Our most popular question is “What can I do to get great GPU performance for deep learning?” We’ve recently published a detailed Deep Learning Performance Guide…
Overview
This article provides essential tips for optimizing GPU performance using Tensor Cores, focusing on practical strategies for deep learning tasks. It highlights the importance of mixed precision and specific configurations that can significantly enhance computational efficiency.
What You'll Learn
1
How to activate Tensor Cores for improved GPU performance
2
Why padding vocabulary size to multiples of 8 enhances throughput
3
How to choose batch sizes to avoid quantization effects
Prerequisites & Requirements
- Understanding of deep learning and GPU architecture
- Familiarity with NVIDIA's CUDA and cuBLAS libraries(optional)
Key Questions Answered
How do Tensor Cores accelerate deep learning operations?
Tensor Cores accelerate deep learning operations by enabling mixed precision computations, which allows for faster processing of fully-connected and convolutional layers. They are activated when certain parameters of a layer are divisible by 8 for FP16 data or 16 for INT8 data, significantly improving throughput.
What are the effects of quantization on GPU performance?
Quantization can lead to inefficiencies such as tile quantization and wave quantization, which occur when output matrices do not evenly divide into available tile sizes or when thread blocks do not fully utilize multiprocessors. Choosing parameters that are divisible by powers of 2 can help mitigate these effects.
What batch sizes should be used to optimize Tensor Core performance?
Batch sizes should be chosen to be multiples of 640 to avoid wave quantization effects and should also be divisible by 128 to prevent tile quantization. This ensures that the GPU operates efficiently and maximizes throughput during training.
Key Statistics & Figures
Vocabulary size for WMT14 task
33708
This size is used in the projection layer example to illustrate the importance of padding for Tensor Core activation.
Batch size for feed-forward layer example
5120
This batch size is used in performance comparisons to demonstrate the impact of Tensor Core activation.
Technologies & Tools
Hardware
Tensor Cores
Used to accelerate deep learning operations on NVIDIA GPUs.
Software
Cuda
Provides the programming model for utilizing GPU resources effectively.
Software
Cublas
Used for optimized matrix operations in deep learning tasks.
Key Actionable Insights
1Activate Tensor Cores by ensuring layer parameters are divisible by 8 or 16.This is crucial for maximizing GPU performance in deep learning tasks, as it allows the use of Tensor Cores instead of fallback CUDA cores, leading to significant speed improvements.
2Pad vocabulary sizes to the next multiple of 8 for projection layers.This simple adjustment can lead to substantial performance gains, especially in large models like Transformers, where the vocabulary size can exceed 30,000.
3Choose batch sizes that are multiples of 640 to optimize performance.This helps avoid wave quantization, ensuring that all available multiprocessors on the GPU are utilized effectively, which is essential for maintaining high throughput.
Common Pitfalls
1
Not ensuring that layer parameters are divisible by required sizes.
This can lead to the use of slower CUDA cores instead of Tensor Cores, resulting in suboptimal performance during training.
2
Ignoring the effects of quantization on performance.
Failing to account for tile and wave quantization can lead to inefficient GPU utilization, wasting computational resources and slowing down training.
Related Concepts
Mixed Precision Training Techniques
Performance Optimization Strategies For Deep Learning
Understanding GPU Architecture And Its Implications For Neural Networks