In this paper, we introduce Choreographer, a simulation framework that enables a holistic system-level evaluation of fine-grained accelerators designed for latency-sensitive tasks. Unlike existing frameworks, Choreographer captures all hardware and software overheads in core-accelerator and cache-accelerator interactions, integrating a detailed gem5-based hardware stack featuring an AMBA coherent hub interface (CHI) mesh network and a complete Linux-based software stack. To facilitate rapid prototyping, it offers a C++ application programming interface and modular configuration options. Our detailed cache model provides accurate insights into performance variations caused by cache configurations, which are not captured by other frameworks. The framework is demonstrated through two case studies: a data-aware prefetcher for graph analytics workloads, and a quicksort accelerator. Our evaluation shows that the prefetcher achieves speedups between 1.08x and 1.88x by reducing memory access latency, while the quicksort accelerator delivers more than 2x speedup with minimal address translation overhead. These findings underscore the ability of Choreographer to model complex hardware-software interactions and optimize performance in small task offloading scenarios.

翻译：本文介绍了Choreographer，一种仿真框架，旨在对面向延迟敏感任务的细粒度加速器进行全系统级评估。与现有框架不同，Choreographer全面捕捉了核心-加速器与缓存-加速器交互中的所有硬件与软件开销，集成了基于gem5的详细硬件栈（包含AMBA一致性集线器接口（CHI）网状网络）以及完整的基于Linux的软件栈。为支持快速原型设计，该框架提供了C++应用程序编程接口和模块化配置选项。其精细的缓存模型能够准确反映由缓存配置引起的性能变化，这是其他框架所无法捕捉的。通过两个案例研究展示了该框架的应用：面向图分析工作负载的数据感知预取器，以及快速排序加速器。评估结果表明，预取器通过降低内存访问延迟实现了1.08倍至1.88倍的加速，而快速排序加速器在地址转换开销极小的前提下实现了超过2倍的加速。这些发现凸显了Choreographer在建模复杂软硬件交互及优化小任务卸载场景性能方面的能力。