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DTSTART;TZID=Europe/Berlin:20240311T090000
DTEND;TZID=Europe/Berlin:20240315T170000
DTSTAMP:20260427T002942
CREATED:20231214T125226Z
LAST-MODIFIED:20240318T101315Z
UID:6690-1710147600-1710522000@blog.rwth-aachen.de
SUMMARY:PPCES 2024
DESCRIPTION:in cooperation with \n\n\n\n\n\n\n\n\n\n  \n\nParallel Programming in Computational Engineering and Science (PPCES)\nMarch 11th – 15th\, 2024\n \nAbout PPCES\nThis one week event will continue the tradition of previous annual week-long events that take place in Aachen every spring since 2001. We will cover the basics of parallel programming using OpenMP and MPI in Fortran and C/C++ and a first step towards performance tuning. Furthermore\, we will embrace current topics in machine & deep learning. Hands-on exercises for each topic will be included. \nThe contents of the courses are generally applicable but will be specialized towards the compute cluster CLAIX which is the current system installed at the RWTH’s IT Center. It might be helpful to read through the information which is provided during the HPC introduction on February 5th and 6th 2024. This is especially true if you want to actively use CLAIX after this event. \nTopics\nOpenMP is a widely used approach for programming shared memory architectures\, supported by most compilers nowadays. We will cover the basics of the programming paradigm as well as some advanced topics such as programming NUMA machines. The nodes of the RWTH Compute Cluster contain an increasing number of cores and thus we consider shared memory programming a vital alternative for applications that cannot be easily parallelized with MPI. We also expect a growing number of application codes to combine MPI and OpenMP for clusters of nodes with a growing number of cores. \nThe Message Passing Interface (MPI) is the de-facto standard for programming large HPC systems. We will introduce the basic concepts and give an overview of some advanced features. Also covered is hybrid parallelization\, i.e.\, the combination of MPI and shared memory programming\, which is gaining popularity as the number of cores per cluster node grows. \nMachine & Deep Learning: We provide a fundamental introduction into machine and deep learning approaches as well as data processing techniques that support dataset preparation or model selection for training and inference. It covers the basic concepts for supervised and unsupervised learning such as classification\, regression\, and clustering to get a feeling which technique is appropriate for a particular problem. Additionally\, we conduct several hands-on exercises with common frameworks such as scikit-learn and PyTorch on our recent HPC infrastructure that is equipped with powerful Intel Sapphire Rapid CPUs and NVIDIA H100 GPUs. In these exercises\, we demonstrate how to define models\, construct dataset and training pipelines or utilize monitoring and visualization of the training results\, e.g.\, with Tensorboard. For the deep learning exercises\, we start by training on a single GPU first. If that is not enough\, we also show how to scale up distributed training onto multiple compute nodes or GPUs. \nOrganization\n\nThere is no seminar fee.\nPresentations will be given in English. Slides will be available during or after the event.\nThis is an in-person event and will be held on the RWTH IT Center premises (see below). Coffee\, tea\, and soda are sponsored by NEC throughout the event.\nYou can/must register per topic\, i.e.\, OpenMP (Mon + Tue)\, MPI (Wed + Thur)\, and ML/DL (Fri).\nFor more details on the target audience\, prerequistes and gained skills\, see the “Skills” sections below (per topic).\n\nGuest Speakers\nWe are happy to announce our guest speaker: Ruud van der Pas (Oracle Linux Engineering) \nAgenda\nAgenda is still subject to small changes. \nDay 1+2: OpenMP\n\n\n\nMonday\, March 11\nDay 1: OpenMP Part I\n\n\n\n09:00 – 09:10\nWelcome & Organization \nOpenMP Intro\nChristian Terboven (RWTH)\n\n\n09:10 – 10:10\nIntroduction into Parallel Computing\nRuud van der Pas (Oracle Linux Engineering)\n\n\n10:40 – 12:00\nOpenMP Basics – Part 1 \n\nOverview\nParallel Region\nWorksharing\nScoping\nCompilers + Exercises\n\n\nChristian Terboven (RWTH)\n\n\n13:30 – 14:30\nOpenMP Basics – Part 2 (incl. Lab)\nChristian Terboven (RWTH)\n\n\n14:30 – 15:30\nOpenMP Basics – Part 3 (incl. Lab) \n\nMotivation\nTasking Model\nTaskloop\nCutoff\nDependencies\n\n\nChristian Terboven (RWTH)\n\n\n16:00 – 17:00\nOpenMP Basics – Part 4 (incl. Lab) \n\nNUMA\nAffinity\nHybrid\n\n\nChristian Terboven (RWTH)\n\n\n\n  \n\n\n\n\nTuesday\, March 12\nDay 2: OpenMP Part II\n\n\n\n09:00 – 10:30\nSpeed up Your OpenMP Application Without Doing Much\nRuud van der Pas (Oracle Linux Engineering)\n\n\n11:00 – 12:00\nOpenMP SIMD\nChristian Terboven (RWTH)\n\n\n13:30 – 15:30\nOpenMP Advanced Tasking (incl. Lab)\nChristian Terboven (RWTH)\n\n\n16:00 – 17:00\nOpenMP for Accelerators\nChristian Terboven (RWTH)\n\n\n\n\n\nDay 3+4: MPI\n\n\n\nWednesday\, March 13\nDay 3: MPI Part I\n\n\n\n09:00 – 10:30\n\n\nMPI Basics\nMPI Concepts\n(incl. Lab)\n\n\nTobias Dollenbacher (RWTH)\,\nJoachim Jenke (RWTH)\n\n\n11:00 – 12:30\nBlocking Point-to-Point Communication – Part 1 (incl. Lab)\nTobias Dollenbacher (RWTH)\n\n\n14:00 – 15:30\nBlocking Point-to-Point Communication – Part 2 (incl. Lab)\nTobias Dollenbacher (RWTH)\n\n\n16:00 – 17:00\nNon-blocking Point-to-Point Communication (incl. Lab)\nJoachim Jenke (RWTH)\n\n\n\n  \n\n\n\nThursday\, March 14\nDay 4: MPI Part II\n\n\n\n09:00 – 10:30\nDerived Datatypes (incl. Lab)\nMarc-André Hermanns (RWTH)\n\n\n11:00 – 12:30\nBlocking Collective Communication (incl. Lab)\nMarc-André Hermanns (RWTH)\n\n\n14:00 – 15:30\nCommunicator Basics (incl. Lab)\nTobias Dollenbacher (RWTH)\n\n\n16:00 – 17:00\n\n\nHybrid Programming\nWrap-Up\n\n\nJannis Klinkenberg (RWTH)\n\n\n\n\nDay 5: Machine & Deep Learning\n\n\n\nFriday\, March 15\nDay 5: Machine Learning\n\n\n\n09:00 – 10:30\nIntroduction to AI and Machine Learning\, and Preprocessing Techniques – Part 1 \n\n01_ML-DL_Agenda\n02_ML-DL_Introduction_AI_ML\n\n\nJannis Klinkenberg (RWTH)\n\n\n11:00 – 12:30\nIntroduction to AI and Machine Learning\, and Preprocessing Techniques – Part 2 \n\n03_ML-DL_Execution_Options\nML-DL_Hands-on\n\n\nJannis Klinkenberg (RWTH)\n\n\n14:00 – 15:30\nIntroduction to (Distributed) Deep Learning \n\n04_ML-DL_Introduction_DL\n05_ML-DL_PyTorch\n06_ML-DL_Distributed_DL\n99_ML-DL_Further_Links_and_Literature\n\n\nJannis Klinkenberg (RWTH)\n\n\n16:00 – 17:00\nML-DL_Hands-on\nJannis Klinkenberg (RWTH)\n\n\n\n\nSkills – OpenMP\nCourse level\nbeginner \nTarget audience\n\nHPC users\nHPC developers\n\nPrerequisites\n\nAble to understand C/C++ (or Fortran) source codes\nComfortable with C/C++ or Fortran programming (for lab sessions)\nComfortable using a Linux environment (for lab sessions)\n\nGained skills\n\nBroad knowledge of OpenMP\, including the machine\, memory and execution model\nGeneral understanding when and how to use the different worksharing\, synchronization and tasking constructs and directives\nCapability to apply OpenMP to existing codes and to use the scoping correctly\nUnderstanding of how OpenMP parallel programs interact with the system\, in particular of NUMA architectures and SIMD microarchitectures\n\nSkills – MPI\nCourse level\nbeginner \nTarget audience\n\nHPC developers\n\nPrerequisites\n\nAble to understand C/C++ (or Fortran) source codes\nComfortable with C/C++ or Fortran programming (for lab sessions)\nComfortable using a Linux environment (for lab sessions)\n\nGained skills\n\nGeneral understanding of the classic MPI “world model” of process initialization\nOverview of point-to-point and collective communication an their use cases\nUnderstand the use and definition of MPI derived datatypes\nKnowledge of basic MPI terminology\nKnowledge of blocking and non-blocking procedure calls and when to use them\nUsing MPI together with OpenMP in basic hybrid scenarios\n\nSkills – ML/DL\nCourse level\n\nbeginner to intermediate\n\nTarget audience\n\nHPC/AI users\nHPC/AI developers\n\nPrerequisites\n\nBasic knowledge of Python\nComfortable using a JupyterHub or Linux environment (for lab sessions)\nComfortable with Python programming and a little Bash scripting (for lab sessions)\n\nGained skills\n\nHigh-level overview of data proprocessing\, AI\, machine and deep learning techniques and how they relate to each other\nUnderstanding how to apply shallow ML techniques to solve classification and clustering tasks\nUnderstanding how to execute PyTorch deep learning and distributed deep learning on HPC infrastructures\n\nMaterial Downloads\nOpenMP Labs\n\ntbd.\n\nMPI Labs\n\nppces2024-MPI-labs-C.tar.gz\nppces2024-MPI-labs-Fortran.tar.gz\nppces2024-MPI-labs-all.tar.gz\n\nML/DL Labs\n\nppces2024-ML-DL-labs.tar.gz\n\nFurther Links\nNeed support or want to ask questions? \n\nHPC Consultation Hour (once per month)\nAI/ML/DL Consultation Hour (once per week)\n\nWant to apply for compute time (proposals)? \n\nOverview of Compute Project Categories\nVisit WestAI website and send mail to contact@westai.de\n\nRegistration\nRegistration link: https://eveeno.com/ppces_2024 (until March 4th\, 2024) \nDuring registration\, you can select on which days you want to participate. \nSponsoring\nCatering is sponsored by NEC.
URL:https://blog.rwth-aachen.de/itc-events/en/event/ppces-2024/
LOCATION:Seminarraum 003/004\, IT Center\, Kopernikusstraße 6\, Aachen
CATEGORIES:HPC Events,PPCES
ATTACH;FMTTYPE=image/jpeg:https://blog.rwth-aachen.de/itc-events/files/2022/09/NHR4CES_RGB.jpg
ORGANIZER;CN="HPC":MAILTO:hpcevent@itc.rwth-aachen.de
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