I/O Lower Bounds for Auto-tuning of Convolutions in CNNs

TL;DR

This paper develops I/O lower bounds for CNN convolution algorithms, designs near-optimal dataflow strategies, and employs auto-tuning to significantly improve GPU performance over existing methods like cuDNN and TVM.

Contribution

It introduces a comprehensive I/O lower bound theory for CNN convolutions, and applies it to optimize dataflow and auto-tuning strategies for direct and Winograd algorithms on GPUs.

Findings

Achieves 3.32x speedup over cuDNN

Faster auto-tuning than TVM

Higher performance than TVM's optimal configurations

Abstract

Convolution is the most time-consuming part in the computation of convolutional neural networks (CNNs), which have achieved great successes in numerous applications. Due to the complex data dependency and the increase in the amount of model samples, the convolution suffers from high overhead on data movement (i.e., memory access). This work provides comprehensive analysis and methodologies to minimize the communication for the convolution in CNNs. With an in-depth analysis of the recent I/O complexity theory under the red-blue game model, we develop a general I/O lower bound theory for a composite algorithm which consists of several different sub-computations. Based on the proposed theory, we establish the data movement lower bound results of two representative convolution algorithms in CNNs, namely the direct convolution and Winograd algorithm. Next, derived from I/O lower bound results, we design the near I/O-optimal dataflow strategies for the two main convolution algorithms by fully exploiting the data reuse. Furthermore, in order to push the envelope of performance of the near I/O-optimal dataflow strategies further, an aggressive design of auto-tuning based on I/O lower bounds, is proposed to search an optimal parameter configuration for the direct convolution and Winograd algorithm on GPU, such as the number of threads and the size of shared memory used in each thread block. Finally, experiment evaluation results on the direct convolution and Winograd algorithm show that our dataflow strategies with the auto-tuning approach can achieve about 3.32x performance speedup on average over cuDNN. In addition, compared with TVM, which represents the state-of-the-art technique for auto-tuning, not only our auto-tuning method based on I/O lower bounds can find the optimal parameter configuration faster, but also our solution has higher performance than the optimal solution provided by TVM.