This set of scripts can be used to benchmark the performance of KeOps versus other solutions.

We compare the performances of several computing libraries on a simple benchmark: a Gaussian kernel matrix-vector product with a growing number of points N in dimension D = 3. All experiments are performed with float32 precision on a Nvidia RTX 2080 TiGPU, with the exception of the PyTorch-TPU column that was run in Google Colab.

To compute the Gaussian convolution on GPU using Pykeops with torch.

1. intall pykeops

$ pip install pykeops

2. Run

$ python KeOps.py

To compute the Gaussian convolution on GPU using KeOps c++ backend

mkdir build
cd build
cmake ../../keops
make test_fromdevice
example/test_fromdevice 100000

To compute the Gaussian convolution on GPU using Halide (c++ code)

1. Download Halide, and extract it in the current folder E.g.

$ wget https://github.com/halide/Halide/releases/download/release_2019_08_27/halide-linux-64-gcc53-800-65c26cba6a3eca2d08a0bccf113ca28746012cc3.tgz
$ tar xvf halide-linux-64-gcc53-800-65c26cba6a3eca2d08a0bccf113ca28746012cc3.tgz

2. Compile the sources launch it:

$ g++ gauss_conv_halide.cpp -g -std=c++11 -I halide/tutorial -I halide/include -I halide/tools -L halide/bin -lHalide -lpthread -ldl -o gauss_conv_halide
$ LD_LIBRARY_PATH=halide/bin ./gauss_conv_halide 100000

To compute the Gaussian convolution on GPU using TF-XLA:

1. Install TensorFlow-XLA 2.xx with gpu support. On Google Colab,

!pip install tensorflow-gpu==2.0.0

should do the trick. On a linux system, you may need to define the XLA-FLAGS env:

$ export XLA_FLAGS="--xla_gpu_cuda_data_dir=/path/to/cuda

2. Run

$ python TF_XLA.py

To compute the Gaussian convolution on GPU using PyTorch:

$ python Pytorch_GPU.py

To compute the Gaussian convolution on TPU using PyTorch. This code should be run on a Google Colab session, with TPU acceleration. Copy/paste the content of PyTorch_TPU.py in the collab notebook.

To compute the Gaussian convolution on GPU using PyTorch:

1. Install TVM using this tutorial
2. If you are running on a linux system, export TVM_HOME and PYTHONPATH variable.
3. Run

$ python TVM.py