std::pair<
std::vector<std::unique_ptr<UnitEmitter>>,
std::unique_ptr<ArrayTypeTable::Builder>
>
whole_program(std::vector<std::unique_ptr<UnitEmitter>> ues,
int num_threads) {
trace_time tracer("whole program");
RuntimeOption::EvalLowStaticArrays = false;
if (num_threads > 0) {
parallel::num_threads = num_threads;
}
LitstrTable::get().setReading();
auto program = parse_program(std::move(ues));
state_after("parse", *program);
Index index{borrow(program)};
if (!options.NoOptimizations) {
assert(check(*program));
constant_pass(index, *program);
analyze_iteratively(index, *program, AnalyzeMode::NormalPass);
if (options.AnalyzePublicStatics) {
analyze_public_statics(index, *program);
analyze_iteratively(index, *program, AnalyzeMode::NormalPass);
}
final_pass(index, *program);
state_after("optimize", *program);
}
if (options.AnalyzePublicStatics) {
mark_persistent_static_properties(index, *program);
}
debug_dump_program(index, *program);
print_stats(index, *program);
LitstrTable::get().setWriting();
ues = make_unit_emitters(index, *program);
return { std::move(ues), std::move(index.array_table_builder()) };
}
void optimize(Index& index, php::Program& program) {
assert(check(program));
trace_time tracer("optimize");
SCOPE_EXIT { state_after("optimize", program); };
// Counters, just for debug printing.
std::atomic<uint32_t> total_funcs{0};
auto round = uint32_t{0};
/*
* Algorithm:
*
* Start by running an analyze pass on every function. During
* analysis, information about functions or classes will be
* requested from the Index, which initially won't really know much,
* but will record a dependency. This part is done in parallel: no
* passes are mutating anything, just reading from the Index.
*
* After a pass, we do a single-threaded "update" step to prepare
* for the next pass: for each function that was analyzed, note the
* facts we learned that may aid analyzing other functions in the
* program, and register them in the index. At this point, if any
* of these facts are more useful than they used to be, add all the
* Contexts that had a dependency on the new information to the work
* list again, in case they can do better based on the new fact.
*
* Repeat until the work list is empty.
*/
auto work = initial_work(program);
while (!work.empty()) {
auto const results = [&] {
trace_time trace(
"analyzing",
folly::format("round {} -- {} work items", round, work.size()).str()
);
return parallel_map(
work,
[&] (const Context& ctx) -> folly::Optional<FuncAnalysis> {
total_funcs.fetch_add(1, std::memory_order_relaxed);
return analyze_func(index, ctx);
}
);
}();
work.clear();
++round;
trace_time update_time("updating");
std::set<Context> revisit;
for (auto i = size_t{0}; i < results.size(); ++i) {
auto& result = *results[i];
assert(result.ctx.func == work[i].func);
assert(result.ctx.cls == work[i].cls);
assert(result.ctx.unit == work[i].unit);
auto deps = index.refine_return_type(
result.ctx.func, result.inferredReturn
);
for (auto& d : deps) revisit.insert(d);
}
std::copy(begin(revisit), end(revisit), std::back_inserter(work));
}
if (Trace::moduleEnabledRelease(Trace::hhbbc_time, 1)) {
Trace::traceRelease("total function visits %u\n", total_funcs.load());
}
/*
* Finally, use the results of all these iterations to perform
* optimization. This reanalyzes every function using our
* now-very-updated Index, and then runs optimize_func with the
* results.
*
* We do this in parallel: all the shared information is queried out
* of the index, and each thread is allowed to modify the bytecode
* for the function it is looking at.
*
* NOTE: currently they can't modify anything other than the
* bytecode/Blocks, because other threads may be doing unlocked
* queries to php::Func and php::Class structures.
*/
trace_time final_pass("final pass");
work = initial_work(program);
parallel_for_each(
initial_work(program),
[&] (Context ctx) {
optimize_func(index, analyze_func(index, ctx));
}
);
}
