一句话：为 RAPID 模式匹配程序和有限自动机加速器提供无需改硬件的交互式步进调试与变量检查支持。
有限自动机处理加速器上的模式匹配程序通常难以调试，尤其缺乏单步执行等交互式支持，导致故障定位和程序维护困难。本文针对 RAPID 领域特定语言，提出一种可在现有商用硬件上实现的调试方法，支持用户编写的自动机处理程序进行步进调试与变量检查。作者为 Xilinx FPGA 和 Micron Automata Processor 构建原型，在不修改底层硬件的情况下同时进行高速数据处理与交互式调试。评估在 ANMLZoo 基准的 13 个 FPGA 应用上显示较低时钟开销；一项超过 60 名参与者、20 段缺陷代码的人因研究表明，生成的调试信息将故障定位准确率提高 22%（10 个百分点），并具统计显著性（p=0.013）。
Programs written for hardware accelerators can often be difficult to debug. Without adequate tool support, program maintenance tasks such as fault localization and debugging can be particularly challenging. In this work, we focus on supporting hardware that is specialized for finite automata processing, a computational paradigm that has accelerated pattern-matching applications across a diverse set of problem domains. While commodity hardware enables high-throughput data analysis, direct interactive debugging (e.g., single-stepping) is not currently supported. We propose a debugging approach for existing commodity hardware that supports step-through debugging and variable inspection of user-written automata processing programs. We focus on programs written in RAPID, a domain-specific language for pattern-matching applications. We develop a prototype of our approach for both Xilinx FPGAs and Micron's Automata Processor that supports simultaneous high-speed processing of data and interactive debugging without requiring modifications to the underlying hardware. Our empirical evaluation demonstrates low clock overheads for our approach across thirteen applications in the ANMLZoo automata processing benchmark suite on FPGAs. Additionally, we evaluate our technique through a human study involving over 60 participants and 20 buggy segments of code. Our generated debugging information increases fault localization accuracy by 22%, or 10 percentage points, in a statistically significant manner (p=0.013).
@inproceedings{paperbot4877,
title = {Debugging Support for Pattern-Matching Languages and Accelerators},
author = {Matthew Casias and Kevin Angstadt and Tommy Tracy II and Kevin Skadron and Westley Weimer},
booktitle = {ASPLOS},
year = {2019},
doi = {10.1145/3297858.3304066}
}