CANN流水线并行推理与资源调度优化

2026-02-08 11:13:39 栏目：最新资讯 3 阅读

在大规模AI推理场景中，如何充分利用多核硬件资源、提升整体吞吐量是一个关键挑战。CANN提供的流水线并行推理技术，通过将推理过程划分为多个阶段，让不同阶段在不同的计算单元上并行执行，实现了硬件资源的最大化利用。本文将深入剖析流水线并行推理的架构设计、资源调度策略，以及在实际应用中的优化技巧。

相关链接：CANN 组织:https://atomgit.com/cann

parser 仓库:https://atomgit.com/cann/parser

一、流水线并行推理基础
1.1 流水线并行的基本概念
流水线并行是将一个完整的推理任务分解为多个连续的阶段，每个阶段在不同的计算单元上并行执行。当一个请求在某个阶段处理完成后，立即进入下一个阶段，同时该计算单元开始处理下一个请求的同一阶段。

流水线并行的核心优势在于：提高硬件资源利用率、提升整体吞吐量、降低平均延迟、支持更高并发。通过合理的阶段划分和资源分配，可以将吞吐量提升2-4倍，同时保持甚至降低单个请求的延迟。

1.2 流水线并行的性能模型
流水线并行的性能可以用Amdahl定律来建模。假设整个推理任务可以划分为N个阶段，第i个阶段的执行时间为Ti，理想情况下，流水线的吞吐量为1/max(Ti)。实际情况下，由于流水线的填充和排空开销，吞吐量会略低于理想值。

流水线的延迟由两部分组成：单个请求的处理时间和流水线的深度。单个请求的处理时间是所有阶段执行时间的总和。流水线的深度是指同时有多少个请求在流水线中并行处理。

流水线的吞吐量提升取决于流水线的并行度和各阶段的负载均衡。如果某个阶段的执行时间显著长于其他阶段，那么这个阶段就成为流水线的瓶颈，限制了整体的吞吐量。

二、流水线阶段划分
2.1 典型的阶段划分
典型的推理流程可以划分为以下几个阶段：数据预处理、模型推理、结果后处理。

数据预处理阶段负责将原始输入数据转换为模型所需的格式，如图像归一化、文本tokenization等。模型推理阶段执行实际的模型计算，这是最耗时的阶段。结果后处理阶段将模型输出转换为最终结果，如softmax、阈值过滤等。

除了这三个基本阶段，还可以根据实际情况增加其他阶段，如数据增强、特征提取、结果聚合等。

2.2 阶段划分的原则
阶段划分需要遵循几个关键原则：各阶段的计算量相对均衡、阶段间的数据传输量最小、各阶段的资源需求匹配硬件、阶段的独立性便于并行执行。

计算量均衡可以避免流水线瓶颈，数据传输量最小可以减少通信开销，资源需求匹配可以充分利用硬件，独立性便于可以简化调度逻辑。

三、资源调度策略
3.1 静态资源分配
静态资源分配是在系统启动时，为每个阶段分配固定的资源。这种方式简单直观，但灵活性较差。

静态资源分配需要考虑各阶段的资源需求和硬件的资源配置。常见的分配策略包括：基于历史数据的分配、基于负载预测的分配、基于优先级的分配。
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