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 **Message Passing Interface** (MPI) is a standardized and portable message-passing system that defines the syntax and semantics of a core of library routines useful to a wide range of users writing portable message-passing programs in `C`, `C++`, and `Fortran`. 

# History

Before the 1990's, writing parallel applications for different computing architectures was a difficult and tedious task. At that time, many libraries could facilitate building parallel applications, but there was not a standard accepted way of doing it. A small group of researchers started discussions in Austria and came out with a Workshop on 'standards' for **Message Passing in a Distributed Memory Environment**. Attendees  discussed the basic features essential to a standard message-passing interface and established a working group to continue the standardization process and put forward a preliminary draft proposal, **"MPI1"**, in November 1992.  After its first implementations were created, MPI was widely adopted and still continues to be the de-facto method of writing **message-passing** applications.

**MPI Chronology**:

+ 1994: MPI-1 (specification, not strictly a library)
+ 1996: MPI-2 (addresses some extensions)
+ 2012: MPI-3 (extensions, remove `C++` bindings)

# Programming Model

+    Distributed programming model. Also data parallel

 +   Hardware platforms: **distributed, shared and hybrid**

+    Parallelism is explicit. The programmer is responsible for implementing all parallel constructs.

The number of tasks dedicated to run a parallel program is static. New tasks can not be dynamically spawned during run time. However, MPI-2 addressed this issue.

# Benefits of MPI


+    **Portability:** There is no need to modify your source code when you port your application to a different platform that supports (and is compliant with) the MPI standard.

+   **Standardization:** MPI is the only message passing library which can be considered a standard. It is supported on virtually all HPC platforms. Practically, it has replaced all previous message passing libraries.

+    **Functionality:** Over 115 routines are defined in the MPI-1 alone.

+    **Availability:** A variety of implementations are available, both vendor and public domain (see below).

# How to get the MPI?

Your institutions HPC cluster should have some kinds of MPI installed already, which may be (but not limited to):

+ Open MPI
+ Intel MPI
+ MPICH2
+ SGI's MPT
+ and so on.

You need to load the appropriate module (e.g., `module load openmpi`) prior to running code containing MPI stuffs. If your load was successful, you should be able to type `mpiexec --version` and see something similar to this:




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

MPI - Message Passing Interface