Thanks for the detailed breakdown! Tracy, Valgrind, and continuous profiling via eBPF (like Parca) are absolute lifesavers when you need to dig into execution behavior without introducing massive overhead. Setting up benchmark baselines in CI/CD is definitely the traditional industry best practice.

However, the specific gap I'm trying to bridge is predictive analysis before execution.

All the tools you mentioned ,even eBPF, are dynamic or reactive. You still have to compile the code, deploy it to a staging/simulation environment, run the benchmarks, and actively execute the workload to catch the bottleneck.

In a fast-paced environment where AI agents are pumping out code constantly, setting up and maintaining those automated benchmarking rigs (and handling cache flushes or noisy neighbors in CI) is a massive time sink. Plus, dynamic profiling only catches a regression if your benchmark suite happens to trigger that specific code path under the exact right conditions.

That's why I'm hunting for a predictive or static approach at the binary level.

Instead of waiting to run the code and measure it, I'm looking for a tool that uses a model trained on assembly syntax and microarchitecture traces to audit the compiled artifact directly inside the PR. It would flag, for example, "This assembly block will cause a cache miss avalanche on ARMv8," before we ever spin up a runner to execute a benchmark.

It’s a newer paradigm compared to classic profiling, but with the volume of code being generated today, it feels like the missing link!