Benchmarks shouldn't live in a different world from tests. Most C++ projects already use Google Test, but the moment performance enters the picture, developers reach for separate tools, separate workflows, and ad-hoc timing loops that don't survive contact with CI. This talk argues for a different default: treat performance measurements as first-class tests. They are written in the same harness, run in the same suite, and fail the same builds.
Using a real C++23 framework built on top of Google Test, the session shows what that looks like in practice. Semantic macros distinguish throughput, latency, and contention tests; built-in statistical analysis tracks medians, percentiles, and coefficient of variation. Adaptive thresholds scale with payload size, so tiny operations and multi-megabyte workloads aren't held to the same stability expectations.
Once benchmarking lives inside the test framework, the rest of the performance toolchain follows. Five CPU profiler backends drop in behind a single --profile flag: perf, gperftools, bpftrace, RAPL, and callgrind. Attaching a memory profile to a test prints bandwidth, efficiency, and a CPU-bound or memory-bound classification with no extra code. The same harness extends to CUDA through a fluent kernel builder that captures launch configuration, achieved occupancy, transfer overhead, multi-GPU scaling efficiency, and thermal throttling. Nsight Compute drops in through the same --profile flag.
The payoff is CI integration that's nearly automatic. A companion CLI tool compares baseline and candidate runs, applies statistical thresholds, posts markdown reports on pull requests, and fails the build on regressions. The result is a single workflow for performance verification that scales from a developer's laptop to a CI runner to a Jetson on a workbench, across hardware ranging from x86 to ARM to RISC-V.
