ISAAC
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).
License
ISAAC is distributed under the MIT License.
Installation
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.
Benchmark
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
BLAS routines supported
Currently supported functions are:
BLAS1 | BLAS2 | BLAS3 |
---|---|---|
xAXPY | xGEMV | xGEMM |
xCOPY | xGER | |
xSCAL | ||
xDOT | ||
xASUM |
For x in {S, D}
Contributing
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!