pc = 0
		}
	}()

	t.initFileMap()
	filenum, ok := t.fileMap[file]
	if !ok {
		return 0
	}

	// Scan all functions.
	// If this turns out to be a bottleneck, we could build a map[int32][]int32
	// mapping file number to a list of functions with code from that file.
	var cutab []byte
	for i := uint32(0); i < t.nfunctab; i++ {
		f := t.funcData(i)
		entry := f.entryPC()

	//
	// However, in practice, setting c above 4 tends not to help very much.
	// See the analysis in CL 41192.
	//
	// TODO(josharian): attempt to detect whether this particular compilation
	// is likely to be a bottleneck, e.g. when:
	//   - it has no successor packages to compile (usually package main)
	//   - all paths through the build graph pass through it
	//   - critical path scheduling says it is high priority

using SideEffectsByResourceId = std::map<ResourceId, SideEffects>;

// We use `std::unordered_map` here for pointer stability reasons.
// Note: If memory usage ever becomes a bottleneck here (not expected) we could
// use a Trie-like data structure to avoid storing side effects in both parent
// op and all its child ops (recursively), at the expense of lookup time.

		// is we'd need to wait on the result of a create to record the pod's
		// UID, which would require locking *across* the create, which will turn
		// into a performance bottleneck. We should generate a UID for the pod
		// beforehand and store it via ExpectCreations.
		rsc.expectations.ExpectCreations(logger, rsKey, diff)

			seen[fn] = true
		}
	}
	p.Function = fns
}

// remapMappingIDs matches location addresses with existing mappings
// and updates them appropriately. This is O(N*M), if this ever shows
// up as a bottleneck, evaluate sorting the mappings and doing a
// binary search, which would make it O(N*log(M)).
func (p *Profile) remapMappingIDs() {
	// Some profile handlers will incorrectly set regions for the main

Build scans give you a visual timeline of task execution. In the following
example build, you can see long-running tasks at the beginning and end of the build:

image::performance/parallel-task-slow.png[title="Bottleneck in parallel execution"]

Tweaking the build configuration to run the two slow tasks early on and in parallel
reduces the overall build time from 8 seconds to 5 seconds: