Accelerating Deep Learning with TensorFlow and GPUs

Deep learning has revolutionized the field of machine learning, enabling remarkable breakthroughs in areas like computer vision, natural language processing, robotics, and more. However, training complex deep neural networks is an extremely computationally intensive process.

Accelerating the training of deep learning models is critical for making them practical to develop and deploy. One of the most significant breakthroughs in this area has been the use of graphics processing units (GPUs) to dramatically speed up the core computational bottleneck in deep learning: matrix multiplication. By taking advantage of the highly parallel architecture of modern GPUs, deep learning frameworks like TensorFlow are able to train models orders of magnitude faster than with CPUs alone.

In this article, we‘ll take an in-depth look at how TensorFlow utilizes NVIDIA GPUs to accelerate deep learning workloads. We‘ll explore the performance benefits, key GPU libraries, tips for maximizing performance, and the future outlook of GPU-accelerated deep learning.

How GPUs Accelerate Deep Learning

To understand how GPUs provide such a dramatic speedup for deep learning, we need to look at two key factors:

1. The computational demands of deep neural networks
2. The architecture of modern GPUs

Deep neural networks are composed of multiple layers of interconnected nodes. Each connection has a weight that is learned during training. The magic of deep learning happens when input data is fed through the network and each layer transforms the data in a way that enables the network to learn hierarchical representations and discover useful patterns.

Fundamentally, these transformations involve a huge number of matrix multiplication operations. Matrix multiplies are the core computational kernel of deep learning. At each layer, the input data and weights are multiplied together, element-wise, to produce the output. For a large neural network with millions of weights trained on billions of data points, the sheer number of floating point operations required is staggering.

This is where GPUs come to the rescue. While CPUs are designed for general purpose computing and optimized for sequential tasks, GPUs have a fundamentally different architecture. Originally designed to render graphics for video games and other visually intensive applications, GPUs contain thousands of small, specialized cores optimized for parallel processing.

A single GPU can have thousands of cores, compared to a high-end CPU which may have 8, 16, or 32 cores. Moreover, GPUs have a very high memory bandwidth, which is critical for the type of dense matrix computations found in deep learning. For certain types of parallel computations, GPUs can be up to 100X faster than CPUs.

The highly parallel architecture of GPUs maps perfectly to the type of dense matrix multiplication that is the computational crux of deep learning. With a CPU, the matrix multiplications are performed mostly sequentially. But with a GPU, many of the multiplication operations can be performed in parallel, greatly reducing the overall computation time.

Let‘s look at some concrete performance numbers to illustrate the difference. A 2019 paper by researchers at Lawrence Berkeley National Laboratory benchmarked the training performance of several deep learning models on CPUs vs GPUs [1]. They found that:

• Training ResNet-50 on ImageNet took 29 hours on a 24-core CPU vs just 1 hour on an NVIDIA V100 GPU (a 29X speedup)
• Training GNMT, a translation model, took 16 days on a 24-core CPU vs 16 hours on an NVIDIA P100 GPU (a 24X speedup)

Other industry benchmarks have shown similar results. A 2019 benchmark by Lambda Labs compared NLP model training performance across different GPU and CPU instances on Amazon Web Services [2]. They found that for training BERT-large, one of the most popular NLP models:

• An 8x NVIDIA V100 GPU setup was 53X faster than a 96-core CPU
• A single V100 was 12X faster than a 48-core CPU

These speedups are not just theoretical – they have a massive impact on the productivity of machine learning engineers and researchers. Jobs that used to take weeks or months can now be done in days or hours. The faster training times enabled by GPUs make a lot more experiments and iterations possible, driving progress in the field.

Of course, training deep learning models is not a simple matter of throwing more GPU hardware at the problem. Successful training requires carefully designing a model architecture, tuning hyperparameters, preprocessing the training data, and optimizing the training pipeline. But the availability of GPU acceleration has been a key enabler of many of the most significant breakthroughs in deep learning over the past decade.

GPU Acceleration in TensorFlow

TensorFlow, one of the most popular open source libraries for machine learning developed by Google, has offered built-in support for NVIDIA GPUs since its initial public release in 2015. TensorFlow provides a high-level API for building and training deep learning models that abstracts away many of the complexities of working with GPUs.

Under the hood, TensorFlow relies on multiple GPU-accelerated libraries to implement the low-level computations on NVIDIA hardware:

• CUDA: The NVIDIA CUDA (Compute Unified Device Architecture) library provides a general purpose parallel programming model for NVIDIA GPUs. CUDA exposes the GPU as a parallel processing device that can execute a large number of threads concurrently. TensorFlow uses CUDA to implement many of its core operations on tensors.

• cuDNN: The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks [3]. It provides highly tuned implementations of common DNN layers like convolution, pooling, normalization, and activation functions. TensorFlow can use cuDNN to accelerate these building blocks of neural networks.

• TensorRT: TensorRT is an SDK for high-performance deep learning inference on NVIDIA GPUs [4]. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications. TensorFlow can use TensorRT to optimize trained models for inference on GPUs.

With each new release, TensorFlow has continued to deepen its integration with these libraries to deliver better performance on NVIDIA GPUs. TensorFlow 2.0, released in 2019, introduced eager execution as the default execution mode. Eager execution allows for more intuitive debugging and a more natural imperative programming style. On the performance front, TensorFlow 2.0 included several GPU-related enhancements [5]:

• Improved data pipeline performance with the tf.data API
• Better memory management and allocation for GPU training
• More effective use of TensorCore units on Volta and Turing GPUs
• Multi-GPU enhancements including tf.distribute.MirroredStrategy

All these optimizations have a material impact on end-to-end training time. Google reports that with TensorFlow 2.0, ResNet-50 training on a TPU Pod is 7X faster compared to TensorFlow 1.0, with a significant portion of that speedup coming from GPU optimizations [5].

Optimizing GPU Performance in TensorFlow

To get the best training performance with TensorFlow and GPUs, you need to be mindful of a few key factors that affect GPU utilization and throughput. Here are some tips for optimizing GPU performance:

1. Use the latest versions of TensorFlow and cuDNN: Performance improvements and bug fixes are added with each new release. Staying up to date will ensure you benefit from the latest optimizations.

2. Minimize host-to-device transfers: Copying data between the CPU (host) and GPU (device) is relatively slow. Batch your data and perform as much preprocessing on the GPU as possible to minimize these transfers.

3. Optimize data loading: Use the tf.data API to parallelize data loading and preprocessing. This can help ensure your GPUs are fed quickly with training data.

4. Increase batch sizes: GPUs achieve the highest utilization with large batch sizes that fully saturate the available cores and memory bandwidth. Experiment with different batch sizes to find the optimal trade-off between training speed and memory usage.

5. Distribute training across multiple GPUs: Modern neural networks can take days or weeks to train on a single GPU. You can dramatically reduce training time by distributing the workload across multiple GPUs with data parallelism. TensorFlow supports several strategies for multi-GPU training including mirrored strategy and parameter server strategy.

6. Mixed precision training: Many NVIDIA GPUs include Tensor Cores that provide significant speedups for lower-precision operations compared to single-precision (FP32). Mixed precision training uses FP16 (half-precision) for some or most of the compute operations while maintaining FP32 master weights. TensorFlow 2 provides a tf.keras.mixed_precision API for enabling mixed precision training with minimal code changes [6].

7. Optimize model architecture: Not all neural network architectures are created equal when it comes to GPU performance. Techniques like pruning and quantization can reduce the computational demands of a model without significantly impacting accuracy. Newer model architectures like EfficientNet, MobileNet, and SqueezeNet are designed to provide good performance with reduced parameters and computation [7].

8. Fuse ops: Combining many small kernel ops into a single larger kernel can significantly improve performance by reducing memory accesses and kernel invocation overhead [8]. The XLA (Accelerated Linear Algebra) JIT compiler for TensorFlow can automatically fuse ops where possible behind-the-scenes. Using @tf.function decoration tells TensorFlow to apply XLA optimizations to your code.

9. Profile and monitor performance: The TensorFlow profiler provides a suite of tools for analyzing your model‘s performance characteristics. You can use the profiler to debug performance bottlenecks, visualize computational graphs, and generate step-by-step execution traces [9].

10. Optimize inference: Training gets a lot of attention, but optimizing inference is critical for model deployment. TensorFlow 2 includes a tf.lite API for converting trained models to a format optimized for inference on mobile and edge devices. You can also use TensorRT to further optimize models for inference on NVIDIA GPUs and edge devices like the Jetson Nano [10].

Future Directions

The future of GPU-accelerated deep learning with TensorFlow is bright. Google continues to invest heavily in TensorFlow development, with a major focus on improving performance at scale. TensorFlow 2 introduced several new features for large-scale GPU training including better support for multi-GPU and TPU pods [5].

The pipeline of new GPU hardware from NVIDIA and others also shows no signs of slowing down. The recently announced NVIDIA A100 GPU packs a whopping 54 billion transistors and delivers 5 petaflops of AI performance [11]. It includes new Tensor Core technology, high bandwidth memory, and high-speed interconnects that provide substantial speedups over the previous generation V100 GPU.

Google has also developed their own custom AI chip called the TPU (Tensor Processing Unit). TPUs are designed from the ground up for accelerating TensorFlow workloads and can provide significant speedups over GPUs on certain models and datasets. TensorFlow 2 introduced the tf.distribute.TPUStrategy API to seamlessly scale models on TPU hardware [12].

Another promising area of research is using AI to optimize AI. Techniques like neural architecture search and AutoML use machine learning to automatically discover optimal neural network architectures and hyperparameters. This "AI designing AI" approach has already yielded state-of-the-art results on certain problems [13]. As the search space of potential architectures grows, the massive parallelism of GPUs will be essential for training the huge number of candidate models.

Federated learning, which trains models on distributed datasets without sharing the underlying data, is another emerging area that will benefit from ongoing improvements in GPU performance and scalability. TensorFlow Federated provides a set of tools for experimenting with decentralized AI [14].

Conclusion

The confluence of big data, GPU hardware, and deep learning frameworks like TensorFlow has unlocked remarkable new capabilities for AI systems. As models continue to get larger and datasets continue to grow, GPU acceleration will be critical to achieving the next wave of breakthroughs.

TensorFlow and NVIDIA GPUs are a powerful combination for deep learning engineers. Hopefully this article has given you a solid understanding of how GPUs accelerate deep learning, how to optimize GPU performance in TensorFlow, and a glimpse of where things are heading.

References

[1] Thorsten Kurth et al. "Exascale Deep Learning for Climate Analytics." SC ‘18: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2018.

[2] Lambda Labs. "TensorFlow Benchmarks." https://lambdalabs.com/blog/best-gpu-tensorflow-2080-ti-vs-v100-vs-titan-v-vs-1080-ti-benchmark/, 2019.

[3] NVIDIA. "NVIDIA cuDNN." https://developer.nvidia.com/cudnn.

[4] NVIDIA. "TensorRT." https://developer.nvidia.com/tensorrt.

[5] TensorFlow. "Effective TensorFlow 2." https://www.tensorflow.org/guide/effective_tf2, 2020.

[6] TensorFlow. "Mixed precision." https://www.tensorflow.org/guide/mixed_precision, 2020.

[7] Mingxing Tan and Quoc V. Le. "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks." ICML, 2019.

[8] NVIDIA. "TensorFlow Performance Guide." https://docs.nvidia.com/deeplearning/frameworks/tensorflow-user-guide/index.html, 2020.

[9] TensorFlow. "Optimize TensorFlow performance using the Profiler." https://www.tensorflow.org/guide/profiler, 2020.

[10] NVIDIA. "TensorFlow Converter." https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html, 2020.

[11] NVIDIA. "NVIDIA A100 Tensor Core GPU Architecture." https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/nvidia-ampere-architecture-whitepaper.pdf, 2020.

[12] TensorFlow. "Distributed training with TensorFlow." https://www.tensorflow.org/guide/distributed_training, 2020.

[13] Barret Zoph and Quoc V. Le. "Neural Architecture Search with Reinforcement Learning." ICLR, 2017.

[14] TensorFlow Federated. "TensorFlow Federated: Machine Learning on Decentralized Data." https://www.tensorflow.org/federated, 2020.