Architectures and platforms for AI -- Module 2

OpenMP and CUDA implementations of a locally connected MLP (2020/2021)

by Daniele Verì

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Requirements

• gcc and make (both found in build-essential)
• nvcc

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Build

command	description
make	build all
make openmp	build openMP implementation
make openmp_release	build openMP release
make openmp_dbg	build openMP debug
make cuda	build CUDA implementation
make cuda_release	build CUDA release
make cuda_dbg	build CUDA debug
make cuda_legacy	build CUDA for arch compute_20 (cuda version < 9)
make doc	create documentation with doxygen
make clean	clean objects

Run

Once builded the source you can just launch the executables passing the mandatory arguments N and K:

• ./mlp_omp N K
• ./mlp_cuda N K

Profile

In order to profile the programs on the local machine just run:

profile/local_profile.sh

To profile the OMP implementation on a remote machine with ssh server enabled just run:

profile/gcp_profile.sh USER HOST CERTIFICATE

To profile the CUDA implementation on a Google Colab instance with GPU:

• open the profile/colab_norebook.ipynb on a Colab instance
• set up a machine with public ip and ssh server enabled that accept a certificate as credential
• upload on the colab instance BOTH public key and the certificate
• run the first two cells, setting the actual server ip
• on the local machine, just run profile/colab_profile.sh SERVER_USER SERVER_HOST CERTIFICATE

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