Gain insights into using HPC systems and supercomputers to solve large computational problems. Explore HPC components, software stacks, job schedulers, and parallel programming, including Open MP, MPI, and GPU coding.

Learn to use High Performance Computing (HPC) Systems  and solve large computational problems.  This course assumes basic familiarity with the Bash command line environment found on GNU/Linux and other Unix-like environments. This course is of the first of its kind, should be your second step from my previous Educative course "Learn to Analyze Text Data in Bash Shell and Linux"

You'll learn:
- Intro to HPC Systems and Supercomputers
- HPC system's basic components
- HPC software stack
- HPC job schedulers and batch systems (PBS and Slurm with demos)
- Introduction to parallel programming (Open MP, MPI and GPU coding)

Video demos are included, I think it is a must have course for students, researchers, big data analysts, and developers. It will help you to utilize High Performance Computing systems at your institution. Non-interactive course. More details at:  www.scientificprogramming.io/learn-hpc

Ajay Shenoy

This course demystifies HPC and introduces you to the essential tools required to schedule and run parallel programs. Easy to understand.

India

Learn to Use HPC Systems and Supercomputers

The table below summarizes which **clauses** are accepted by which OpenMP **directives**.

|**Clause**|**Description**|
|---|---|
|`copyin`|Allows threads to access the master thread's value, for a threadprivate variable.|
|`copyprivate`|Specifies that one or more variables should be shared among all threads.|
|`default`|Specifies the behavior of unscoped variables in a parallel region.|
|`firstprivate`|Specifies that each thread should have its own instance of a variable, and that the variable should be initialized with the value of the variable, because it exists before the parallel construct.|
|`if`|Specifies whether a loop should be executed in parallel or in serial.|
|`lastprivate`|Specifies that the enclosing context's version of the variable is set equal to the private version of whichever thread executes the final iteration (`for-loop` construct) or last section (`#pragma` sections).|
|`nowait`|Overrides the barrier implicit in a directive.|
|`num_threads`|Sets the number of threads in a thread team.|
|`ordered`|Required on a parallel `for-loop` statement if an **ordered** directive is to be used in the **loop**.|
|`private`|Specifies that each thread should have its own instance of a variable.|
|`reduction`|Specifies that one or more variables that are private to each thread are the subject of a reduction operation at the end of the parallel region.|
|`schedule`|Applies to the `for` directive.|
|`shared`|Specifies that one or more variables should be shared among all threads.|

Some examples next:

Supercomputers and HPC clusters

Components of a HPC system

HPC software stack

PBS - Portable Batch System

SLURM -Workload Manager

Parallel programming - OpenMP

Parallel programming - MPI

Parallel programming - GPU and CUDA

Conclusions and references

Open MP - clauses