Nsight Systems Exposes GPU Optimization

Unlocking GPU Performance: A Deep Dive into NVIDIA Nsight Systems

Summary: NVIDIA Nsight Systems is a powerful performance analysis tool designed to help developers optimize their applications running on GPUs, CPUs, and multi-node systems. This article explores how Nsight Systems can be used to identify bottlenecks, optimize GPU usage, and improve overall system performance.

Understanding the Need for GPU Optimization

Machine learning (ML) and deep learning (DL) workloads are becoming increasingly complex, demanding efficient use of hardware resources and time. Training times can significantly affect productivity and model iteration cycles, making it crucial to leverage the right tools to profile and optimize these workloads.

What is NVIDIA Nsight Systems?

NVIDIA Nsight Systems is a performance analysis tool that provides a detailed, time-accurate view of system-wide activities such as kernel launches, memory transfers, and CPU/GPU interactions. It integrates seamlessly with CUDA, DNNL, TensorRT, and other NVIDIA libraries, making it an ideal tool for optimizing ML, DL, and AI workloads.

Key Components of Nsight Systems

Timeline View

The timeline view shows the execution timeline of different threads and processes on the system, allowing developers to see how CPU and GPU workloads overlap and where bottlenecks occur.

CUDA Kernels

The CUDA kernels section monitors the performance of CUDA kernels, including kernel execution time, memory accesses, and other critical metrics.

CPU/GPU Activity

This section displays the amount of time spent on CPU and GPU operations, helping developers identify if there’s an imbalance between CPU and GPU workloads.

NVTX Ranges

NVIDIA Tools Extensions (NVTX) are user-defined markers that help track specific events in the code, allowing developers to visualize where time is being spent in the model or application.

Identifying Bottlenecks with Nsight Systems

Nsight Systems can be used to identify bottlenecks such as GPU starvation, unnecessary GPU synchronization, insufficient CPU parallelization or pipelining, and unexpectedly expensive CPU or GPU algorithms.

GPU Starvation Investigations

Nsight Systems makes it easy to spot GPU starvation and work backward to understand the cause. The CUDA device row contains blue height graphs representing CUDA kernel coverage for a given segment of time, relative to the zoom level.

Case Study: VMD Performance Increase

VMD developer John Stone presented how he achieved a greater than 3x performance increase in VMD using Nsight Systems. This presentation highlighted the importance of using the right tools to identify and optimize performance bottlenecks.

Optimizing Deep Learning Workloads with Nsight Systems

Nsight Systems can be used to optimize deep learning workloads by identifying performance bottlenecks and optimizing GPU usage.

Example: FashionMNIST Dataset

Using Nsight Systems to optimize a deep learning model built on the FashionMNIST dataset resulted in a dramatic reduction in training time - from 28 seconds to just 2 seconds.

Optimizing Data Loading and Transfer

Setting pin_memory=True in the DataLoader and using DistributedDataParallel instead of DataParallel can greatly improve performance by reducing training time and improving scalability.

Table: Nsight Systems Key Features

Table: Optimizing Deep Learning Workloads

Conclusion

NVIDIA Nsight Systems is a powerful tool for optimizing GPU performance and improving overall system performance. By identifying bottlenecks and optimizing GPU usage, developers can significantly reduce training times and improve model iteration cycles. With its detailed, time-accurate view of system-wide activities, Nsight Systems is an essential tool for any developer working with ML, DL, and AI workloads.