<\/p>\n
This one week online event will continue the tradition of previous annual week-long events<\/a> 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 as well as current topics in AI\/machine learning. Hands-on exercises for each topic will be included.<\/p>\n The contents of the courses are generally applicable but will be specialized towards CLAIX the compute cluster which is the current system installed at the RWTH’s IT Center. It might be helpful to read through the information which was provided during the HPC introduction<\/a> on March 12 this year. This is especially true if you want to actively use CLAIX after this event.<\/span><\/span><\/p>\n OpenMP<\/strong> 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.<\/p>\n The Message Passing Interface (MPI)<\/strong> 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.<\/p>\n Machine Learning<\/strong>: We provide an overview to end-to-end deep learning with the latest version\u00a0of Tensorflow\/Keras. It covers the basic concepts to define models with Keras \u00a0and data pipelines with Tensorflow’s “Dataset”, and to visualize the results\u00a0with Tensorboard while training. If training on one node or GPU is not enough, \u00a0we show how to scale up\/out distributed training onto multiple compute nodes \u00a0and GPUs with Horovod.\u00a0Furthermore, we provide an introduction to scikit-learn, with an overview of \u00a0different machine learning algorithms it provides and how to utilize it on GPUs \u00a0with H2O4GPU.\u00a0The training courses consist of a hands-on exercises to be run directly on \u00a0RWTH infrastructure.<\/p>\n We are very happy to present two guest speakers:<\/p>\n Monday, March 22<\/span><\/p>\nGuest Speakers<\/h3>\n
\n
Agenda<\/h3>\n
Day 1+2: OpenMP<\/span>\u00a0<\/span><\/h4>\n