Evaluating architectural ideas on realistic workloads is increasingly challenging due to the prohibitive cost of detailed simulation and the lack of portable sampling tools. Existing targeted sampling techniques are often tied to specific binaries, incur significant overhead, and make rapid validation across systems infeasible. To address these limitations, we introduce Nugget, a flexible framework that enables portable sampling across simulators, hardware, architectural differences, and libraries. Nugget leverages LLVM IR to perform binary-independent interval analysis, then generates lightweight, cross-platform executable snippets (nuggets), that can be validated natively on real hardware before use in simulation. This approach decouples samples from specific binaries, dramatically reduces analysis overhead, and allows researchers to iterate on sampling methodologies while efficiently validating samples across diverse systems.

翻译：在真实工作负载上评估体系结构思想正变得日益困难，这既源于详细模拟的过高成本，也由于缺乏可移植的采样工具。现有的定向采样技术通常与特定二进制文件绑定，会产生显著开销，并且难以实现跨系统的快速验证。为应对这些局限，我们提出了Nugget——一个灵活的框架，能够在模拟器、硬件、体系结构差异及库之间实现可移植采样。Nugget利用LLVM IR执行与二进制无关的区间分析，随后生成轻量级、跨平台的可执行代码片段（nuggets）。这些片段可在实际硬件上进行原生验证，之后再用于模拟。该方法将采样样本与特定二进制文件解耦，显著降低了分析开销，并使研究人员能够在迭代优化采样方法的同时，高效地在不同系统间验证样本。