Polyhedral Optimization of TensorFlow Computation Graphs
Benoit Pradelle, Benoit Meister, Muthu Baskaran, Jonathan Springer, Richard Lethin
Publication Source: The 6th Workshop on Extreme-scale Programming Tools (ESPT-2017) at The International Conference for High Performance Computing, Networking, Storage and Analysis (SC17)
We present R-Stream·TF, a polyhedral optimization tool for neural network computations. R-Stream·TF transforms computations performed in a neural network graph into C programs suited to the polyhedral representation and uses R-Stream, a polyhedral compiler, to parallelize and optimize the computations performed in the graph. R-Stream·TF can exploit the optimizations available with R-Stream to generate a highly optimized version of the computation graph, specifically mapped to the targeted architecture. During our experiments, R-Stream·TF was able to automatically reach performance levels close to the hand-optimized implementations, demonstrating its utility in porting neural network computations to parallel architectures.