Proběhlo

Summary and benefits for the attendees

Numerical simulations conducted on current high-performance computing (HPC) systems face an ever growing need for scalability. Larger HPC platforms provide opportunities to push the limitations on size and properties of what can be accurately simulated. Serial approaches on handling I/O in a parallel application will dominate the performance on massively parallel systems. Heterogeneity of platforms can impose a high level of maintenance, when different data representations are needed.

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The current wave of advances in Deep Learning (DL) has led to many exciting challenges and opportunities for Computer Science and Artificial Intelligence researchers alike. Modern DL frameworks like Caffe/Caffe2, TensorFlow, Cognitive Toolkit, Torch, and several others have emerged that offer ease of use and flexibility to describe, train, and deploy various types of Deep Neural Networks (DNNs) including deep convolutional networks.

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InfiniBand (IB), Omni-Path, and High-speed Ethernet (HSE) technologies are generating a lot of excitement toward building next generation High-End Computing (HEC) systems including clusters, datacenters, file systems, storage, cloud computing and Big Data (Hadoop, Spark, HBase, and Memcached) environments. RDMA over Converged Enhanced Ethernet (RoCE) technology is also emerging.

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This HPC training aims to expand skills and knowledge in using productivity tools and technologies, to quickly build up an efficient HPC user environment, from scratch, without admin rights. We shall demonstrate tools and procedures tailored to the Salomon or Anselm clusters, however they are rapidly replicable on any HPC system.

In particular, our “Swiss knife” course will cover the following tools: