Performance-evaluation pipelines in systems research often combine benchmarks, system configuration steps, profiling tools, and analysis scripts. In practice, these components are glued together with ad-hoc shell scripts, notebooks, and bespoke tooling, making experiment dimensions difficult to explore systematically and results hard to reproduce or extend. We present benchkit, a lightweight Python library that provides a structured way to express performance experiments declaratively and to automate their full lifecycle—from build and execution to system configuration, profiling, and result collection. Instead of relying on monolithic scripts, benchkit provides a structured way to compose existing system tools (e.g., CPU-placement utilities, frequency controllers, and performance profilers) while keeping benchmark code untouched. We illustrate benchkit through two representative studies: (1) a drilldown of performance anomalies in SPEC CPU workloads on hybrid-core x86 processors, enabled by systematic exploration of CPU placement policies; and (2) an analysis of lock implementations and scheduling strategies on a many-core ARM server, where benchkit coordinates system tools and visualizations to interpret performance differences. We evaluate the overhead of benchkit and show that it introduces no measurable cost compared to hand-written shell workflows, both on the host and inside containers. These results show that benchkit provides a reproducible, extensible, and principled foundation for system-level performance experimentation.

In this paper, we investigate a consensus-based distributed Kalman filter for single-target tracking over loopy communication graphs using Gaussian belief propagation. Each node (tracker) maintains a local Kalman filter and exchanges Gaussian messages with its neighbors on a factor–graph, implementing a distributed fusion of measurement increments. Instead of the usual static factor precision$L_{ij} = \beta I$, we introduce state-dependent factor precisions that adapt to the local uncertainty and network topology. We further derive simple local bounding rules that guarantee walk-summability of an associated Gaussian surrogate model. In simulations with range-dependent measurement noise and increasingly dense communication graph topologies, a proposed dynamic precision achieves better normalized estimation error square calibration and mean-squared error than static baselines, and remains resilient even in over-connected networks.

The automatic translation of legacy C code to Rust presents significant challenges, particularly in handling preprocessor macros. C macros introduce metaprogramming constructs that operate at the text level, outside of C's syntax tree, making their direct translation to Rust non-trivial. Existing transpilers --- source-to-source compilers --- expand macros before translation, sacrificing their abstraction and reducing code maintainability. In this work, we introduce Oxidize, a macro-aware C-to-Rust transpilation framework that preserves macro semantics by translating C macros into Rust-compatible constructs while selectively expanding only those that interfere with Rust's stricter semantics. We evaluate our techniques on a small-scale study of real-world macros and find that the majority can be safely and idiomatically transpiled without full expansion.