Correct links to hpc.uni.lu (first batch)

hcartiaux · hcartiaux · commit 4140bfa52647 · 2021-11-17T09:37:06.000+01:00
diff --git a/advanced/TotalView/README.md b/advanced/TotalView/README.md
@@ -30,7 +30,7 @@ This practical session will be organized as follows:
 * Asynchronous control of Parallel Applications (TP)
 * Type Transformation (TP)
 
-While TotalView is available on the [UL HPC Platform](http://hpc.uni.lu), the specific exercises proposed have been embedded into a Virtual Machine (VM) you will need to setup to run this practical session.
+While TotalView is available on the [UL HPC Platform](https://hpc.uni.lu), the specific exercises proposed have been embedded into a Virtual Machine (VM) you will need to setup to run this practical session.
 
 ## Pre-Requisites
 
diff --git a/beginners/README.md b/beginners/README.md
@@ -8,7 +8,7 @@
 
 
 This tutorial will guide you through your first steps on the
-[UL HPC platform](http://hpc.uni.lu).
+[UL HPC platform](https://hpc.uni.lu).
 
 Before proceeding:
 
diff --git a/bigdata/README.md b/bigdata/README.md
@@ -7,7 +7,7 @@
 
 [![](https://github.com/ULHPC/tutorials/raw/devel/bigdata/cover_slides.png)](https://github.com/ULHPC/tutorials/raw/devel/bigdata/slides.pdf)
 
-The objective of this tutorial is to demonstrate how to build and run on top of the [UL HPC](http://hpc.uni.lu) platform a couple of reference analytics engine for large-scale Big Data processing, _i.e._ [Hadoop](http://hadoop.apache.org/) or  [Apache Spark](http://spark.apache.org/).
+The objective of this tutorial is to demonstrate how to build and run on top of the [UL HPC](https://hpc.uni.lu) platform a couple of reference analytics engine for large-scale Big Data processing, _i.e._ [Hadoop](http://hadoop.apache.org/) or  [Apache Spark](http://spark.apache.org/).
 
 
 --------------------
@@ -268,7 +268,7 @@ Once this is done, Repeat the execution of the [official Wordcount example](http
 -----------------------------------------------
 ## Interactive Big Data Analytics with Spark ##
 
-The objective of this section is to compile and run on [Apache Spark](http://spark.apache.org/)  on top of the [UL HPC](http://hpc.uni.lu) platform.
+The objective of this section is to compile and run on [Apache Spark](http://spark.apache.org/)  on top of the [UL HPC](https://hpc.uni.lu) platform.
 
 [Apache Spark](http://spark.apache.org/docs/latest/) is a large-scale data processing engine that performs in-memory computing. Spark offers bindings in Java, Scala, Python and R for building parallel applications.
 high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.
diff --git a/bio/basics/README.md b/bio/basics/README.md
@@ -8,7 +8,7 @@ Authors: Valentin Plugaru and Sarah Peter
 
 [![](https://github.com/ULHPC/tutorials/raw/devel/bio/basics/cover_slides.png)](https://github.com/ULHPC/tutorials/raw/devel/bio/basics/slides.pdf)
 
-The objective of this tutorial is to exemplify the execution of several Bioinformatics packages on top of the [UL HPC](http://hpc.uni.lu) platform.
+The objective of this tutorial is to exemplify the execution of several Bioinformatics packages on top of the [UL HPC](https://hpc.uni.lu) platform.
 
 The targeted applications are:
 
diff --git a/deep_learning/basics/README.md b/deep_learning/basics/README.md
@@ -7,7 +7,7 @@
 
 [![](cover_slides.png)](slides.pdf)
 
-This tutorial demonstrates how to develop and run on the [UL HPC](http://hpc.uni.lu) platform a deep learning application using the Tensorflow framework.
+This tutorial demonstrates how to develop and run on the [UL HPC](https://hpc.uni.lu) platform a deep learning application using the Tensorflow framework.
 
 The scope of this tutorial is *single* node execution, multi-CPU and multi-GPU.
 Another tutorial covers multi-node execution.
diff --git a/docs/README.md b/docs/README.md
@@ -12,7 +12,7 @@ In particular, the latest version of these tutorials is available online:
 <http://ulhpc-tutorials.rtfd.io>
 
 The list of proposed tutorials is continuously evolving.
-They used on a regular basis during the [UL HPC School](http://hpc.uni.lu/hpc-school/) we organise.
+They used on a regular basis during the [UL HPC School](https://hpc.uni.lu/education/hpcschool) we organise.
 So far, the following tutorials are proposed:
 
 | **Category**            | **Description**                                                                                       | **Level**      |
diff --git a/docs/contacts.md b/docs/contacts.md
@@ -1,6 +1,6 @@
-These tutorials has been designed and implemented in the context of the [UL HPC](http://hpc.uni.lu) Platform of the [University of Luxembourg](http://www.uni.lu) by the [UL HPC Team](https://hpc.uni.lu/about/team.html#system-administrators), _i.e._
+These tutorials has been designed and implemented in the context of the [UL HPC](https://hpc.uni.lu) Platform of the [University of Luxembourg](http://www.uni.lu) by the [UL HPC Team](https://hpc.uni.lu/about/team), _i.e._
 
-* Dr. [Sébastien Varrette](https://varrette.gforge.uni.lu/), [UL HPC](http://hpc.uni.lu) Manager
+* Dr. [Sébastien Varrette](https://varrette.gforge.uni.lu/), [UL HPC](https://hpc.uni.lu) Manager
 * Dr. [Frederic Pinel](https://wwwen.uni.lu/recherche/fstc/computer_science_and_communications_research_unit/members/frederic_pinel_2)
 * Dr. [Emmanuel Kieffer](https://wwwen.uni.lu/recherche/fstc/computer_science_and_communications_research_unit/members/emmanuel_kieffer)
 * Dr. [Ezhilmathi Krishnasamy](https://wwwen.uni.lu/snt/people/ezhilmathi_krishnasamy)
diff --git a/docs/hpc-school.md b/docs/hpc-school.md
@@ -1,4 +1,4 @@
-[![](https://hpc.uni.lu/images/logo/logo_hpc-shool2020.png)](https://hpc.uni.lu/hpc-school/)
+[![](https://hpc.uni.lu/images/logo/logo_hpc-shool2021.png)](https://hpc.uni.lu/education/hpcschool/)
 
 * [Latest UL HPC School](https://hpc.uni.lu/education/hpcschool#program)
 
@@ -25,4 +25,4 @@ The practical sessions of the Nov 2021 HPC School are listed below
 | 11 | Introduction to GPU programming with OpenACC and OpenCL                           | L. Koutsantonis, E. Ezhilmathi, T. Pessoa| 1h45     |
 | 12 | R - Statistical computing                                                         | A. Ginolac           | 2h30     |
 | 13 | Data management (backup, security)                                                | S. Peter             | 1h00     |
-| 14 | Big Data Analytics: Batch, Stream and Hybrid processing engines                   | S. Varrette          | 55min    |
+| 14 | Big Data Analytics: Batch, Stream and Hybrid processing engines                   | S. Varrette          | 55min    |
diff --git a/maths/R/README.Rmd b/maths/R/README.Rmd
@@ -24,7 +24,7 @@ library(tidyverse)
   <script>fitvids('.shareagain', {players: 'iframe'});</script>
 </div>
 
-Through this tutorial you will learn how to use R from your local machine or from one of the [UL HPC platform](http://hpc.uni.lu) clusters.
+Through this tutorial you will learn how to use R from your local machine or from one of the [UL HPC platform](https://hpc.uni.lu) clusters.
 Then, we will see how to organize and group data. Finally we will illustrate how R can benefit from multicore and cluster parallelization.
 
 Warning: this tutorial does not focus on the learning of R language but aims at showing you nice start-up tips.
diff --git a/maths/R/README.md b/maths/R/README.md
@@ -29,7 +29,7 @@ forks](https://img.shields.io/github/stars/ULHPC/tutorials.svg?style=social&labe
 </div>
 
 Through this tutorial you will learn how to use R from your local
-machine or from one of the [UL HPC platform](http://hpc.uni.lu)
+machine or from one of the [UL HPC platform](https://hpc.uni.lu)
 clusters. Then, we will see how to organize and group data. Finally we
 will illustrate how R can benefit from multicore and cluster
 parallelization.
diff --git a/maths/matlab/basics/README.md b/maths/matlab/basics/README.md
@@ -8,7 +8,7 @@
 
 The objective of this tutorial is to exemplify the execution of [MATLAB](http://www.matlab.com) -
 a high-level language and interactive environment for numerical computation,
-visualization and programming, on top of the [UL HPC](http://hpc.uni.lu) platform.
+visualization and programming, on top of the [UL HPC](https://hpc.uni.lu) platform.
 
 The tutorial will show you:
 
@@ -19,7 +19,7 @@ The tutorial will show you:
 5. how to plot data, saving the plots to file
 6. how to take advantage of some of the paralelization capabilities of MATLAB to speed up your tasks
 
-For the tutorial we will use the UL HPC [Iris](http://hpc.uni.lu/systems/iris/) cluster that includes nodes with GPU accelerators.
+For the tutorial we will use the UL HPC [Iris](https://hpc.uni.lu/systems/iris/) cluster that includes nodes with GPU accelerators.
 
 ## Prerequisites
 
diff --git a/multiphysics/README.md b/multiphysics/README.md
@@ -6,7 +6,7 @@
 
 [![](https://github.com/ULHPC/tutorials/raw/devel/multiphysics/cover_slides.png)](https://github.com/ULHPC/tutorials/raw/devel/multiphysics/slides.pdf)
 
-The objective of this session is to exemplify the execution of several common, parallel, Computational Physics, Chemistry & Engineering software on the [UL HPC](http://hpc.uni.lu) platform.
+The objective of this session is to exemplify the execution of several common, parallel, Computational Physics, Chemistry & Engineering software on the [UL HPC](https://hpc.uni.lu) platform.
 
 Targeted applications include:
 
diff --git a/parallel/basics/README.md b/parallel/basics/README.md
@@ -330,7 +330,7 @@ In most MPI implementations,   a fixed set of processes is created at program in
 
 ### MPI implementations
 
-The [UL HPC platform](http://hpc.uni.lu) offers to you different MPI implementations:
+The [UL HPC platform](https://hpc.uni.lu) offers to you different MPI implementations:
 
 | MPI Suit                                                        | Version | `module load`...  | Compiler                    |
 |-----------------------------------------------------------------|---------|-------------------|-----------------------------|
diff --git a/parallel/hybrid/HPCG/README.md b/parallel/hybrid/HPCG/README.md
@@ -10,7 +10,7 @@ Copyright (c) 2015-2017 UL HPC Team  <hpc-sysadmins@uni.lu>
 
 
 The objective of this tutorial is to compile and run one of the newest HPC benchmarks, [High Performance Conjugate Gradients (HPCG)](http://www.hpcg-benchmark.org/), on top of the
-[UL HPC](http://hpc.uni.lu) platform.
+[UL HPC](https://hpc.uni.lu) platform.
 
 You can work in groups for this training, yet individual work is encouraged to ensure you understand and practice the usage of MPI programs on an HPC platform.
 If not yet done, you should consider completing the [OSU Micro-benchmark](../OSU_MicroBenchmarks/) and [HPL](../HPL/) tutorials.
diff --git a/parallel/mpi/HPL/README.md b/parallel/mpi/HPL/README.md
@@ -4,7 +4,7 @@
 
      Copyright (c) 2013-2019 UL HPC Team  <hpc-sysadmins@uni.lu>
 
-The objective of this tutorial is to compile and run on of the reference HPC benchmarks, [HPL](http://www.netlib.org/benchmark/hpl/), on top of the [UL HPC](http://hpc.uni.lu) platform.
+The objective of this tutorial is to compile and run on of the reference HPC benchmarks, [HPL](http://www.netlib.org/benchmark/hpl/), on top of the [UL HPC](https://hpc.uni.lu) platform.
 
 You can work in groups for this training, yet individual work is encouraged to ensure you understand and practice the usage of MPI programs on an HPC platform.
 If not yet done, you should consider completing the following tutorials:
@@ -96,7 +96,7 @@ HPL rely on an efficient implementation of the Basic Linear Algebra Subprograms
 * [ATLAS](http://math-atlas.sourceforge.net/atlas_install/)
 * [GotoBlas](https://www.tacc.utexas.edu/research-development/tacc-software/gotoblas2/)
 
-The idea is to compare the different MPI and BLAS implementations available on the [UL HPC platform](http://hpc.uni.lu):
+The idea is to compare the different MPI and BLAS implementations available on the [UL HPC platform](https://hpc.uni.lu):
 
 * [Intel MPI](http://software.intel.com/en-us/intel-mpi-library/) and the Intel MKL
 * [OpenMPI](http://www.open-mpi.org/)
diff --git a/parallel/mpi/OSU_MicroBenchmarks/README.md b/parallel/mpi/OSU_MicroBenchmarks/README.md
@@ -37,7 +37,7 @@ In this tutorial, we will build **version 5.5 of the [OSU micro-benchmarks](http
 
 > The bandwidth tests were carried out by having the sender sending out a fixed number (equal to the window size) of back-to-back messages to the receiver and then waiting for a reply from the receiver. The receiver sends the reply only after receiving all these messages. This process is repeated for several iterations and the bandwidth is calculated based on the elapsed time (from the time sender sends the first message until the time it receives the reply back from the receiver) and the number of bytes sent by the sender. The objective of this bandwidth test is to determine the maximum sustained date rate that can be achieved at the network level. Thus, non-blocking version of MPI functions (MPI_Isend and MPI_Irecv) were used in the test.
 
-The idea is to compare the different MPI implementations available on the [UL HPC platform](http://hpc.uni.lu).:
+The idea is to compare the different MPI implementations available on the [UL HPC platform](https://hpc.uni.lu).:
 
 * [Intel MPI](http://software.intel.com/en-us/intel-mpi-library/)
 * [OpenMPI](http://www.open-mpi.org/)
diff --git a/python/advanced/celery/README.md b/python/advanced/celery/README.md
@@ -2,7 +2,7 @@
 
 Authors: Clément Parisot
 
-Copyright (c) 2018 UL HPC Team  -- see <http://hpc.uni.lu>
+Copyright (c) 2018 UL HPC Team  -- see <https://hpc.uni.lu>
 
 ---------------------------------------------------------
 # UL HPC Tutorial: [Advanced] Python : Use Jupyter notebook on UL HPC
diff --git a/tools/cli/modules.md b/tools/cli/modules.md
@@ -6,7 +6,7 @@
 
 This page is part of the Getting started tutorial.
 
-[Environment Modules](http://modules.sourceforge.net/) is a software package that allows us to provide a [multitude of applications and libraries in multiple versions](http://hpc.uni.lu/users/software/) on the UL HPC platform. The tool itself is used to manage environment variables such as `PATH`, `LD_LIBRARY_PATH` and `MANPATH`, enabling the easy loading and unloading of application/library profiles and their dependencies.
+[Environment Modules](http://modules.sourceforge.net/) is a software package that allows us to provide a [multitude of applications and libraries in multiple versions](https://hpc.uni.lu/users/software/) on the UL HPC platform. The tool itself is used to manage environment variables such as `PATH`, `LD_LIBRARY_PATH` and `MANPATH`, enabling the easy loading and unloading of application/library profiles and their dependencies.
 
 We will have multiple occasion to use modules in the other tutorials so there is nothing special we foresee here. You are just encouraged to read the following resources:
 
