Fast Convolutional Nets With fbfft: A GPU Performance Evaluation (1412.7580v3)

Abstract: We examine the performance profile of Convolutional Neural Network training on the current generation of NVIDIA Graphics Processing Units. We introduce two new Fast Fourier Transform convolution implementations: one based on NVIDIA's cuFFT library, and another based on a Facebook authored FFT implementation, fbfft, that provides significant speedups over cuFFT (over 1.5x) for whole CNNs. Both of these convolution implementations are available in open source, and are faster than NVIDIA's cuDNN implementation for many common convolutional layers (up to 23.5x for some synthetic kernel configurations). We discuss different performance regimes of convolutions, comparing areas where straightforward time domain convolutions outperform Fourier frequency domain convolutions. Details on algorithmic applications of NVIDIA GPU hardware specifics in the implementation of fbfft are also provided.

A GPU Performance Evaluation of Fast Convolutional Networks Using fbfft

The paper "Fast Convolutional Nets with fbfft: A GPU Performance Evaluation" presents a detailed paper of efficient Fast Fourier Transform (FFT)-based convolution implementations for accelerating convolutional neural networks (CNNs) on GPUs. The research introduces two FFT convolution techniques—an adaptation of NVIDIA’s cuFFT library and a customized implementation named fbfft developed by Facebook. These implementations aim to outperform existing methods provided in NVIDIA’s cuDNN library, emphasizing speedup gains across various convolution layer configurations.

Key Contributions and Results

The research explores the computational bottlenecks in CNNs caused by the intensive operations required in convolutional layers and proposes FFT-based solutions to mitigate these challenges. The contributions are encapsulated as follows:

1. Implementation Details:
   • The convolution implementations leverage the FFT approach to handle convolutions in the frequency domain, where they are computationally more efficient compared to the spatial domain for large input sizes and kernel configurations.
   • Detailed methods for forward propagation (fprop), back-propagation (bprop), and accumulating gradients (accGrad) are described with their adaptations to FFT frameworks, with a focus on handling data layout conversions and complex arithmetic efficiently.
2. Performance Evaluation:
   • Extensive empirical evaluation demonstrates substantial speedup over cuDNN, particularly for medium to large kernel sizes (e.g., 5x5 and above) where FFT-based methods become advantageous, achieving speedups of up to 23.5 times in specific configurations.
   • The evaluation spans 8,232 configuration settings, emphasizing robustness across diverse problem sizes and network archetypes.
3. fbfft Development:
   • fbfft is built to address limitations observed in black-box libraries like cuFFT for the specific domain of deep learning where small, batched transforms are prevalent.
   • The paper notes fbfft’s efficiency, achieving GPU utilization ratios upwards of 75% by optimizing communication and computation using techniques like warp-level parallelism, data locality enhancements, and transposition elimination.

Theoretical Implications and Practical Applications

The theoretical implications lie in demonstrating the viability of FFTs not only for theoretical but practical enhancement of CNN performance metrics. The implications span:

• Algorithm Efficiency:

The reduction of computational complexity by transforming convolution operations, which are typically O(n^2), to frequency domain multiplications where they benefit from FFT’s O(n log n) complexity, opens pathways for algorithmically efficient yet hardware-friendly neural network training paradigms.

• Exploration of Hardware Capabilities:

This work underscores the importance of low-level hardware optimizations, leveraging GPU architectural specifics to maximize data throughput and minimize latency, potentially influencing further CUDA-based parallel computing development.

Future Directions

The insights provided set a direct pathway for exploring further optimizations such as bit twiddling and improved memory management to handle larger FFTs beyond the current dimensional limits. Moreover, the tiling strategies proposed could be further refined to exploit spatial domain characteristics for more intricate CNN architectures. This foundation potentially lays the groundwork for newer, faster neural network frameworks optimized for emerging AI workloads.

Overall, the paper contributes significant advancements both in GPU computational strategies and in the application of FFTs within machine learning pipelines, offering compelling evidence that dedicated, domain-specific algorithm optimization can outclass general-purpose library functions in specialized contexts. As neural networks grow more complex, such specialized improvements may prove crucial in maintaining feasible training and inference timelines.