Kineto is a production-grade C++ library for GPU and CPU performance profiling, primarily embedded in PyTorch's profiler. It provides low-overhead timeline tracing of GPU kernels, memory operations, synchronization events, and CPU-side activities, with support for CUDA (CUPTI), ROCm (rocprofiler-sdk), and Intel XPU (PTI) backends. The library supports both synchronous (manual start/stop) and asynchronous (on-demand, daemon-triggered) profiling modes, and emits Chrome Trace JSON and in-memory trace buffers for downstream analysis. It is not a standalone application; it is a linkable library with a public C API and plugin interfaces for custom profilers and loggers. Architecturally, Kineto is organized around a central ActivityProfilerController that drives a warmup/collect/process state machine, delegating platform-specific capture to backend profilers derived from GenericActivityProfiler. Configuration flows through a ConfigLoader that polls files or an IPC fabric daemon, and activity data flows from vendor callback/activity APIs through correlation-ID-based flow tracking into typed metadata and trace spans. The codebase demonstrates strong software engineering practices: extensive unit tests, CI matrices for CPU/CUDA/ROCm/XPU, clean interface abstractions, and reusable patterns such as observer-based logging, factory-based output registration, and type-safe metadata visitors. However, heavy conditional compilation, vendor SDK version guards, and platform-specific ifdefs introduce maintenance complexity and moderate technical drift risk.