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[MemDep] Optimize SortNonLocalDepInfoCache sorting strategy for large caches with few unsorted entries #143107

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nikic merged 2 commits into from
Jul 25, 2025
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[MemDep] Optimize SortNonLocalDepInfoCache sorting strategy for large caches with few unsorted entries #143107

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[MemDep] Optimize SortNonLocalDepInfoCache sorting strategy for large…
DingdWang Jun 26, 2025
fec951d
fix comments from nikic
DingdWang Jul 25, 2025
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44 changes: 24 additions & 20 deletions llvm/lib/Analysis/MemoryDependenceAnalysis.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -983,33 +983,37 @@ MemDepResult MemoryDependenceResults::getNonLocalInfoForBlock(
static void
SortNonLocalDepInfoCache(MemoryDependenceResults::NonLocalDepInfo &Cache,
unsigned NumSortedEntries) {
switch (Cache.size() - NumSortedEntries) {
case 0:
// done, no new entries.
break;
case 2: {
// Two new entries, insert the last one into place.
NonLocalDepEntry Val = Cache.back();
Cache.pop_back();
MemoryDependenceResults::NonLocalDepInfo::iterator Entry =
std::upper_bound(Cache.begin(), Cache.end() - 1, Val);
Cache.insert(Entry, Val);
[[fallthrough]];

// If only one entry, don't sort.
if (Cache.size() < 2)
return;

unsigned s = Cache.size() - NumSortedEntries;

// If the cache is already sorted, don't sort it again.
if (s == 0)
return;

// If no entry is sorted, sort the whole cache.
if (NumSortedEntries == 0) {
llvm::sort(Cache);
return;
}
case 1:
// One new entry, Just insert the new value at the appropriate position.
if (Cache.size() != 1) {

// If the number of unsorted entires is small and the cache size is big, using
// insertion sort is faster. Here use Log2_32 to quickly choose the sort
// method.
if (s < Log2_32(Cache.size())) {
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@DingdWang DingdWang Jun 26, 2025

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The choice of using log2 here is based on empirical experience. The main goal is to have a relatively fast way to determine whether the number of unsorted entries is significantly smaller than the cache size. To tune this condition, I experimented with the following four options, and based on the timing results, using log2 proved to be the fastest. The benchmark results are as follows:

  1. s < NumSortedEntries: https://llvm-compile-time-tracker.com/compare.php?from=26f3f24a4f0a67eb23d255aba7a73a12bee1db11&to=e174118c88ee3d9d31fb3ed4e29b9ae2fcac46fa&stat=instructions%3Au
  2. s < Log2_32(Cache.size()) * llvm::numbers::ln2 / llvm::numbers::ln10: https://llvm-compile-time-tracker.com/compare.php?from=26f3f24a4f0a67eb23d255aba7a73a12bee1db11&to=9368621b42fa8b68e1e3081110f82ae9a5d57458&stat=instructions%3Au
  3. s < Log2_32(Cache.size()) * llvm::numbers::ln2: https://llvm-compile-time-tracker.com/compare.php?from=26f3f24a4f0a67eb23d255aba7a73a12bee1db11&to=19a8584d14dbb95b4a71a92a43da3a2c5d5e550a&stat=instructions%3Au
  4. s < Log2_32(Cache.size()): https://llvm-compile-time-tracker.com/compare.php?from=26f3f24a4f0a67eb23d255aba7a73a12bee1db11&to=0fa6bc6bdf1c9c5464e81970e973f2c43edac874&stat=instructions%3Au

while (s > 0) {
NonLocalDepEntry Val = Cache.back();
Cache.pop_back();
MemoryDependenceResults::NonLocalDepInfo::iterator Entry =
llvm::upper_bound(Cache, Val);
std::upper_bound(Cache.begin(), Cache.end() - s + 1, Val);
Cache.insert(Entry, Val);
s--;
}
break;
default:
// Added many values, do a full scale sort.
} else {
llvm::sort(Cache);
break;
}
}

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