Step 2: How it works
Given the topic complexity and the length of this article I have split it in 3 three different blog-post:
How PGO works: PGO is a two-pass build. First pass compiles with instrumentation (-fprofile-generate): every basic block and branch gets a counter. You run a training workload, counters are dumped to profraw files. Second pass recompiles using those counts to drive inlining decisions, branch layout (hot path falls through, cold path jumps away), hot/cold function splitting, code ordering for icache/iTLB locality, loop unrolling, and indirect-call promotion. Crucially, PGO is not “make the trained workload fast” it’s “tell the compiler which code is hot and which is cold, and let it reshape the whole binary accordingly.”
But how does it work?
Phase 1: what actually gets recorded. The instrumented binary has counters injected at compile time one per edge in the control-flow graph, not just per function. So for every branch, every loop back-edge, every call site, there’s a counter that increments each time execution takes that path. This is finer-grained than “function X was called N times” it’s “when we reached this branch, we went left 950,000 times and right 50 times.” That per-edge granularity is what lets the second phase make surgical decisions rather than just “function X is hot, function Y is cold.”
Phase 2: what the compiler does with those counts. Several distinct transformations, all driven by the same counter data:
Inlining. Normally the compiler inlines based on static heuristics:
- function size call-site count
- estimated cost/benefit.
With profile data it can override those heuristics: a call site executed millions of times gets inlined even if it looks “too expensive” by static cost rules, because the runtime benefit clearly outweighs the code-size cost.
A call site that’s technically inlinable but sits in dead-cold code gets left as a real call inlining it would only bloat the binary for no benefit.
Branch layout. Every “if” in our code compiles down to a branch instruction with two possible outcomes:
- continue straight to the next instruction
- jump somewhere else.
Continuing straight is basically free; the CPU is already fetching instructions in order, so there’s no extra cost. Jumping is not free: the CPU has to guess in advance which way a branch will go so it can keep fetching ahead of time, and if it guesses wrong, it has to throw away the work it already queued up and start over from the right place.
That is the “pipeline bubble,” a small stall. So the “straight through” path is cheap and the “jump elsewhere” path carries a penalty when the guess is wrong.
The compiler arranges the hot path as the fall-through and pushes the cold path out of line; literally relocated to a separate location in the binary, often into a .text.unlikely section. So an if (unlikely_error_condition) { …rare handling… } block doesn’t sit inline interrupting the hot path anymore; it is moved somewhere else entirely, and the hot path becomes a straight run of instructions with no diversion.
To be clear, reordering our if/else in the source code usually doesn’t change how the compiler lays out the machine code. Optimizing compilers decide branch layout themselves based on either profile data (PGO) or static heuristics, not on which branch we happened to write first in the source. So swapping the order of our if blocks by hand generally has little to no effect on the compiled result.
Hot/cold function splitting. This is the same idea applied within a single function. A function might have a hot core loop and a rarely-hit error-handling tail. The compiler physically splits the function into two pieces:
- the hot part stays in .text.hot
- the cold part moves to .text.unlikely.
The function still works identically (a jump connects them when needed), but now the hot part is smaller and denser, so more of it fits in an instruction-cache line, and cold code that’s almost never touched isn’t wasting icache space sitting next to it.
Whole-binary function reordering. This is where “reshape the whole binary” becomes literal. At link time (especially with LTO, which MySQL’s PGO build enables), functions get physically reordered in the final executable so that functions which call each other frequently, or execute in sequence during a hot workload, are placed near each other in memory. This maximizes instruction-cache and iTLB locality; the CPU’s fetch unit is pulling in a tight cluster of hot functions instead of jumping all over a 100+MB binary. For something the size of mysqld, this is often the single biggest win, because normal builds place functions in whatever order the source files happen to be compiled, which has no relationship to runtime call patterns.
Indirect-call promotion. If profile data shows a virtual call or function-pointer call resolves to the same target the overwhelming majority of the time (common in C++ with vtables, e.g. a storage-engine interface with basically only InnoDB registered), the compiler inserts a guarded direct call: “if target == this specific address, call it directly and skip the indirect jump; otherwise fall back to the indirect call.” Direct calls are cheaper and more predictable for the branch predictor than an indirect jump through a table.
Register allocation and code density trade-offs. Hot code gets compiled favoring speed, more aggressive unrolling, more registers dedicated to hot-path values. Cold code, especially with -fprofile-partial-training and cold-path treatment, gets compiled favoring size, fewer registers, less unrolling because it barely executes. So runtime cost there is irrelevant but its footprint in the binary is not free (it still occupies disk/page-cache space and can evict hot lines from cache if placed carelessly, which is exactly why it gets segregated into .text.unlikely rather than just left unoptimized in place).
Switch/jump-table lowering. A switch statement with many cases can be compiled as a jump table (fast, O(1), but requires a full table load and indirect jump) or as a cascade of compares (slower per-case but better branch prediction if one case dominates). Profile data tells the compiler which case actually dominates in practice and picks accordingly.
Given the above, “reshape the whole binary” is not metaphorical. The compiler is redrawing the physical layout of machine code in the executable: which instructions are adjacent to which, which code sections are hot and packed tightly versus cold and shoved to the side, which calls are direct versus indirect, and where the CPU’s fetch/prediction effort gets spent. None of this requires the values processed during training to resemble production traffic. It only requires the shape of control flow. Which branches, functions, and paths are frequently exercised to resemble production traffic. That’s the core reason MTR’s broad-but-different-data coverage transfers well: it walks nearly every code path in mysqld even though the actual queries and data are nothing like a TPCC workload, and control-flow shape is exactly what PGO optimizes for.