cuDNN: Efficient Primitives for Deep Learning (1410.0759v3)

Abstract: We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and time-consuming. As parallel architectures evolve, kernels must be reoptimized, which makes maintaining codebases difficult over time. Similar issues have long been addressed in the HPC community by libraries such as the Basic Linear Algebra Subroutines (BLAS). However, there is no analogous library for deep learning. Without such a library, researchers implementing deep learning workloads on parallel processors must create and optimize their own implementations of the main computational kernels, and this work must be repeated as new parallel processors emerge. To address this problem, we have created a library similar in intent to BLAS, with optimized routines for deep learning workloads. Our implementation contains routines for GPUs, although similarly to the BLAS library, these routines could be implemented for other platforms. The library is easy to integrate into existing frameworks, and provides optimized performance and memory usage. For example, integrating cuDNN into Caffe, a popular framework for convolutional networks, improves performance by 36% on a standard model while also reducing memory consumption.

cuDNN: Efficient Primitives for Deep Learning

The paper "cuDNN: Efficient Primitives for Deep Learning" by Sharan Chetlur et al. addresses a fundamental need in the field of deep learning: the provision of highly-optimized computational routines for deep neural network operations. This necessity arises due to the computational intensity of deep learning workloads, which require frequent optimization and reoptimization of kernels as parallel architectures, particularly GPUs, evolve.

Summary of Contributions

The authors introduce cuDNN, a C-language API library offering efficient implementations of deep learning primitives specifically optimized for GPUs. This library aims to streamline the process of training and utilizing deep neural networks by providing a suite of optimized routines such as convolution, pooling, and activation functions. The main contributions of the paper can be summarized as follows:

1. Library Design and API:
   • cuDNN aims for easy integration with existing deep learning frameworks without imposing specific software frameworks or data layouts.
   • The API is low-level, focusing on computational primitives to facilitate broad compatibility and ease of integration.
2. Implementation Details:
   • The library supports various operations that are crucial for deep neural networks, including forward and backward passes for convolutions in both single and double precision floating-point arithmetic.
   • It includes a range of tensor transformations, such as those required for manipulating 4D-tensors, which are commonly used in deep learning.
3. Performance and Portability:
   • By leveraging an algorithm that dynamically constructs lowered matrices into on-chip memory, the convolution routines in cuDNN avoid the overhead of materializing large auxiliary data structures.
   • Performance evaluations demonstrate significant improvements. For instance, when integrated into the Caffe framework, cuDNN achieves a 36% improvement in training time for a standard model.
4. Caffe and Baidu Integration:
   • The paper details the integration of cuDNN into the Caffe deep learning framework, resulting in notable speed and memory efficiency gains.
   • Additionally, cuDNN has been integrated into Baidu's internal deep learning framework, PADDLE, achieving a 30% performance improvement over previous approaches.

Numerical Results and Performance Implications

The empirical results presented in the paper are compelling. For instance:

• Caffe Integration: Training time for 200 iterations of a standard model decreased by 36% with cuDNN, demonstrating the library's efficiency in real-world applications.
• Convolution Performance: On an NVIDIA Tesla K40 GPU, cuDNN's convolution routines achieve up to 2.25 times the performance of cuda-convnet2 and up to 1.41 times that of Caffe.
• Performance Portability: cuDNN's flexibility is highlighted by its consistent performance across different GPU architectures, achieving up to 51% of peak performance on the newer Maxwell architecture compared to the Kepler architecture.

Theoretical and Practical Implications

The introduction of cuDNN has both theoretical and practical implications. Theoretically, it extends the concept of a standard library for deep learning primitives, analogous to BLAS for linear algebra. Practically, it significantly reduces the time and expertise required for researchers and engineers to optimize their deep learning code across different GPU architectures. This democratizes the ability to experiment with larger and more complex models by efficiently managing computational resources and memory.

Future Developments

Looking forward, the paper outlines several promising avenues for future development of cuDNN:

• Extending Primitive Support: The addition of 1D and 3D convolutions for applications in speech, language processing, and video is under consideration.
• Performance Enhancements: Further work aims to close the performance gap between convolution routines and highly-optimized matrix multiplication routines.
• Multi-GPU Support: Enabling the library to leverage multiple GPUs for training acceleration is an anticipated enhancement.

Conclusion

The cuDNN library represents a significant advancement in providing efficient, reusable primitives for deep learning workloads. Its capacity to offer substantial performance gains while minimizing memory overhead makes it a crucial tool for the machine learning community. By abstracting the complexity of hardware-specific optimizations, cuDNN allows researchers to focus on high-level issues and model innovations, reflecting a pivotal step in the evolution of deep learning infrastructure.