Exercise repository for the Numerical Linear Algebra for HPC course, Winter 2020.

This repository provides a framework (with building and testing) for you to write your code, allowing you to concentrate on the kernel implementation and the performance benchmarking.

Build instructions

We use cmake to build the framework. Therefore, all you need to do is setup and build is to clone this repository and within the directory,

mkdir build && cd build && cmake -DNLA4HPC_KIT_UNAME=<your kit uname> -DNLA4HPC_HW=<the HW number you are trying to compile> .. 

Adding your own code.

Please create a separate repository for each hw. pr_hw0_build for HW0, pr_hw1_build for HW1 and so on.

First, please set the main exercises/ repository as the upstream repository

git remote add upstream https://git.scc.kit.edu/nla4hpc_winter20-21/exercises.git

See git-remote doc for more details.

Every once in a while or when you see that the repository has been updated with new commits, do a git fetch upstream to get the changes from the upstream repository into your local system.

Then, assuming that you have not any changes outside of the uxxxx0 directory, when you are on your pr_hwX_build you should directly be able to do

git rebase upstream/master

See git-rebase doc for more details.

In case there were changes in the specific uxxxx0 directories, then you can merge the changes, but please overwrite any of your changes outside of this directory with the commits from the upstream. This makes merging to the master much easier later.

All homeworks have the following structure:

 hwX
 \|
 \|
 uxxxx0 (your pseudonym)
 \|
 \|-- include
 \|     \|
 \|     \|-- hwX.hpp (contains the function and class definitions)
 \|
 \|-- src 
 \|   \|
 \|   \|-- hwX.cpp (contains the implementations themselves (ADD YOUR CODE HERE!))
 \|
 \|-- tests
 \|    \|
 \|    \|-- hwX.cpp (contains the unit tests for the hwX/hwX.cpp functions, update your tests here.)
 \|
 \|-- benchmark
       \|
       \|-- hwX.cpp (contains the benchmarks for the hwX/hwX.cpp functions, update your benchmarks here)

See the instruction sheet for each of the HW for more details and the README.md files within the hwX folders should also provide you with more information.

Working with CUDA and GPUs

Homeworks 5 and 6 require the CUDA Toolkit. A helper library called the CudaArchitectureSelector is used to detect the existing CUDA architectures and pass the appropriate flags. You can tweak the architectures by modifying the NLA4HPC_CUDA_ARCHITECTURES flag which is set to Auto by default.

The CUDA Programming guide has detailed information on how to program on NVIDIA GPUs. You can also refer to the corresponding lecture slides for more references.

Some general tips

Working with Ginkgo.

For these HW’s we would like you to not use Ginkgo and its functionalities in your code, but use them only for the correctness checks. Nevertheless, detailed documentation on Ginkgo is available through the Ginkgo repository.
If your tests cannot seem to find ginkgo, you may need to update your LD_LIBRARY_PATH to add the library path of Ginkgo. If for example, Ginkgo has been installed in Ginkgo_DIR, then you can set the LD_LIBRARY_PATH with export LD_LIBRARY_PATH=$Ginkgo_DIR/lib:$LD_LIBRARY_PATH.

If you already have Ginkgo installed.

If you have Ginkgo installed or you are having some issues with installation with the third_party/ginkgo, you can export Ginkgo_DIR=/path/to/ginkgo/installation and for the cmake step use

cmake -DNLA4HPC_USE_EXTERNAL_GINKGO=on -DNLA4HPC_KIT_UNAME=<your kit uname> -DNLA4HPC_HW=<the HW num> -DNLA4HPC_GENERATE_BENCHMARK=ON ..

instead. This means that your installation of Ginkgo would be used for the correctness checks instead of the one in the third_party/ginkgo directory.

For a more detailed explanation on the workflow and general tips and tricks see the Contributing guidelines