Merge remote-tracking branch 'origin/main'

.github/workflows/pages-html.yml

@@ -0,0 +1,25 @@
+name: Deploy HTML slides to Pages
+
+on:
+  # Runs on pushes targeting the default branch
+  push:
+    branches:
+      - "main"
+    paths:
+      - "docs/**"
+      - ".github/workflows/pages.yml"
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+jobs:
+  pages-html:
+    uses: ./.github/workflows/pages.yml
+    with:
+      include_pdf: false

.github/workflows/pages-pdf.yml

@@ -0,0 +1,17 @@
+name: Deploy HTML and PDF slides to Pages
+
+on:
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+jobs:
+  pages-pdf:
+    uses: ./.github/workflows/pages.yml
+    with:
+      include_pdf: true

.github/workflows/pages.yml

@@ -0,0 +1,62 @@
+# Script based on examples in https://github.com/actions/starter-workflows/tree/main/pages
+name: Deploy slides to Pages
+
+on:
+  workflow_call:
+    inputs:
+      include_pdf:
+        required: true
+        type: boolean
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
+# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  build:
+    timeout-minutes: 30
+    runs-on: ubuntu-latest
+    container:
+      image: ghcr.io/csc-training/slidefactory:3.1.0-beta.6
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Setup Pages
+        id: pages
+        uses: actions/configure-pages@v4
+      - name: Build slides
+        env:
+          INCLUDE_PDF: ${{ inputs.include_pdf }}
+        run: |
+          git config --global --add safe.directory $PWD
+          GIT_SHORT_SHA=$(git rev-parse --short $GITHUB_SHA)
+          GIT_DATE=$(git show -s --format=%ci $GITHUB_SHA)
+
+          ARGS=""
+          [[ "$INCLUDE_PDF" == "true" ]] && ARGS="--with-pdf"
+
+          slidefactory pages about.yml build --info_content "Updated for [$GIT_SHORT_SHA]($GITHUB_SERVER_URL/$GITHUB_REPOSITORY/commit/$GITHUB_SHA) ($GIT_DATE)" $ARGS
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: ./build
+
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    needs: build
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4

.gitignore

@@ -4,3 +4,4 @@ a.out
 *.qdrep
 *.sqlite
 heat
+build/

Exercises_Instructions.md

@@ -38,21 +38,21 @@ The Intel DevCloud can be acces via the [web interface](https://console.cloud.in
 
 ### Disk area
 
-The  (computing and storage)  resources can be accessed on on supercomputers via project-based allocation system, where users are granted access based on the specific needs and goals of their projects. Running applications and storage area are directly linked ot this projects. For this event we have been granted access to the training `project_2008874` on Mahti and `project_462000456` on LUMI.
+The  (computing and storage)  resources can be accessed on on supercomputers via project-based allocation system, where users are granted access based on the specific needs and goals of their projects. Running applications and storage area are directly linked ot this projects. For this event we have been granted access to the training `project_2012125` on Mahti and `project_462000456` on LUMI.
 
 All the exercises in the supercomputers have to be carried out in the **scratch** disk area. The name of the scratch directory can be queried with the commands `csc-workspaces` on Mahti and `lumi-workspaces` onLUMI. As the base directory is shared between members of the project, you should create your own
 directory:
 
 on Mahti
 ```
-cd /scratch/project_2008874
+cd /scratch/project_2012125
 mkdir -p $USER
 cd $USER
 ```
 
 on LUMI
 ```
-cd /scratch/project_462000456
+cd /scratch/project_462000752
 mkdir -p $USER
 cd $USER
 ```
@@ -122,19 +122,18 @@ In order to use the intel SYCL compiler one has to  set the environment varibles
 
 on Mahti:
 ```
-. /projappl/project_2008874/intel/oneapi/setvars.sh --include-intel-llvm
+. /projappl/project_2012125/intel/oneapi/setvars.sh --include-intel-llvm
 module load cuda # This is needed for compiling sycl code for nvidia gpus
 module load openmpi/4.1.2-cuda # This is neeeded for using CUDA aware MPI 
 ```
 
 on LUMI:
 ```
-. /projappl/project_462000456/intel/oneapi/setvars.sh --include-intel-llvm
+. /projappl/project_462000752/intel/oneapi/setvars.sh --include-intel-llvm
 
-module load LUMI/22.08
+module load LUMI
 module load partition/G
-module load rocm/5.3.3
-module load cce/16.0.1
+module load rocm/6.0.3
 export MPICH_GPU_SUPPORT_ENABLED=1 # Needed for GPU aware MPI
 ```
 After this one can load other modules that might be needed for compiling the codes. With the environment set-up we can compile and run the SYCL codes.
@@ -158,21 +157,20 @@ module load cuda # This is needed for compiling sycl code for nvidia gpus
 module load openmpi/4.1.2-cuda # This is neeeded for using CUDA aware MPI
 ```
 ```
-/projappl/project_2008874/AdaptiveCpp/bin/acpp -fuse-ld=lld -O3 -L/appl/spack/v017/install-tree/gcc-8.5.0/gcc-11.2.0-zshp2k/lib64 <sycl_code>.cpp
+/projappl/project_2012125/AdaptiveCpp/bin/acpp -fuse-ld=lld -O3 -L/appl/spack/v017/install-tree/gcc-8.5.0/gcc-11.2.0-zshp2k/lib64 <sycl_code>.cpp
 ```
 on LUMI:
 ```
-module load LUMI/22.08
+module load LUMI
 module load partition/G
-module load rocm/5.3.3
-module load cce/16.0.1
+module load rocm/6.0.3
 export MPICH_GPU_SUPPORT_ENABLED=1
-export LD_LIBRARY_PATH=/appl/lumi/SW/LUMI-22.08/G/EB/Boost/1.79.0-cpeCray-22.08/lib:$LD_LIBRARY_PATH
-export LD_PRELOAD=/pfs/lustrep4/appl/lumi/SW/LUMI-22.08/G/EB/rocm/5.3.3/llvm/lib/libomp.so
+#export LD_LIBRARY_PATH=/appl/lumi/SW/LUMI-22.08/G/EB/Boost/1.79.0-cpeCray-22.08/lib:$LD_LIBRARY_PATH ??? 
+#export LD_PRELOAD=/pfs/lustrep4/appl/lumi/SW/LUMI-22.08/G/EB/rocm/5.3.3/llvm/lib/libomp.so ??????
 ```
 
 ```
- /projappl/project_462000456/AdaptiveCpp/bin/acpp -O3 <sycl_code>.cpp
+ /projappl/project_462000752/AdaptiveCpp/bin/acpp -O3 <sycl_code>.cpp
 ```
 In general one can set specific targets via the `--acpp-targets` flag, but we set-up AdaptiveCpp so that on Mahti the `acpp` compiler will automatically generate code for CPU and Nvidia GPUs, while on LUMI for CPU and AMD GPUs.
 
@@ -198,17 +196,16 @@ icpx -fuse-ld=lld -fsycl -fsycl-targets=nvptx64-nvidia-cuda -Xsycl-target-backen
 ```
 or
 ```
-/projappl/project_2008874/AdaptiveCpp/bin/acpp -fuse-ld=lld -O3 -L/appl/spack/v017/install-tree/gcc-8.5.0/gcc-11.2.0-zshp2k/lib64 `mpicxx --showme:compile` `mpicxx --showme:link` <sycl_mpi_code>.cpp
+/projappl/project_2012125/AdaptiveCpp/bin/acpp -fuse-ld=lld -O3 -L/appl/spack/v017/install-tree/gcc-8.5.0/gcc-11.2.0-zshp2k/lib64 `mpicxx --showme:compile` `mpicxx --showme:link` <sycl_mpi_code>.cpp
 ```
 
 Similarly on LUMI. First we set up the envinronment and load the modules as indicated above
 ```
-. /projappl/project_462000456/intel/oneapi/setvars.sh --include-intel-llvm
+. /projappl/project_462000752/intel/oneapi/setvars.sh --include-intel-llvm
 
-module load LUMI/22.08
+module load LUMI
 module load partition/G
-module load rocm/5.3.3
-module load cce/16.0.1
+module load rocm/6.0.3
 export MPICH_GPU_SUPPORT_ENABLED=1
 ```
 Now compile with intel compilers:
@@ -218,8 +215,8 @@ icpx -fsycl -fsycl-targets=amdgcn-amd-amdhsa,spir64_x86_64 -Xsycl-target-backend
 ```
 Or with AdaptiveCpp:
 ```
-export LD_PRELOAD=/pfs/lustrep4/appl/lumi/SW/LUMI-22.08/G/EB/rocm/5.3.3/llvm/lib/libomp.so
-/projappl/project_462000456/AdaptiveCpp/bin/acpp -O3  `CC --cray-print-opts=cflags` <sycl_mpi_code>.cpp `CC --cray-print-opts=libs`
+#export LD_PRELOAD=/pfs/lustrep4/appl/lumi/SW/LUMI-22.08/G/EB/rocm/5.3.3/llvm/lib/libomp.so 
+/projappl/project_462000752/AdaptiveCpp/bin/acpp -O3  `CC --cray-print-opts=cflags` <sycl_mpi_code>.cpp `CC --cray-print-opts=libs`
 ```
 
 ## Running applications in supercomputers
@@ -231,9 +228,9 @@ The `job.sh` file contains all the necessary information (number of nodes, tasks
 
 ### Useful environment variables
 
-Use [`SYCL_PI_TRACE`](https://intel.github.io/llvm-docs/EnvironmentVariables.html#sycl-pi-trace-options) to enable runtime tracing (e.g. device discovery):
+Use [`SYCL_UR_TRACE`](https://intel.github.io/llvm-docs/EnvironmentVariables.html#sycl-pi-trace-options) to enable runtime tracing (e.g. device discovery):
 
-    export SYCL_PI_TRACE=1
+    export SYCL_UR_TRACE=1
 
 
 ### Running on Mahti
@@ -242,7 +239,7 @@ Use [`SYCL_PI_TRACE`](https://intel.github.io/llvm-docs/EnvironmentVariables.htm
 ```
 #!/bin/bash
 #SBATCH --job-name=example
-#SBATCH --account=project_2008874
+#SBATCH --account=project_2012125
 #SBATCH --partition=medium
 #SBATCH --reservation=hlgp-cpu-f2024
 #SBATCH --time=00:05:00
@@ -258,7 +255,7 @@ The output of job will be in file `slurm-xxxxx.out`. You can check the status of
 `scancel JOBID`.
 
 The reservation `hlgp-cpu-f2024` for partition `medium` is available during the training days and it
-is accessible only if the users are part of `project_2008874`.
+is accessible only if the users are part of `project_2012125`.
 
 Some applications use MPI, in this case the number of node and number of tasks per node will have to be adjusted accordingly.
 
@@ -270,9 +267,9 @@ single GPU with single MPI task and a single thread use:
 ```
 #!/bin/bash
 #SBATCH --job-name=example
-#SBATCH --account=project_2008874
+#SBATCH --account=project_2012125
 #SBATCH --partition=gpusmall
-#SBATCH --reservation=hlgp-gpu-f2024-thu
+#SBATCH --reservation=hlgp-gpu-f2024-thu ?????????
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
 #SBATCH --time=00:05:00
@@ -282,7 +279,7 @@ srun my_gpu_exe
 ```
 The reservation `hlgp-gpu-f2024-wed` is valid on Wednesday, 15:00 to 17:00. On Thursday we will use `hlgp-gpu-f2024-thu` , while on Friday `hlgp-gpu-f2024-fri`. Outside the course hours, you can use gputest partition instead without the reservation argument, ie, 
 ```
-srun --account=project_2008874 --nodes=1 --partition=gputest --gres=gpu:a100:1 --time=00:05:00 ./my_gpu_exe
+srun --account=project_2012125 --nodes=1 --partition=gputest --gres=gpu:a100:1 --time=00:05:00 ./my_gpu_exe
 ```
 
 
@@ -295,9 +292,9 @@ LUMI is similar to Mahti.
 ```
 #!/bin/bash
 #SBATCH --job-name=example
-#SBATCH --account=project_462000456
+#SBATCH --account=project_462000752
 #SBATCH --partition=standard
-##SBATCH --reservation=hlgp-cpu-f2024  # The reservation does not work 
+##SBATCH --reservation=hlgp-cpu-f2024  ??????# The reservation does not work 
 #SBATCH --time=00:05:00
 #SBATCH --nodes=1
 #SBATCH --ntasks-per-node=1
@@ -312,9 +309,9 @@ srun my_cpu_exe
 ```
 #!/bin/bash
 #SBATCH --job-name=example
-#SBATCH --account=project_462000456
+#SBATCH --account=project_462000752
 #SBATCH --partition=standard-g
-#SBATCH --reservation=hlgp-gpu-f2024
+#SBATCH --reservation=hlgp-gpu-f2024 ??????
 #SBATCH --time=00:05:00
 #SBATCH --nodes=1
 #SBATCH --ntasks-per-node=1