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184 lines
9.0 KiB
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=====================================
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Performance Tips for Frontend Authors
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=====================================
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.. contents::
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:local:
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:depth: 2
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Abstract
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========
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The intended audience of this document is developers of language frontends
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targeting LLVM IR. This document is home to a collection of tips on how to
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generate IR that optimizes well. As with any optimizer, LLVM has its strengths
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and weaknesses. In some cases, surprisingly small changes in the source IR
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can have a large effect on the generated code.
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Avoid loads and stores of large aggregate type
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================================================
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LLVM currently does not optimize well loads and stores of large :ref:`aggregate
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types <t_aggregate>` (i.e. structs and arrays). As an alternative, consider
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loading individual fields from memory.
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Aggregates that are smaller than the largest (performant) load or store
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instruction supported by the targeted hardware are well supported. These can
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be an effective way to represent collections of small packed fields.
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Prefer zext over sext when legal
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==================================
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On some architectures (X86_64 is one), sign extension can involve an extra
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instruction whereas zero extension can be folded into a load. LLVM will try to
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replace a sext with a zext when it can be proven safe, but if you have
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information in your source language about the range of a integer value, it can
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be profitable to use a zext rather than a sext.
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Alternatively, you can :ref:`specify the range of the value using metadata
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<range-metadata>` and LLVM can do the sext to zext conversion for you.
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Zext GEP indices to machine register width
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============================================
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Internally, LLVM often promotes the width of GEP indices to machine register
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width. When it does so, it will default to using sign extension (sext)
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operations for safety. If your source language provides information about
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the range of the index, you may wish to manually extend indices to machine
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register width using a zext instruction.
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Other things to consider
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=========================
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#. Make sure that a DataLayout is provided (this will likely become required in
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the near future, but is certainly important for optimization).
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#. Add nsw/nuw flags as appropriate. Reasoning about overflow is
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generally hard for an optimizer so providing these facts from the frontend
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can be very impactful.
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#. Use fast-math flags on floating point operations if legal. If you don't
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need strict IEEE floating point semantics, there are a number of additional
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optimizations that can be performed. This can be highly impactful for
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floating point intensive computations.
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#. Use inbounds on geps. This can help to disambiguate some aliasing queries.
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#. Add noalias/align/dereferenceable/nonnull to function arguments and return
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values as appropriate
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#. Mark functions as readnone/readonly or noreturn/nounwind when known. The
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optimizer will try to infer these flags, but may not always be able to.
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Manual annotations are particularly important for external functions that
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the optimizer can not analyze.
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#. Use ptrtoint/inttoptr sparingly (they interfere with pointer aliasing
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analysis), prefer GEPs
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#. Use the lifetime.start/lifetime.end and invariant.start/invariant.end
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intrinsics where possible. Common profitable uses are for stack like data
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structures (thus allowing dead store elimination) and for describing
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life times of allocas (thus allowing smaller stack sizes).
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#. Use pointer aliasing metadata, especially tbaa metadata, to communicate
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otherwise-non-deducible pointer aliasing facts
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#. Use the "most-private" possible linkage types for the functions being defined
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(private, internal or linkonce_odr preferably)
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#. Mark invariant locations using !invariant.load and TBAA's constant flags
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#. Prefer globals over inttoptr of a constant address - this gives you
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dereferencability information. In MCJIT, use getSymbolAddress to provide
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actual address.
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#. Be wary of ordered and atomic memory operations. They are hard to optimize
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and may not be well optimized by the current optimizer. Depending on your
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source language, you may consider using fences instead.
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#. If calling a function which is known to throw an exception (unwind), use
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an invoke with a normal destination which contains an unreachable
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instruction. This form conveys to the optimizer that the call returns
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abnormally. For an invoke which neither returns normally or requires unwind
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code in the current function, you can use a noreturn call instruction if
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desired. This is generally not required because the optimizer will convert
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an invoke with an unreachable unwind destination to a call instruction.
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#. If you language uses range checks, consider using the IRCE pass. It is not
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currently part of the standard pass order.
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#. For languages with numerous rarely executed guard conditions (e.g. null
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checks, type checks, range checks) consider adding an extra execution or
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two of LoopUnswith and LICM to your pass order. The standard pass order,
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which is tuned for C and C++ applications, may not be sufficient to remove
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all dischargeable checks from loops.
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#. Use profile metadata to indicate statically known cold paths, even if
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dynamic profiling information is not available. This can make a large
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difference in code placement and thus the performance of tight loops.
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#. When generating code for loops, try to avoid terminating the header block of
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the loop earlier than necessary. If the terminator of the loop header
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block is a loop exiting conditional branch, the effectiveness of LICM will
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be limited for loads not in the header. (This is due to the fact that LLVM
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may not know such a load is safe to speculatively execute and thus can't
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lift an otherwise loop invariant load unless it can prove the exiting
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condition is not taken.) It can be profitable, in some cases, to emit such
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instructions into the header even if they are not used along a rarely
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executed path that exits the loop. This guidance specifically does not
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apply if the condition which terminates the loop header is itself invariant,
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or can be easily discharged by inspecting the loop index variables.
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#. In hot loops, consider duplicating instructions from small basic blocks
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which end in highly predictable terminators into their successor blocks.
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If a hot successor block contains instructions which can be vectorized
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with the duplicated ones, this can provide a noticeable throughput
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improvement. Note that this is not always profitable and does involve a
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potentially large increase in code size.
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#. Avoid high in-degree basic blocks (e.g. basic blocks with dozens or hundreds
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of predecessors). Among other issues, the register allocator is known to
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perform badly with confronted with such structures. The only exception to
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this guidance is that a unified return block with high in-degree is fine.
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#. When checking a value against a constant, emit the check using a consistent
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comparison type. The GVN pass *will* optimize redundant equalities even if
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the type of comparison is inverted, but GVN only runs late in the pipeline.
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As a result, you may miss the opportunity to run other important
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optimizations. Improvements to EarlyCSE to remove this issue are tracked in
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Bug 23333.
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#. Avoid using arithmetic intrinsics unless you are *required* by your source
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language specification to emit a particular code sequence. The optimizer
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is quite good at reasoning about general control flow and arithmetic, it is
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not anywhere near as strong at reasoning about the various intrinsics. If
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profitable for code generation purposes, the optimizer will likely form the
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intrinsics itself late in the optimization pipeline. It is *very* rarely
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profitable to emit these directly in the language frontend. This item
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explicitly includes the use of the :ref:`overflow intrinsics <int_overflow>`.
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#. Avoid using the :ref:`assume intrinsic <int_assume>` until you've
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established that a) there's no other way to express the given fact and b)
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that fact is critical for optimization purposes. Assumes are a great
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prototyping mechanism, but they can have negative effects on both compile
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time and optimization effectiveness. The former is fixable with enough
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effort, but the later is fairly fundamental to their designed purpose.
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p.s. If you want to help improve this document, patches expanding any of the
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above items into standalone sections of their own with a more complete
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discussion would be very welcome.
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Adding to this document
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=======================
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If you run across a case that you feel deserves to be covered here, please send
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a patch to `llvm-commits
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<http://lists.cs.uiuc.edu/mailman/listinfo/llvm-commits>`_ for review.
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If you have questions on these items, please direct them to `llvmdev
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<http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev>`_. The more relevant
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context you are able to give to your question, the more likely it is to be
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answered.
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