Using Nsight Compute or Nvprof for Mixed Precision in Deep Learning Models

Unlocking the Power of Mixed Precision in Deep Learning Models

Summary

Mixed precision is a technique that combines different numerical precisions in a computational method to accelerate deep learning model training and inference. This article explores how to use NVIDIA’s Nsight Compute and Nvprof tools to analyze and optimize mixed precision in deep learning models. We will delve into the benefits of mixed precision, how to identify which operations can be run in lower precision, and how to use Nsight Compute and Nvprof to profile and optimize model performance.

What is Mixed Precision?

Mixed precision is a technique that allows deep learning models to be trained and inferred using a combination of different numerical precisions. This approach can significantly accelerate model training and inference by reducing memory traffic and increasing throughput. The Volta and Turing generation of GPUs introduced Tensor Cores, which provide significant throughput speedups over single precision math pipelines.

Benefits of Mixed Precision

Mixed precision offers several benefits, including:

• Increased throughput: By using lower precision for certain operations, mixed precision can increase model throughput and reduce training time.
• Reduced memory traffic: Lower precision operations require less memory bandwidth, reducing memory traffic and increasing model performance.
• Improved model accuracy: By using higher precision for critical operations, mixed precision can maintain model accuracy while still achieving performance gains.

Identifying Operations for Lower Precision

To use mixed precision effectively, it’s essential to identify which operations can be run in lower precision without impacting model accuracy. This typically includes:

• Matrix multiplications: These operations can be run in lower precision using Tensor Cores, which provide significant throughput speedups.
• Convolutional layers: These layers can also be run in lower precision, reducing memory traffic and increasing model performance.

Using Nsight Compute and Nvprof

Nsight Compute and Nvprof are powerful tools for analyzing and optimizing mixed precision in deep learning models. Here’s how to use them:

Nsight Compute

Nsight Compute is an interactive profiler for CUDA and NVIDIA OptiX that provides detailed performance metrics and API debugging via a user interface and command-line tool. To use Nsight Compute:

1. Launch Nsight Compute: Open the Nsight Compute GUI or use the command-line tool to launch the profiler.
2. Select the model: Choose the deep learning model you want to profile and optimize.
3. Run the profiler: Run the profiler to collect performance metrics and identify areas for optimization.
4. Analyze the results: Use the Nsight Compute GUI to analyze the results and identify opportunities for optimization.

Nvprof

Nvprof is a command-line profiling tool that provides detailed performance metrics and API debugging for CUDA applications. To use Nvprof:

1. Launch Nvprof: Open a terminal and use the nvprof command to launch the profiler.
2. Select the model: Choose the deep learning model you want to profile and optimize.
3. Run the profiler: Run the profiler to collect performance metrics and identify areas for optimization.
4. Analyze the results: Use the nvprof command-line tool to analyze the results and identify opportunities for optimization.

Profiling Mixed Precision with Nsight Compute and Nvprof

To profile mixed precision with Nsight Compute and Nvprof:

1. Use the tensor_precision_fu_utilization metric: This metric reveals the utilization level of Tensor Cores in each kernel of your model.
2. Run the profiler: Run the profiler to collect performance metrics and identify areas for optimization.
3. Analyze the results: Use the Nsight Compute GUI or nvprof command-line tool to analyze the results and identify opportunities for optimization.

Example Use Case

Here’s an example use case for profiling mixed precision with Nsight Compute and Nvprof:

In this example, using mixed precision with Nsight Compute and Nvprof resulted in a 3x increase in model throughput.

Conclusion

Mixed precision is a powerful technique for accelerating deep learning model training and inference. By using Nsight Compute and Nvprof, developers can analyze and optimize mixed precision in their models, achieving significant performance gains while maintaining model accuracy. By following the steps outlined in this article, developers can unlock the full potential of mixed precision and take their deep learning models to the next level.