This is the developer repository for ISAAC, a library that uses machine learning to find input-aware kernels for element-wise operations, 1D/2D reductions and GEMM. It works with both cuBLAS and clBLAS, and fallbacks on those when appropriate (typically large square matrices).

ISAAC is distributed under the MIT License.

ISAAC is dependency-free, and will load either OpenCL and/or CUDA 7.0+ dynamically depending on which GPUs are detected at runtime.

You only need CMake 2.8.7+ and a C++11 compliant compiler:

git clone https://github.com/ptillet/isaac.git
mkdir -p isaac/build && cd isaac/build
cmake ../ && make -j4

Link against libisaac.so instead of libcublas.so or libclblas.so, and you're good to go!

The C++ and Python API does some kernel fusion, but is not entirely stable. It works well to compose element-wise operations, though.

The windows version depends on dlfcn project, please download it at dlfcn windows version and save it at the same directory as isaac project, build dlfcn before build isaac. The following instructions are for Visual C 2015 and to build it for x64 target.

git clone https://github.com/dlfcn-win32/dlfcn-win32
cd dlfcn-win32
cmake -G "Visual Studio 14 2015 Win64" .
cmake --build . --config Release
git clone https://github.com/ptillet/isaac.git
mkdir -p isaac/build
cd build
cmake -G "Visual Studio 14 2015 Win64" ..
cmake --build . --config Release

Usage : blas-bench [--op {axpy, dot, gemv, gemm}] [--dtype {float32, float64}] [--device DEVICE_IDX] [--help]
--op: operation to benchmark (default = gemm)
--dtype: data-type to benchmark (default = float32)
--device: index of isaac device in [0, ..., ndevices - 1] (default = 0)
--help: display this message

It detects clBLAS or cuBLAS and compares it against ISAAC for e.g., DeepBench, Covariance, LAPACK (packed rank1 updates), etc.

Below is the TFLOPS you get for GEMM on a Pascal Titan X (cuBLAS 8.0). Numbers in bold represent speed-ups greater than 5%.

For AMD Fury (clBLAS-2.10-Fiji):

Same trend on Intel Broadwell iGPU

Currently supported functions are:

For x in {S, D}

You can contribute to further tuning isaac if you have one of the following architecture:

• NVidia: SM 2.x ; SM 3.5 ; SM 5.0

If you have one of the following architectures you can contribute by running:

git clone https://github.com/ptillet/isaac.git
cd isaac/python ;
python setup.py build;
cd ../tune
PYTHONPATH=../python/build/lib.linux-x86_64-2.7/ python main.py --float64 --float32 --elementwise_1d --elementwise_2d --reduce_1d --reduce_2d_rows --reduce_2d_cols --gemm_nn --gemm_nt --gemm_tn --gemm_tt

This will output a .json file that you can submit for integration.

Bug reports are more than welcome!