohoku University \nTohoku University Cyberscience Center started operation of the world’s largest SX-Aurora TSUBASA system named AOBA-S in August 2023. This talk reports the experience of operating the AOBA-S system while showing some performance evaluation results and discussions. Several important applications have already been optimized for AOBA-S\, and the performance evaluation results clearly suggest the potential of the latest-generation vector engines adopted in AOBA-S. This talk also introduces our research projects that have recently started in collaboration with AOBA users.\n\n\n10:45 – 11:15\nPerformance of a direct numerical simulation code for isothermal compressible turbulence on the SX-Aurora TSUBASA\nMitsuo Yokokawa\, Kobe University \nA direct numerical simulation code for compressible turbulent flows under isothermal conditions in a box with periodic boundary conditions was developed. A finite difference method is used for a discretization of the governing equations. In paticular\, an eighth-order compact difference scheme was used for the covective terms and the Mattor’s method\, which is a parallel solver for a linear system with tridiagonal matrix\, was used to compute the first-order derivative of the covective terms. Performance of the DNS code was measured on the SX-Aurora TSUBASA.\n\n\n11:15 – 11:45\nAim and Strategy of mdx2\, IaaS-typed Computing Infrastructure\nSusumu Date\, Osaka University \nThe Cybermedia Center at Osaka University has installed an IaaS-typed Computing Infrastructure in March 2024 and will soon start the IaaS service. In this talk\, the speaker will start with the background and show the aim and strategy of this system installation from now on as well as overview the structure of the system.\n\n\n11:45 – 12:15\nImproving Efficiency of Monte Carlo Method via Code Intrinsic framework\nQifeng Pan\, High-Performance Computing Center Stuttgart\, University of Stuttgart \nThe Monte Carlo (MC) method is widely used in many engineering fields\, especially in uncertainty quantification\, due to its robustness and simplicity. However\, the existing execution pattern of MC suffers from low efficiency and scaling problems in high-performance computing (HPC). In this talk\, the speaker will introduce the code intrinsic framework designed to tackle the efficiency problem of MC in HPC. The basic idea of the code intrinsic framework is to reduce the redundant calculations of MC and increase the code vectorization rate. Numerical results show that performance improvements can be achieved on various platforms\, including the Intel and SX-Aurora TSUBASA machines.\n\n\n\n12:15 – 13:15\nLunch Break\n\n\n\n13:15 – 13:45\nDEPO Meets Mechanics: A Case Study on Dynamic Power Capping for Energy Efficiency\nJohannes Gebert\, Jonathan Schäfer\, High-Performance Computing Center Stuttgart\, University of Stuttgart \nMoore’s law is slowing down despite HPC centers’ increasing energy consumption. For cost and climate reasons\, developing techniques for reducing energy usage while at least maintaining performance is sensible. DEPO\, a software-agnostic\, node-level\, and dynamic power-capping approach by Krzywaniak\, Czarnul\, and Proficz (2022) promises to achieve these goals. We present their approach to real-world challenges based on an FEA. The Direct Tensor Computation (DTC) of Ralf Schneider and Johannes Gebert is run with DEPO to demonstrate the tools’ applicability. We explore different points in the input configuration space of the application and investigate the impact on energy consumption under power capping. Furthermore\, we present and discuss different ways to continue and expand this research.\n\n\n\n13:45 – 14:15\nConnecting Software Methods and Data Driven Methods\nSabine Roller\, German Aerospace Center\, TU Dresden\n\n\n14:15 – 14:45\nEvaluating a Real-Time Lossy Array Compression Algorithm for Computer Simulations\nDarjan Krijan\, High-Performance Computing Center Stuttgart\, University of Stuttgart \nComputer Simulations that were previously regarded as CPU-bound become gradually memory-bound as the growth in memory bandwidth cannot keep up with the much higher advancements in raw computational power. This imbalance is quantified with a relative factor of approximately5.1 per decade since the 1990s\, where a rise in memory bandwidth is met with a 5.1-times increase in relative computing power. In practical terms\, comparing a NEC SX-4 from 1994 that operated at a balanced computational intensity of 0.125 FLOP/Byte with an Intel Ponte Vecchio accelerator from 2023 that operates at 15.9 FLOP/Byte shows a factor of 127 in the described imbalance. Mixed precision approaches that were traditionally used to speed up the throughput of calculations inside the CPU core on a cache/register level now provide speedup due to less demanded memory bandwidth. Approaches to compress arrays in a lossless or lossy manner to reduce memory bandwidth were implemented in LLNL’s zfp library\, although it is not able to process the data in real-time. A similar approach targeting a real-time lossy array compression (RTLAC) algorithm is currently being evaluated for use in highly memory-bound computer simulations at HLRS.\n\n\n14:45 – 15:15\nCoffee Break\n\n\n15:15 – 15:45\nDirect Numerical Simulation of Turbulent Boundary Layers – On the Road to High Reynolds Numbers\nChristoph Wenzel\, Institute of Aerodynamics and Gas Dynamics\, University of Stuttgart\n\n\n\n15:45 – 16:15\nDatenmanagement for HPC Workloads at Scale: Case Studies on Data Structure and Compression.\nGregror Weiß\, High-Performance Computing Center Stuttgart\, University of Stuttgart\n\n\n\n16:15 – 16:45\nCloud resolving global weather simulation with the Model for Prediction Across Scales (MPAS) – A case study\nThomas Schwitalla\, University of Hohenheim\n\n\n\n16:45 – 17:15\nThe Quest for Sustained Performance on Heterogeneous Exascale Architectures with the Climate Model ICON\nPanagiottis Adamidis\, Deutsches Klimarechenzentrum Hamburg\n\n\n\n18:30\nDinner\n\n\n\n\nTuesday\, June 18\, 2024\n\n\n\n\n09:00 – 09:30\nChallenges for HLRS ahead\nMichael Resch\, High-Performance Computing Center Stuttgart\, University of Stuttgart \nThis talk summarizes the challenges that lay ahead for HLRS in the coming decade. It looks into the challenges that we face when changing technology from CPU to GPU. It will also address the issue of AI as a new user community in HPC. \n\n\n09:30 – 10:00\nAnalyses of Turbomachinery and Heat Transfer Cases using HPC Systems\nMatthias Meinke\, Institute of Aerodynamics\, RWTH Aachen University \nThis presentation will highlight computational methodologies and results from two industrial applications related to the field of turbomachinery and steel casting. Details of the computational methods featuring\, adaptive mesh refinement\, dynamic load balancing and multigrid methods will be presented together with the required HPC resources.\n\n\n10:00 – 10:30\nAlgorithmic Differentiation of Geometric Modelling Libraries Aimed at Gradient-Based Shape Optimization\nMladen Banovic\, German Aerospace Center\, TU Dresden\n\n\n\n10:30 – 11:00\nCoffee Break\n\n\n\n11:00 – 11:30\nML for Computational Science: Machine Learning Models to Accelerate Simulation Science on HPC\nMakoto Takamoto\, NEC Germany\n\n\n\n11:30 – 12:00\nA Constraint Partition Method for Combinatorial Optimization Problems\nKazuhiko Komatsu\, Cyberscience Center\, Tohoku University \nIn recent years\, Ising machines have attracted much attention due to their potential in solving combinatorial optimization problems that are challenging for conventional computers. For optimization problems formulated into quadratic unconstrained binary optimization (QUBO) problems with constraints\, known as constraint problems\, an objective function and constraint function\, along with penalty coefficients\, are combined into a single Hamiltonian of a QUBO problem. However\, when solving constraint problems\, solution accuracy typically degrades by excessively large penalty coefficients avoiding constraint violations. As a result\, minimizing the objective function becomes challenging. To solve this issue\, the presentation introduces a method that partitions constraint functions and reduces penalty coefficient values. Performance evaluation using the traveling salesperson problem (TSP) with one-hot constraints illustrates that the proposed method enhances solution accuracy compared to conventional approaches.\n\n\n12:00 – 12:30\nAn analyse of Kernels Performances when porting a CFD code to Nvidia GPUs using different Programming Models\nPaul Saumet\, High-Performance Computing Center Stuttgart\, University of Stuttgart\n\n\n\n12:30 – 13:30\nLunch\n\n\n13:30 – 14:00\nAI for HPC: Optimising Operations\nRishabh Saxena\, High-Performance Computing Center Stuttgart\, University of Stuttgart \nIn the last few years\, Artificial Intelligence and Machine Learning have been dominant topics in the general field of computer science\, and the scientific community as well. Since one of the core principles of machine learning-based algorithms requires a huge amount of data being processed on a large scale\, it is expectant that high performance hardware would be needed for such computations. In this context\, HPC can provide a suitable platform for various aspects of the ML pipeline\, from data pre-processing to the deployment of models in production environment. In this talk\, we will look at how HLRS is working towards optimizing its systems for AI and ML workloads\, what are the aspects of ML pipelines that are relevant for HPC\, how traditional HPC workloads\, like simulations\, can be integrated into ML pipelines\, and what is the outlook for AI on HPC in the near future at HLRS.\n\n\n14:00 – 14:30\nModelling of a Offshore Wind Park with OpenFOAM\nFlavio Galeazzo\, High-Performance Computing Center Stuttgart\, University of Stuttgart\n\n\n\n14:30 – 15:00\n HANAMI: Advancing Supercomputing Collaboration Between Europe and Japan\nSophia Honisch\, High-Performance Computing Center Stuttgart\, University of Stuttgart \nThe HANAMI project\, a strategic alliance between Europe and Japan\, aims to innovate high-performance computing (HPC) applications for next-generation supercomputers across various scientific domains. This collaboration focuses on enhancing simulation capabilities in environmental sciences\, biomedicine\, and materials science. Aligned with the EuroHPC Joint Undertaking\, HANAMI will port existing code\, evaluate application performance on new architectures\, and facilitate access to advanced supercomputing resources like Fugaku and EuroHPC systems.\n\n\n15:00 – 15:45\n\nFarewell\n\n\n\n  \n  \n 
URL:https://www.wssp.hlrs.de/events/37th-workshop-on-sustained-simulation-performance/
LOCATION:HLRS\, Nobelstraße 19\, Stuttgart\, Baden-Württemberg\, 70569\, Germany
ORGANIZER;CN="Mr. Johannes Gebert":MAILTO:gebert@hlrs.de
END:VEVENT
END:VCALENDAR