How fast? (particularly compared to the first implementation, and relative to -fsyntax-only)
On Aug 28, 2012, at 11:48 AM, Richard Trieu <[email protected]> wrote: > New patch with PointerUnion and DenseMap is slightly faster than the previous > DenseMap patch. > > On Mon, Aug 27, 2012 at 9:51 PM, Ted Kremenek <[email protected]> wrote: > Thanks! Quick question before I review it in more details: what is the > performance characteristics of this patch compared to the others? > > On Aug 27, 2012, at 11:35 AM, Richard Trieu <[email protected]> wrote: > >> Incorporated most of the suggestions into this patch. Still using a double >> pass over the constants for the reasons outlined below. >> >> On Fri, Aug 17, 2012 at 10:00 PM, Ted Kremenek <[email protected]> wrote: >> BTW, I wrote this two days ago. For some reason my mail client didn't send >> it out until now. My apologies for the delay. >> No worries. Had some issues that cropped up on template diffing that took >> my time. >> >> On Aug 15, 2012, at 10:11 PM, Ted Kremenek <[email protected]> wrote: >> >>> On Aug 15, 2012, at 6:12 PM, Richard Trieu <[email protected]> wrote: >>> >>>> On Tue, Aug 14, 2012 at 9:48 PM, Ted Kremenek <[email protected]> wrote: >>>> On Aug 14, 2012, at 2:32 PM, Richard Trieu <[email protected]> wrote: >>>> >>>>> At a high level, I honestly find this logic to be more complicated than I >>>>> would have expected. The sorting seems unnecessary, and will report >>>>> diagnostics in an unnatural order (first based on enum constant value, >>>>> then on declaration order). A straight linear pass seems more naturally >>>>> to me, and DenseMap is very efficient. >>>>> Is there a comparison between the different containers in LLVM and the >>>>> STL containers? >>>> >>>> This is a reasonable place to start: >>>> >>>> http://llvm.org/docs/ProgrammersManual.html#ds_map >>>> >>>> The key with DenseMap is that it is probed hashtable. There is one big >>>> allocation for the entire table, instead of a bunch of buckets. When >>>> applicable, it can be very fast, and feels like the right data structure >>>> to use here. >>>> >>>> Duplicate enum detection, now with DenseMap. The DenseMap maps a int64_t >>>> to a vector pointer. 0 and 1 were special keys for the DenseMap, so two >>>> separate pointers special cased for them. The vectors pointers are >>>> stored in another vector in declaration order. One pass is made over the >>>> enums to find ones without initializers. These are used to create >>>> vectors. A second pass through the enums populates the vectors. Finally, >>>> a pass over the vector of vectors is used to generate all the warnings and >>>> notes. >>>> >>>> Run time is fairly consistent with the sorted vector implementation, which >>>> is max %3 difference against control. >>>> <duplicate-enum-densemap.patch> >>> >>> Thanks for working on this. My main concern is this patch now has a lot of >>> unnecessary malloc() traffic, which will certainly slow it down. Comments >>> inline: >>> >>>> + >>>> +static int64_t GetInt64(const llvm::APSInt& Val) { >>>> + return Val.isSigned() ? Val.getSExtValue() : Val.getZExtValue(); >>>> +} >>>> + >>>> +struct DenseMapInfoint64_t { >>>> + static int64_t getEmptyKey() { return 0; } >>>> + static int64_t getTombstoneKey() { return 1; } >>>> + static unsigned getHashValue(const int64_t Val) { >>>> + return (unsigned)(Val * 37); >>>> + } >>>> + static bool isEqual(const int64_t& LHS, const int64_t& RHS) { >>>> + return LHS == RHS; >>>> + } >>>> +}; >>> >>> This trait class doesn't look like it was actually used. The DenseMap >>> below just uses the default trait for int64_t. >>> >>> I also still think we can so something a bit smarter here. What I think we >>> need to distinguish between is whether or not a constant has appeared more >>> than once. We're saving a bit of memory on the keys, but spending that >>> savings elsewhere when we allocate the vectors unconditionally for each >>> constant. >>> >>>> + >>>> +// Emits a warning when an element is implicitly set a value that >>>> +// a previous element has already been set to. >>>> +static void CheckForDuplicateEnumValues(Sema &S, Decl **Elements, >>>> + unsigned NumElements, EnumDecl >>>> *Enum, >>>> + QualType EnumType) { >>>> + if (S.Diags.getDiagnosticLevel(diag::warn_duplicate_enum_values, >>>> + Enum->getLocation()) == >>>> + DiagnosticsEngine::Ignored) >>>> + return; >>>> + // Avoid anonymous enums >>>> + if (!Enum->getIdentifier()) >>>> + return; >>>> + >>>> + // Only check for small enums. >>>> + if (Enum->getNumPositiveBits() > 63 || Enum->getNumNegativeBits() > 64) >>>> + return; >>>> + >>>> + typedef llvm::SmallVector<EnumConstantDecl*, 4> SameValueVector; >>>> + typedef llvm::DenseMap<int64_t, SameValueVector*> ValueToVectorMap; >>>> + typedef llvm::SmallVector<SameValueVector*, 10> DoubleVector; >>>> + ValueToVectorMap EnumMap; >>>> + DoubleVector EnumVector; >>>> + SameValueVector *ZeroVector = 0, *OneVector = 0; >>> >>> It took me a while to understand what this was doing, so I feel it could >>> really benefit from a comment. This also appears to result in a ton of >>> malloc traffic below. Here's my suggestion: >>> >>> typedef llvm::SmallVector<EnumConstantDecl*, 3> ECDVector; >>> typedef llvm::SmallVector<ECDVector *, 3> DuplicatesVector; >>> >>> typedef llvm::PointerUnion<EnumConstantDecl*, ECDVector *> DeclOrVector; >>> typedef llvm::DenseMap<int64_t, DeclOrVector> ValueToVectorMap; >>> >>> DuplicatesVector DupVector; >>> ValueToVectorMap EnumMap; >>> >>> The trick here is that the DenseMap maps from a constant to the first >>> EnumConstantDecl it encounters. Only if we encounter a second >>> EnumConstantDecl with the same enum value do we pay the cost of allocating >>> another vector. This will drastically optimize in the common case, as >>> calling malloc() is really slow. Right now the code appears to be doing a >>> malloc() for every enum constant, which is going to really penalize us here. >>> >>>> + >>>> + for (unsigned i = 0; i < NumElements; ++i) { >>>> + EnumConstantDecl *ECD = cast<EnumConstantDecl>(Elements[i]); >>>> + if (!ECD) { >>>> + for (DoubleVector::iterator I = EnumVector.begin(), E = >>>> EnumVector.end(); >>>> + I != E; ++I) >>>> + delete *I; >>>> + return; >>>> + } >>> >>> I don't quite understand this loop through DoubleVector here, but it looks >>> like logic in case we want to return early and cleanup. Is there a case >>> where the EnumConstantDecl can be null? >> >> According to ActOnEnumBody, EnumConstantDecl is null if a diagnostic has >> previously been emitted for the constant. Since the enum is possibly >> ill-formed, skip checking it. >>> >>>> + >>>> + if (ECD->getInitExpr()) >>>> + continue; >>>> + >>>> + int64_t Val = GetInt64(ECD->getInitVal()); >>>> + >>> >>> Looks good. >>> >>>> + if (Val == 0) { >>>> + if (ZeroVector) continue; >>>> + ZeroVector = new SameValueVector(); >>>> + ZeroVector->push_back(ECD); >>>> + EnumVector.push_back(ZeroVector); >>>> + } else if (Val == 1) { >>>> + if (OneVector) continue; >>>> + OneVector = new SameValueVector(); >>>> + OneVector->push_back(ECD); >>>> + EnumVector.push_back(OneVector); >>>> + } else { >>>> + if (EnumMap.find(Val) != EnumMap.end()) >>>> + continue; >>>> + SameValueVector *ValueVector = new SameValueVector(); >>>> + ValueVector->push_back(ECD); >>>> + EnumVector.push_back(ValueVector); >>>> + EnumMap.insert(std::make_pair(Val, ValueVector)); >>> >>> The "find()" followed by the "insert()" is wasteful. It results in two >>> lookups to the hash table when we could have just used one. More on that >>> later. >>> >>>> + } >>>> + } >>> >>> IMO, this looks like a lot of complexity just to handle the fact that 0 and >>> 1 are special values for the DenseMap. I don't really see this as the >>> right tradeoff; the code is more complicated with marginal impact on memory >>> usage or performance. >>> >>> If you humor me for a bit, consider using something else for the key, e.g.: >>> >>> struct DupKey { >>> int64_t val; >>> bool isTombstoneOrEmptyKey; >>> }; >>> >>> The idea is if 'isTombStoneOrEmptyKey' is true, we can use val = 0 or val = >>> 1 to represent empty keys or tombstone entries. Otherwise, it's an >>> int64_t, with the full range of values. We can define a DenseMap trait to >>> do the right thing. Yes, this costs a tiny bit more in storage, but it >>> allows the data structure to handle the complete set of values in your >>> domain, instead of resorting to complicating the core algorithm. What I >>> see here now is the same code essentially duplicated twice, which makes it >>> harder to read and more error prone. >>> >>> If we use DupKey as our key for the DenseMap, we can instead do something >>> like this: >>> >>> DeclOrVector &entry = EnumMap[Val]; // Use default construction of >>> 'entry'. >>> // Is the first time we encountered this constant? >>> if (entry.isNull()) { >>> entry = ECD; >>> continue; >>> } >>> // Is this the second time we encountered this constant? If so, >>> // push the previous decl encountered and the one just encountered >>> // to a vector of duplicates. >>> if (EnumConstantDecl *D = entry.dyn_cast<EnumConstantDecl*>()) { >>> ECDVector *Vec = new ECDVector(); >>> Vec->push_back(D); >>> Vec->push_back(ECD); >>> >>> // Update the entry to refer to the duplicates. >>> entry = Vec; >>> >>> // Store the duplicates in a vector we can consult later for >>> // quick emission of diagnostics. >>> DupVector.push_back(Vec); >>> >>> // On to the next constant. >>> continue; >>> } >>> // Is this the third (or greater) time we encountered the constant? If >>> so, >>> // continue to add it to the existing vector. >>> ECDVector *Vec = entry.get<ECDVector*>(); >>> Vec->push_back(ECD); >>> >>> >>> With this code, we only allocate memory (beyond the DenseMap) when we >>> encounter a duplicate that would be worth reporting. In the common case, >>> this savings in malloc traffic should be noticeable. >>> >>> Notice also that I used: >>> >>> DeclOrVector &entry = EnumMap[Val]; // Use default construction of >>> 'entry'. >>> >>> This results in a single lookup in the hashtable. Since we plan on adding >>> a value for a key no matter what, by using this idiom we allow the DenseMap >>> to default construct an entry if it doesn't exist. This results in a >>> single hashtable lookup, from which we can modify the value in place. This >>> is obviously faster than doing a hashtable lookup twice. >>> >>>> + >>>> + for (unsigned i = 0; i < NumElements; ++i) { >>>> + EnumConstantDecl *ECD = cast<EnumConstantDecl>(Elements[i]); >>>> + if (!ValidDuplicateEnum(ECD, Enum)) >>>> + continue; >>>> + >>>> + int64_t Val = GetInt64(ECD->getInitVal()); >>>> + >>>> + if (Val == 0) { >>>> + if (!ZeroVector || *ZeroVector->begin() == ECD) >>>> + continue; >>>> + ZeroVector->push_back(ECD); >>>> + } else if (Val == 1) { >>>> + if (!OneVector || *OneVector->begin() == ECD) >>>> + continue; >>>> + OneVector->push_back(ECD); >>>> + } else { >>>> + ValueToVectorMap::iterator I = EnumMap.find(Val); >>>> + if (I == EnumMap.end()) >>>> + continue; >>>> + SameValueVector *V = I->second; >>>> + if (*V->begin() == ECD) >>>> + continue; >>>> + V->push_back(ECD); >>>> + } >>>> + } >>> >>> This second loop looks unnecessary. I think we can do everything we need >>> to count duplicates with one loop. Of course the ValidDuplicateEnum() >>> would need to be hoisted to the first loop. >> >> Using two traverses allows two things to happen. One, the first element in >> the ECDVector will not have an initializer and will work with the warning. >> Otherwise, the vector needs to be searched for a proper enum constant to >> use. Two, it prevents unneeded creation of ECDVectors. If we have enum A { >> A1 = 2, A2 = 2, A3 = 1, A4 = 1, A5}; vectors for values 1 and 2 are created >> using a single pass while only a vector for 2 will be created using a double >> pass. >>> >>>> + >>>> + for (DoubleVector::iterator DoubleVectorIter = EnumVector.begin(), >>>> + DoubleVectorEnd = EnumVector.end(); >>>> + DoubleVectorIter != DoubleVectorEnd; ++DoubleVectorIter) { >>>> + SameValueVector *V = *DoubleVectorIter; >>>> + if (V->size() == 1) >>>> + continue; >>>> + >>>> + SameValueVector::iterator I = V->begin(); >>>> + S.Diag((*I)->getLocation(), diag::warn_duplicate_enum_values) >>>> + << (*I)->getName() << (*I)->getInitVal().toString(10) >>>> + << (*I)->getSourceRange(); >>>> + ++I; >>>> + for (SameValueVector::iterator E = V->end(); I != E; ++I) >>>> + S.Diag((*I)->getLocation(), diag::note_duplicate_element) >>>> + << (*I)->getName() << (*I)->getInitVal().toString(10) >>>> + << (*I)->getSourceRange(); >>>> + delete V; >>>> + } >>> >>> >>> This is more or less the same, essentially it becomes: >>> >>> for (DuplicateVector::iterator I = DupVector.begin(), E = DupVector.end(); >>> I != E; ++I) { >>> ECDVector *Vec = *I; >>> // do the diagnostic logic ... >>> delete *I; >>> } >>> >>> Note that with my suggestions the vector has size on order of the number of >>> duplicate constants, not the number of total constants. If there are no >>> duplicates, no work is required (including free'ing memory). >>> >>>> +} >>>> + >>>> void Sema::ActOnEnumBody(SourceLocation EnumLoc, SourceLocation LBraceLoc, >>>> SourceLocation RBraceLoc, Decl *EnumDeclX, >>>> Decl **Elements, unsigned NumElements, >>>> @@ -10709,6 +10868,7 @@ >>>> DeclsInPrototypeScope.push_back(Enum); >>>> >>>> CheckForUniqueEnumValues(*this, Elements, NumElements, Enum, EnumType); >>>> + CheckForDuplicateEnumValues(*this, Elements, NumElements, Enum, >>>> EnumType); >>>> } >>>> >>>> Decl *Sema::ActOnFileScopeAsmDecl(Expr *expr, >>> >>> I know this may all be nit-picky, but I really think trying to reduce the >>> malloc() traffic is worth looking at to get a real understanding of the >>> performance improvement that can be found here. >>> >>> Thanks for forging ahead on this. >>> _______________________________________________ >>> cfe-commits mailing list >>> [email protected] >>> http://lists.cs.uiuc.edu/mailman/listinfo/cfe-commits >> >> >> <duplicate-enum-densemap2.patch> > >
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