2025/09/02 by Qiu, Zhantong, Mahyar Samani, Jason Lowe-Power +2
Computer Science · Decision Sciences · #Embedded Systems Design Techniques #Parallel Computing and Optimization Techniques #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2509.02873
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.