Coz is a causal profiler for native code (C/C++, Rust, Go, Swift, Java) that uses virtual speedup experiments to identify which lines of code, if optimized, would most improve program throughput or latency. It operates by injecting a shared library (libcoz) via LD_PRELOAD or DYLD_INSERT_LIBRARIES, interposing synchronization primitives to track blocking and apply controlled delays on non-selected threads. The profiler outputs a JSON Lines profile that can be visualized with a built-in web viewer (D3.js scatter plots with loess smoothing) or analyzed via CLI. It includes an AI-assisted feature for suggesting progress point placement and optimization advice. The architecture is a hybrid: a core runtime library in C++ with language bindings for Rust, Go, Swift, and Java (JVMTI agent), plus a Python CLI that orchestrates profiling, visualization, and LLM integration. The project is a research artifact from UMass Amherst, published at SOSP 2015, and remains actively developed with modern features like streaming AI support.
Likwid is a comprehensive performance monitoring and benchmarking tool suite for HPC environments. It provides command-line tools (likwid-perfctr, likwid-pin, likwid-bench, likwid-powermeter, likwid-topology, etc.) and a C/Fortran/Lua library for reading hardware performance counters, managing CPU/GPU topology, controlling system features (turbo, prefetchers, RAPL power limits), and pinning threads. The architecture is modular: a core C library (liblikwid) handles low-level hardware access via MSR, PCI, MMIO, or a privileged daemon, while Lua scripts provide user-facing interfaces. External dependencies (Lua, hwloc, GOTCHA, cwisstable) are bundled. The codebase supports x86, ARM, POWER, and GPU (NVIDIA, AMD) architectures with per-architecture performance counter definitions. It is primarily an application tool, though its extensive hardware counter definitions and benchmark infrastructure also serve research purposes.