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Automatically-Tuned Input-Aware implementations of HPC/DNN primitives
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README.md

ISAAC

This is the development repository for ISAAC, an input-aware auto-tuning framework and code-generator for HPC/DL. This version is only compatible with NVIDIA hardware (it generates PTX source code). For OpenCL/CUDA compatibility, visit the Intel fork (https://github.com/intel/isaac) or the v1.0 branch (deprecated) or the

License

ISAAC is distributed under the MIT/X11 license.

Getting started - Deep Learning Inference

Execute the following commands on a python environment that contains a recent version of pytorch:

git clone https://github.com/ptillet/isaac.git
cd isaac/python;
python setup.py build;
python setup.py install;
cd examples/pytorch;
python imagenet.py --arch resnet152 /path/to/imagenet/;

This should give you 78.1% accuracy, and roughly 4x speed-up over pytorch.

Getting started - C++ API

In order to compile and use the ISAAC C++ API, only a proprietary NVIDIA driver is necessary. No CUDA SDK is required (except for testing and benchmarking against cuBLAS/cuDNN):

git clone https://github.com/ptillet/isaac.git
cd isaac; 
mkdir build; 
cd build;
cmake ../ ; make -j8;
./examples/isaac-tools --gemm --bench --suite deepbench --dtype float32
./examples/isaac-tools --conv --bench --suite deepbench --dtype float32

If you want, you can also dump the PTX source code generated by ISAAC for some shapes:

./examples/isaac-tools --gemm --dump --format ptx --shape 2048,2048,2048 --layout NT --dtype float32

If you really know what you're doing, you can also capture the tiling parameters found by ISAAC:

./examples/isaac-tools --gemm --dump --format params --shape 2048,2048,2048 --layout NT --dtype float32

You will get the following output:

Tuning parameters: 4, 16, 8, 8, 8, 8, 16, 8, 16, 8, 1, 1, 1

The parameters respectively mean: (1) that shared memory loads have a width of 4 ; (2) each block comprises 16x8 threads ; (3) each threads computes a tile of 8x8 elements; (4) Each loop iteration processes 8 elements along the K axis ; (5) threads are rearranged as a 16 x 8 block for loading A, and a 16 x 8 block for loading B; (6) the reduction is split accross 1, 1 and 1 independent batches within each thread, thread-block and grid, and the results are accumulated after the inner-loop

Benchmarks - C++ API

ISAAC often provides Tesla P100 - SGEMM: sgemm-gv100

Tesla P100 - DGEMM: sgemm-gv100

Tesla P100 - SCONV (vs cuDNN's IMPLICIT_PRECOMP_GEMM) sgemm-gv100

Acknowledgments

This work was partially supported by the National Science Foundation (IIS 1409097) and by IARPA (contract D16PC00002).

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