TensorRT性能优化最佳实践全解析

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"《TensorRT最佳实践指南》是一份由NVIDIA发布的文档，专注于TensorRT 7.0.0版本的性能优化策略。该文档详细探讨了如何有效地衡量、优化TensorRT的性能，以及针对不同场景如模型批量处理、层级优化、插件优化和Python接口的性能提升技巧。在第一章“如何衡量性能？”中，作者强调了在进行优化前测量的重要性。它介绍了几种实用工具，如CPU计时器用于准确记录运算时间，CUDA事件用于更精细地跟踪GPU操作。内置的TensorRT Profiler可以帮助开发者了解模型执行的瓶颈。此外，CUDA Profiler提供了对硬件底层性能的洞察，这对于理解GPU资源使用情况至关重要。第二章“如何优化我的TensorRT性能？”主要涵盖了批量处理（提高数据吞吐量）、流式处理（连续执行多个任务）以及保证线程安全以充分利用多核处理器。作者深入讲解了引擎初始化的最佳实践，特别是关于融合（Fusion）的优化，包括层级融合（如MLP Fusion、PointWise Fusion和QDQ Fusion），这些可以显著减少计算和内存消耗。第三章专门针对单个层的优化，可能涉及调整层参数、选择更适合的计算模式或使用高级配置来提升性能。对于自定义插件的开发者，第四章提供了一套优化方法，包括性能测试、代码结构改进和与TensorRT内建功能的协同工作。最后一章“如何优化Python接口性能？”针对的是那些利用Python API开发的用户，探讨了如何通过优化Python代码、管理内存和合理利用多线程来提高脚本的执行效率。《TensorRT最佳实践指南》是一份全面的资源，无论是初学者还是经验丰富的开发者，都可以从中找到针对性的建议，以最大化TensorRT的性能表现。"

How Do I Measure Performance?

www.nvidia.com

Best Practices For TensorRT Performance SWE-SWDOCTRT-001-BPRC _vTensorRT 7.0.0|3

These types of wall-clock times can be useful for measuring overall throughput and

latency of the application, and for placing inference times in context within a larger

system.

1.3.CUDA Events

One problem with timing on the host exclusively is that it requires host/device

synchronization. Optimized applications may have many inferences running in parallel

on the device with overlapping data movement. In addition, the synchronization itself

adds some amount of noise to timing measurements.

To help with these issues, CUDA provides an Event API. This API allows you to

place events into CUDA streams that will be time-stamped by the GPU as they are

encountered. Differences in timestamps can then tell you how long different operations

took.

The following example code snippet shows computing the time between two CUDA

events:

cudaEvent_t start, end;

cudaEventCreate(&start);

cudaEventCreate(&end);

cudaEventRecord(start, stream);

context->enqueue(batchSize, &buffers[0], stream, nullptr);

cudaEventRecord(end, stream);

cudaEventSynchronize(end);

float totalTime;

cudaEventElapsedTime(&totalTime, start, end);

TensorRT also includes an optional CUDA event in the method

IExecutionContext::enqueue that will be signaled once the input buffers are

free to be reused. This allows the application to immediately start refilling the input

buffer region for the next inference in parallel with finishing the current inference. For

example:

cudaEvent_t inputReady;

cudaEventCreate(&inputReady);

context->enqueue(batchSize, &buffers[0], stream, &inputReady);

cudaEventSynchronize(inputReady);

// At this point we can refill the input buffers, but output buffers may not be

done

1.4.Built-In TensorRT Profiling

To dig deeper into the performance of inference, it requires more fine-grained timing

measurements within the optimized network. The IExecutionContext interface

class provides a method called setProfiler that allows you to write a custom class

implementing the IProfiler interface.

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