COMPLECS: Batch Computing (Part II): Getting Started with Batch Job Scheduling
How to schedule your batch jobs on high-performance computing systems using the Slurm Workload Manager.
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How to schedule your batch jobs on high-performance computing systems using the Slurm Workload Manager.
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An overview of commonly used Linux tools for searching and manipulating text.
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An overview of commonly used Linux tools for searching and manipulating text.
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How to get the data you need for your research to and from high-performance computing systems.
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Interactive high-performance computing (HPC) involves real-time user inputs that result in actions being performed on HPC compute nodes. This session presents an overview of interactive computing tools and methods.
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SYCL is an open standard for programming heterogeneous architectures in ISO C++. This webinar will give a high-level overview of the SYCL programming language and the software ecosystem to write and tune SYCL code for different accelerator architectures. We will focus on GPUs and discuss how we can provide SYCL performance portability across hardware from different vendors, including Intel, Nvidia, and AMD GPUs, by employing a single-source model based on a modern C++ standard. As an example of complex scientific software, we will demonstrate briefly how the Amber molecular dynamics software was ported from CUDA to SYCL using Intel oneAPI software development tools and Intel Xe architecture GPUs. We will discuss numerical results and benchmark data that demonstrate the accuracy and performance of the SYCL implementation on data center and consumer-grade GPU hardware.
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Essentials of using regular expressions with the Linux tools grep, awk and sed.
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How to build and run your high-throughput and many-task computing workflows on high-performance computing systems using the Slurm Workload Manager.
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We will focus on (1) R package management and user environment configuration on the HPC cluster and (2) understanding the R parallelism in HPC, such as using the "parallel" package in R and a few related packages to parallelize and enhance the performance of R programs.
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