Get Started With Deep Learning Performance
This is the landing page for our deep learning performance documentation. This page provides recommendations that apply to most deep learning operations. It also provides links, short explanations of other performance documents, and how these pages fit together.
GPUs accelerate machine learning operations by performing calculations in parallel. Many operations, especially those representable as matrix multiplies, will see good acceleration right out of the box. Even better performance can be achieved by tweaking operation parameters to efficiently use GPU resources.
This document presents the tips that we think are most widely useful. We link to each of the other pages, with more in-depth information, where appropriate. If you want to jump straight to optimizing a network, read our Checklists!
GPUs excel at performing calculations in parallel, but data also needs to be loaded and stored around those calculations, and thus data movement speed can also limit achievable performance. If the speed of a routine is limited by calculation rate (math-limited or math-bound), performance can be improved by enabling Tensor Cores and following our other recommendations.
On the other hand, if a routine is limited by the time taken to load inputs and write outputs (bandwidth-limited or memory-bound), speeding up calculation does not improve performance. For fully-connected and convolutional layers, this occurs mostly when one or more parameters of a layer are small.
In other words, if an operation is memory-bound, tweaking parameters to more efficiently utilize the GPU is ineffective. Operations not representable as matrix multiplies, including activation functions, pooling, and batch normalization, are nearly always memory-bound. Those with an equivalent matrix multiply, including fully-connected, convolutional, and recurrent layers, may be memory-bound or math-bound depending on their sizes. Larger layers tend to have more calculations relative to the number of memory accesses, a ratio that we refer to as arithmetic intensity. If arithmetic intensity exceeds a particular threshold (dependent on the GPU type and the type of calculation being done), the operation is math-bound and can be optimized effectively with our tips. See Understanding Performanceand Math and Memory Bounds for background and details.
Tensor Cores are most efficient when key parameters of the operation are multiples of 4 if using TF32, 8 if using FP16, or 16 if using INT8 (equivalently, when key dimensions of the operation are aligned to multiples of 16 bytes in memory). For fully-connected layers, the relevant parameters are the batch size and the number of inputs and outputs; for convolutional layers, the number of input and output channels; and for recurrent layers, the minibatch size and hidden sizes. With NVIDIA® cuBLAS 11.0 or higher and NVIDIA CUDA® Deep Neural Network library (cuDNN) 7.6.3 or higher, Tensor Cores can be used even if this requirement is not met, though performance is better if it is. In earlier versions, Tensor Cores may not be enabled if one or more dimensions aren’t aligned. This requirement is based on how data is stored and accessed in memory. Further details can be found in Tensor Core Requirements.
TF32, a datatype introduced with the NVIDIA Ampere Architecture, works with existing FP32 code to leverage Tensor Cores. More detail on TF32 can be found at this link. Mixed precision is another option for networks that currently use FP32, and works with both the NVIDIA Ampere Architecture and NVIDIA Volta™ and NVIDIA Turing™ GPUs. The NVIDIA Training with Mixed Precision Guide explains how to use mixed precision with Tensor Cores, including instructions for getting started quickly in a number of frameworks.
GPUs perform operations efficiently by dividing the work between many parallel processes. Consequently, using parameters that make it easier to break up the operation evenly will lead to the best efficiency. This means choosing parameters (including batch size, input size, output size, and channel counts) to be divisible by larger powers of two, at least 64, and up to 256.
There is no downside to using values divisible by 512 and higher powers of two, but there is less additional benefit. Divisibility by powers of two is most important for parameters that are small; choosing 512 over 520 has more impact than choosing 5120 over 5128. Additionally, for these tweaks to improve efficiency, the operation must already be math-bound, which usually requires at least one parameter to be substantially larger than 256. See Operating In Math-Limited Regime Where Possible and other linked sections about calculating arithmetic intensity.
More specific requirements for different routines can be found in the corresponding checklist and guide. Background on why this matters can be found in GPU Architecture Fundamentals and Typical Tile Dimensions in CUBLAS and Performance.
NVIDIA’s GPU deep learning platform comes with a rich set of other resources you can use to learn more about NVIDIA’s Tensor Core GPU architectures as well as the fundamentals of mixed-precision training and how to enable it in your favorite framework.
The NVIDIA V100 GPU architecture whitepaper provides an introduction to NVIDIA Volta, the first NVIDIA GPU architecture to introduce Tensor Cores to accelerate Deep Learning operations. The equivalent whitepaper for the NVIDIA Turing architecture expands on this by introducing NVIDIA Turing Tensor Cores, which add additional low-precision modes. The whitepaper for the NVIDIA Ampere architecture introduces Tensor Core support for additional precisions (including TF32, which works with existing FP32 workloads to leverage Tensor Cores), up to 2x throughput with the Sparsity feature, and virtual partitioning of GPUs with the Multi-Instance GPU feature.
The NVIDIA Training With Mixed Precision User's Guide describes the basics of training neural networks with reduced precision such as algorithmic considerations following from the numerical formats used. It also details how to enable mixed precision training in your framework of choice, including TensorFlow, PyTorch, and MXNet. The easiest and safest way to turn on mixed precision training and use Tensor Cores is through Automatic Mixed Precision, which is supported in PyTorch, TensorFlow, and MxNet. Additional documentation is provided to help explain how to:
- Tweak parameters of individual operations by type, with examples:
- Understand the ideas behind these recommendations:
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