Auto-Tuning TensorFlow Threading Model for CPU Backend

TensorFlow**Other names and brands may be claimed as the property of others. is a popular deep learning framework used by data scientists to solve a wide-range of machine learning and deep learning problems such as image classification and speech recognition. It also operates at a large scale and in heterogeneous environments - it allows users to train neural network models or deploy them for inference using GPUs, CPUs, (Intel^® Xeon^®†Intel, the Intel logo and Xeon ® are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries. © Intel Corporation. CPUs) and deep learning specific custom-designed hardware such as TPUs. Even though TensorFlow supports a variety of optimized backends, realizing the best performance using a backend may require additional efforts. For instance, getting the best performance from a CPU backend requires careful tuning of its threading model. Unfortunately, the best tuning approach used today is manual, tedious, time-consuming, and, more importantly, may not guarantee the best performance. In this paper, we develop an automatic approach, called TensorTuner, to search for optimal parameter settings of TensorFlow's threading model for CPU backends. We evaluate TensorTuner on both Eigen and Intel's MKL CPU backends using a set of neural networks from TensorFlow's benchmarking suite. Our evaluation results demonstrate that the parameter settings found by TensorTuner produce 2% to 123% performance improvement for the Eigen CPU backend and 1.5% to 28% performance improvement for the MKL CPU backend over the performance obtained using their best-known parameter settings. This highlights the fact that the default parameter settings in Eigen CPU backend are not the ideal settings; and even for a carefully hand-tuned MKL backend, the settings are sub-optimal. Our evaluations also revealed that TensorTuner is efficient at finding the optimal settings - it is able to converge to the optimal settings quickly by pruning more than 90% of the parameter search space.