updated the structure and readme files, added a deployment guide

Deployment_Guide.md

@@ -0,0 +1,72 @@
+# N-ways to GPU programming Deployment Guide
+The N-Ways to GPU Programming Bootcamp covers the basics of GPU programming and provides an overview of different methods for porting scientific application to GPUs using NVIDIA® CUDA®, OpenACC, standard languages, OpenMP offloading, and/or CuPy and Numba. Throughout the bootcamp, attendees with learn how to analyze GPU-enabled applications using NVIDIA Nsight™ Systems and participate in hands-on activities to apply these learned skills to real-world problems.
+
+## Deploying the materials
+
+### Prerequisites
+To run this tutorial, you will need a machine with NVIDIA GPU.
+
+- Install the latest [Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) or [Singularity](https://sylabs.io/docs/).
+
+- Install the latest [NVIDIA Nsight™ Systems](https://developer.nvidia.com/nsight-systems).
+
+- The base containers required for the lab may require users to create an NGC account and generate an API key (https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#registering-activating-ngc-account)
+
+The material is also tested to be working with NVIDIA V100 and T4 GPUs, please contact us if you require assistance in deploying the content.
+
+
+### Tested environment
+
+These materials was tested with both Docker and Singularity on an NVIDIA A100 GPU in an x86-64 platform installed with a driver version of `525.105.17`. 
+
+### Deploying with container 
+
+These materials can be deployed with either Docker or Singularity container, refer to the respective sections for the instructions.
+
+#### Docker Container
+
+To build a docker container, specify the dockerfile name using `-f` flag: 
+`sudo docker build -f <dockerfile name> -t <imagename>:<tagnumber> .`
+
+For instance:
+
+- To build the docker container, for N-Ways to GPU Programming-Python, follow the below steps: 
+
+  1. `sudo docker build -f nways_Dockerfile_python -t openhackathons:nways_python .`
+  2. `sudo docker run --rm -it --gpus=all -p 8888:8888 openhackathons:nways_python`
+  3. To access the labs, run: `jupyter-lab --ip 0.0.0.0 --port 8888 --no-browser --allow-root`
+  4. Now, open the jupyter lab in browser: http://localhost:8888, and start working on the lab by clicking on the `_start_nways.ipynb` notebook
+
+
+- To build the singularity container, for N-Ways to GPU Programming-C-Fortran, follow the below steps:  
+
+  1. `sudo docker build -f nways_Dockerfile -t openhackathons:nways_CFortran .`
+  2. `sudo docker run --rm -it --gpus=all -p 8888:8888 openhackathons:nways_CFortran`
+  3. To access the labs, run: `jupyter-lab --ip 0.0.0.0 --port 8888 --no-browser --allow-root`
+  4. Now, open the jupyter lab in browser: http://localhost:8888, and start working on the lab by clicking on the `_start_nways.ipynb` notebook
+
+Please note, if you are to run both contents, you would need to change the ports to access them seperately.
+
+#### Singularity Container
+
+- To build the singularity container, for N-Ways to GPU Programming-Python, follow the below steps: 
+
+  1. `singularity build --fakeroot nways_python.simg nways_Singularity_python`
+  2. `singularity run nways_python.simg cp -rT /labs ~/labs`
+  3. `singularity run --nv nways_python.simg jupyter-lab --notebook-dir=~/labs`
+  4. Now, open the jupyter lab in browser: http://localhost:8888, and start working on the lab by clicking on the `_start_nways.ipynb` notebook
+
+
+- To build the singularity container, for N-Ways to GPU Programming-C-Fortran, follow the below steps:   
+
+  1. `singularity build --fakeroot nways_CFortran.simg nways_Singularity`
+  2. `singularity run nways_CFortran.simg cp -rT /labs ~/labs`
+  3. `singularity run --nv nways_CFortran.simg jupyter-lab --notebook-dir=~/labs`
+  4. Now, open the jupyter lab in browser: http://localhost:8888, and start working on the lab by clicking on the `_start_nways.ipynb` notebook
+
+### Known issues
+
+- Please go through the list of exisiting bugs/issues or file a new issue at [Github](https://github.com/openhackathons-org/nways_accelerated_programming/issues).
+
+
+

README.md

@@ -1,39 +1,50 @@
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) 
 
-# HPC_Bootcamp
+# N-ways to GPU programming
+The N-Ways to GPU Programming Bootcamp covers the basics of GPU programming and provides an overview of different methods for porting scientific application to GPUs using NVIDIA® CUDA®, OpenACC, standard languages, OpenMP offloading, and/or CuPy and Numba. Throughout the bootcamp, attendees with learn how to analyze GPU-enabled applications using NVIDIA Nsight™ Systems and participate in hands-on activities to apply these learned skills to real-world problems.
 
-This repository contains training content for the HPC_Bootcamp materials. This repository includes the following file structure in the initial two levels:
+## Bootcamp contents:
 
-```
-│   ├── 
-├── _basic
-│   ├── cuda
-│   ├── iso
-│   ├── openacc
-│   ├── openmp
-│   └── python
-├── LICENSE
-├── README.md
-├── _scripts
-└── start_notebook
-```
+The content is structured in multiple options covering the following: 
 
-- The __basic_ directory contains all of the introductory training materials for CUDA, Standard Languages, OpenMP Offloading, and OpenACC.
-- The __scripts_ directory contains container definition files for each bootcamp type.
-- The __start_notebook_ directory contains started notebooks and it is optional to use.
+- Option 1: N-Ways to GPU Programming-C-Fortran
+  - NVIDIA® Nsight™ Systems
+  - NVIDIA® Nsight™ Compute (Optional, lecture only)
+  - Lab 1: ISO C++ and ISO Fortran
+  - Lab 2: OpenACC
+  - Lab 3: OpenMP offloading
+  - Lab 4: CUDA
 
-<!--Please note there is a container definition file for each content in `_advanced` and `_basic` directories that can be found inside each folder.-->
+- Option 2: N-Ways to GPU Programming-Python
+  - NVIDIA® Nsight™ Systems
+  - NVIDIA® Nsight™ Compute (Optional, lecture only)
+  - Lab 1: CuPy
+  - Lab 2: Numba
 
-### Building the container using the definition files inside the `_script` folder
+## Tools and frameworks:
 
-To build the singularity container, run: 
-`sudo singularity build miniapp.simg {Name of the content}_Singularity` , alternatively you can use `singularity build --fakeroot miniapp.simg {Name of the content}_Singularity` if you do not have `sudo` rights.
+The tools and frameworks used in the bootcamp are as follows:
+- [NVIDIA HPC SDK](https://developer.nvidia.com/hpc-sdk)
+- [NVIDIA Nsight™ Systems](https://developer.nvidia.com/nsight-systems)
 
-Next, copy the files to a local directory to make sure changes are stored locally:
-`singularity run miniapp.simg cp -rT /labs ~/labs`
+## Bootcamp duration:
 
-Then, run the container:
-`singularity run --nv miniapp.simg jupyter-lab --notebook-dir=~/labs`
+The N-Ways to GPU Programming-C-Fortran Bootcamp will take approximately 6 hours and the N-Ways to GPU Programming-Python Bootcamp will take approximately 4.5 hours.
 
-Once inside the container, open the jupyter lab in browser: http://localhost:8888, and start the lab by clicking on the `_start_{Name of the content}.ipynb` notebook. 
+## Bootcamp prerequisites:
 
+Basic experience with C/C++ or Fortran is needed for the "N-Ways to GPU Programming-C-Fortran" Bootcamp and Python is needed for the "N-Ways to GPU Programming-Python" Bootcamp. No GPU programming experience is required.
+
+## Deploying the Bootcamp materials:
+
+For deploying the materials, please refer to the Deployment guide present [here](Deployment_Guide.md)
+
+## Attribution
+
+This material originates from the OpenHackathons Github repository. Check out additional materials [here](https://github.com/openhackathons-org).
+
+Don't forget to check out additional [Open Hackathons Resources](https://www.openhackathons.org/s/technical-resources) and join our [OpenACC and Hackathons Slack Channel](https://www.openacc.org/community#slack) to share your experience and get more help from the community.
+
+## Licensing
+
+Copyright © 2023 OpenACC-Standard.org. This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0). These materials may include references to hardware and software developed by other entities; all applicable licensing and copyrights apply.

Repo_Structure.md

@@ -0,0 +1,45 @@
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) 
+
+# HPC_Bootcamp
+
+This repository contains training content for the HPC_Bootcamp materials. This repository includes the following file structure in the initial two levels:
+
+```
+│   ├── 
+├── _basic
+│   ├── cuda
+│   ├── iso
+│   ├── openacc
+│   ├── openmp
+│   └── python
+├── LICENSE
+├── README.md
+├── nways_Dockerfile 
+├── nways_Dockerfile_python  
+├── nways_Singularity
+├── nways_Singularity_python
+├── CONTRIBUTING.md
+├── Deployment_Guide.md 
+├── _scripts
+└── start_notebook
+```
+
+- The __basic_ directory contains all of the introductory training materials for CUDA, Standard Languages, OpenMP Offloading, and OpenACC.
+- The __scripts_ directory contains container definition files for each bootcamp type. This is not needed at the moment. Please refer to the [Deployment_Guide](Deployment_Guide.md).
+- The __start_notebook_ directory contains started notebooks and it is optional to use. This is not needed at the moment.
+
+<!--Please note there is a container definition file for each content in `_advanced` and `_basic` directories that can be found inside each folder.-->
+
+### Building the container using the definition files inside the `_script` folder
+
+To build the singularity container, run: 
+`sudo singularity build miniapp.simg {Name of the content}_Singularity` , alternatively you can use `singularity build --fakeroot miniapp.simg {Name of the content}_Singularity` if you do not have `sudo` rights.
+
+Next, copy the files to a local directory to make sure changes are stored locally:
+`singularity run miniapp.simg cp -rT /labs ~/labs`
+
+Then, run the container:
+`singularity run --nv miniapp.simg jupyter-lab --notebook-dir=~/labs`
+
+Once inside the container, open the jupyter lab in browser: http://localhost:8888, and start the lab by clicking on the `_start_{Name of the content}.ipynb` notebook. 
+

_basic/nways_Dockerfile → nways_Dockerfile

@@ -22,12 +22,12 @@ RUN pip3 install ipywidgets
 RUN apt-get update -y
 
 # TO COPY the data
-COPY openacc/ /labs/openacc
-COPY openmp/ /labs/openmp
-COPY _common/ /labs/_common
-COPY iso/ /labs/iso
-COPY cuda/ /labs/cuda
-COPY _start_nways.ipynb /labs
+COPY _basic/openacc/ /labs/openacc
+COPY _basic/openmp/ /labs/openmp
+COPY _basic/_common/ /labs/_common
+COPY _basic/iso/ /labs/iso
+COPY _basic/cuda/ /labs/cuda
+COPY _basic/_start_nways.ipynb /labs
 
 RUN python3 /labs/_common/dataset.py
 

_basic/nways_Dockerfile_python → nways_Dockerfile_python

@@ -48,9 +48,9 @@ RUN apt-get update -y && \
 RUN DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends nsight-systems-cli-2022.1.1 nsight-compute-2022.1.1
 
 # TO COPY the data
-COPY python/ /labs/python
-COPY _common/ /labs/_common
-COPY _start_nways.ipynb /labs
+COPY _basic/python/ /labs/python
+COPY _basic/_common/ /labs/_common
+COPY _basic/_start_nways.ipynb /labs
 
 RUN python3 /labs/_common/dataset_python.py
 

_basic/nways_Singularity → nways_Singularity

@@ -46,12 +46,12 @@ FROM: nvcr.io/nvidia/nvhpc:23.5-devel-cuda_multi-ubuntu20.04
     rm -rf ${build_tmp}
 
 %files
-    ../_basic/openacc/ /labs/openacc
-    ../_basic/openmp/ /labs/openmp
-    ../_basic/_common/ /labs/_common
-    ../_basic/iso/ /labs/iso
-    ../_basic/cuda/ /labs/cuda
-    ../_basic/_start_nways.ipynb /labs
+    _basic/openacc/ /labs/openacc
+    _basic/openmp/ /labs/openmp
+    _basic/_common/ /labs/_common
+    _basic/iso/ /labs/iso
+    _basic/cuda/ /labs/cuda
+    _basic/_start_nways.ipynb /labs
 
 %runscript
     "$@"

_basic/nways_Singularity_python → nways_Singularity_python

@@ -75,9 +75,9 @@ FROM: nvidia/cuda:11.4.2-devel-ubuntu20.04
     rm -rf ${build_tmp}
 
 %files
-    ../_basic/python/ /labs/python
-    ../_basic/_common/ /labs/_common
-    ../_basic/_start_nways.ipynb /labs
+    _basic/python/ /labs/python
+    _basic/_common/ /labs/_common
+    _basic/_start_nways.ipynb /labs
 
 %runscript
     "$@"