llvm/docs/Frontend/PerformanceTips.rst

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Performance Tips for Frontend Authors
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Abstract
========

The intended audience of this document is developers of language frontends 
targeting LLVM IR. This document is home to a collection of tips on how to 
generate IR that optimizes well.  

IR Best Practices
=================

As with any optimizer, LLVM has its strengths and weaknesses.  In some cases, 
surprisingly small changes in the source IR can have a large effect on the 
generated code.  

Beyond the specific items on the list below, it's worth noting that the most 
mature frontend for LLVM is Clang.  As a result, the further your IR gets from what Clang might emit, the less likely it is to be effectively optimized.  It 
can often be useful to write a quick C program with the semantics you're trying
to model and see what decisions Clang's IRGen makes about what IR to emit.  
Studying Clang's CodeGen directory can also be a good source of ideas.  Note 
that Clang and LLVM are explicitly version locked so you'll need to make sure 
you're using a Clang built from the same svn revision or release as the LLVM 
library you're using.  As always, it's *strongly* recommended that you track 
tip of tree development, particularly during bring up of a new project.

The Basics
^^^^^^^^^^^

#. Make sure that your Modules contain both a data layout specification and 
   target triple. Without these pieces, non of the target specific optimization
   will be enabled.  This can have a major effect on the generated code quality.

#. For each function or global emitted, use the most private linkage type
   possible (private, internal or linkonce_odr preferably).  Doing so will 
   make LLVM's inter-procedural optimizations much more effective.

#. Avoid high in-degree basic blocks (e.g. basic blocks with dozens or hundreds
   of predecessors).  Among other issues, the register allocator is known to 
   perform badly with confronted with such structures.  The only exception to 
   this guidance is that a unified return block with high in-degree is fine.

Use of allocas
^^^^^^^^^^^^^^

An alloca instruction can be used to represent a function scoped stack slot, 
but can also represent dynamic frame expansion.  When representing function 
scoped variables or locations, placing alloca instructions at the beginning of 
the entry block should be preferred.   In particular, place them before any 
call instructions. Call instructions might get inlined and replaced with 
multiple basic blocks. The end result is that a following alloca instruction 
would no longer be in the entry basic block afterward.

The SROA (Scalar Replacement Of Aggregates) and Mem2Reg passes only attempt
to eliminate alloca instructions that are in the entry basic block.  Given 
SSA is the canonical form expected by much of the optimizer; if allocas can 
not be eliminated by Mem2Reg or SROA, the optimizer is likely to be less 
effective than it could be.

Avoid loads and stores of large aggregate type
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

LLVM currently does not optimize well loads and stores of large :ref:`aggregate
types <t_aggregate>` (i.e. structs and arrays).  As an alternative, consider 
loading individual fields from memory.

Aggregates that are smaller than the largest (performant) load or store 
instruction supported by the targeted hardware are well supported.  These can 
be an effective way to represent collections of small packed fields.  

Prefer zext over sext when legal
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

On some architectures (X86_64 is one), sign extension can involve an extra 
instruction whereas zero extension can be folded into a load.  LLVM will try to
replace a sext with a zext when it can be proven safe, but if you have 
information in your source language about the range of a integer value, it can 
be profitable to use a zext rather than a sext.  

Alternatively, you can :ref:`specify the range of the value using metadata 
<range-metadata>` and LLVM can do the sext to zext conversion for you.

Zext GEP indices to machine register width
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Internally, LLVM often promotes the width of GEP indices to machine register
width.  When it does so, it will default to using sign extension (sext) 
operations for safety.  If your source language provides information about 
the range of the index, you may wish to manually extend indices to machine 
register width using a zext instruction.

When to specify alignment
^^^^^^^^^^^^^^^^^^^^^^^^^^
LLVM will always generate correct code if you don’t specify alignment, but may
generate inefficient code.  For example, if you are targeting MIPS (or older 
ARM ISAs) then the hardware does not handle unaligned loads and stores, and 
so you will enter a trap-and-emulate path if you do a load or store with 
lower-than-natural alignment.  To avoid this, LLVM will emit a slower 
sequence of loads, shifts and masks (or load-right + load-left on MIPS) for 
all cases where the load / store does not have a sufficiently high alignment 
in the IR.

The alignment is used to guarantee the alignment on allocas and globals, 
though in most cases this is unnecessary (most targets have a sufficiently 
high default alignment that they’ll be fine).  It is also used to provide a 
contract to the back end saying ‘either this load/store has this alignment, or
it is undefined behavior’.  This means that the back end is free to emit 
instructions that rely on that alignment (and mid-level optimizers are free to 
perform transforms that require that alignment).  For x86, it doesn’t make 
much difference, as almost all instructions are alignment-independent.  For 
MIPS, it can make a big difference.

Note that if your loads and stores are atomic, the backend will be unable to 
lower an under aligned access into a sequence of natively aligned accesses.  
As a result, alignment is mandatory for atomic loads and stores.

Other Things to Consider
^^^^^^^^^^^^^^^^^^^^^^^^

#. Use ptrtoint/inttoptr sparingly (they interfere with pointer aliasing 
   analysis), prefer GEPs

#. Prefer globals over inttoptr of a constant address - this gives you 
   dereferencability information.  In MCJIT, use getSymbolAddress to provide 
   actual address.

#. Be wary of ordered and atomic memory operations.  They are hard to optimize 
   and may not be well optimized by the current optimizer.  Depending on your
   source language, you may consider using fences instead.

#. If calling a function which is known to throw an exception (unwind), use 
   an invoke with a normal destination which contains an unreachable 
   instruction.  This form conveys to the optimizer that the call returns 
   abnormally.  For an invoke which neither returns normally or requires unwind
   code in the current function, you can use a noreturn call instruction if 
   desired.  This is generally not required because the optimizer will convert
   an invoke with an unreachable unwind destination to a call instruction.

#. Use profile metadata to indicate statically known cold paths, even if 
   dynamic profiling information is not available.  This can make a large 
   difference in code placement and thus the performance of tight loops.

#. When generating code for loops, try to avoid terminating the header block of
   the loop earlier than necessary.  If the terminator of the loop header 
   block is a loop exiting conditional branch, the effectiveness of LICM will
   be limited for loads not in the header.  (This is due to the fact that LLVM 
   may not know such a load is safe to speculatively execute and thus can't 
   lift an otherwise loop invariant load unless it can prove the exiting 
   condition is not taken.)  It can be profitable, in some cases, to emit such 
   instructions into the header even if they are not used along a rarely 
   executed path that exits the loop.  This guidance specifically does not 
   apply if the condition which terminates the loop header is itself invariant,
   or can be easily discharged by inspecting the loop index variables.

#. In hot loops, consider duplicating instructions from small basic blocks 
   which end in highly predictable terminators into their successor blocks.  
   If a hot successor block contains instructions which can be vectorized 
   with the duplicated ones, this can provide a noticeable throughput
   improvement.  Note that this is not always profitable and does involve a 
   potentially large increase in code size.

#. When checking a value against a constant, emit the check using a consistent
   comparison type.  The GVN pass *will* optimize redundant equalities even if
   the type of comparison is inverted, but GVN only runs late in the pipeline.
   As a result, you may miss the opportunity to run other important 
   optimizations.  Improvements to EarlyCSE to remove this issue are tracked in 
   Bug 23333.

#. Avoid using arithmetic intrinsics unless you are *required* by your source 
   language specification to emit a particular code sequence.  The optimizer 
   is quite good at reasoning about general control flow and arithmetic, it is
   not anywhere near as strong at reasoning about the various intrinsics.  If 
   profitable for code generation purposes, the optimizer will likely form the 
   intrinsics itself late in the optimization pipeline.  It is *very* rarely 
   profitable to emit these directly in the language frontend.  This item
   explicitly includes the use of the :ref:`overflow intrinsics <int_overflow>`.

#. Avoid using the :ref:`assume intrinsic <int_assume>` until you've 
   established that a) there's no other way to express the given fact and b) 
   that fact is critical for optimization purposes.  Assumes are a great 
   prototyping mechanism, but they can have negative effects on both compile 
   time and optimization effectiveness.  The former is fixable with enough 
   effort, but the later is fairly fundamental to their designed purpose.


Describing Language Specific Properties
=======================================

When translating a source language to LLVM, finding ways to express concepts 
and guarantees available in your source language which are not natively 
provided by LLVM IR will greatly improve LLVM's ability to optimize your code. 
As an example, C/C++'s ability to mark every add as "no signed wrap (nsw)" goes
a long way to assisting the optimizer in reasoning about loop induction 
variables and thus generating more optimal code for loops.  

The LLVM LangRef includes a number of mechanisms for annotating the IR with 
additional semantic information.  It is *strongly* recommended that you become 
highly familiar with this document.  The list below is intended to highlight a 
couple of items of particular interest, but is by no means exhaustive.

Restricted Operation Semantics
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#. Add nsw/nuw flags as appropriate.  Reasoning about overflow is 
   generally hard for an optimizer so providing these facts from the frontend 
   can be very impactful.  

#. Use fast-math flags on floating point operations if legal.  If you don't 
   need strict IEEE floating point semantics, there are a number of additional 
   optimizations that can be performed.  This can be highly impactful for 
   floating point intensive computations.

Describing Aliasing Properties
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

#. Add noalias/align/dereferenceable/nonnull to function arguments and return 
   values as appropriate

#. Use pointer aliasing metadata, especially tbaa metadata, to communicate 
   otherwise-non-deducible pointer aliasing facts

#. Use inbounds on geps.  This can help to disambiguate some aliasing queries.


Modeling Memory Effects
^^^^^^^^^^^^^^^^^^^^^^^^

#. Mark functions as readnone/readonly/argmemonly or noreturn/nounwind when
   known.  The optimizer will try to infer these flags, but may not always be
   able to.  Manual annotations are particularly important for external 
   functions that the optimizer can not analyze.

#. Use the lifetime.start/lifetime.end and invariant.start/invariant.end 
   intrinsics where possible.  Common profitable uses are for stack like data 
   structures (thus allowing dead store elimination) and for describing 
   life times of allocas (thus allowing smaller stack sizes).  

#. Mark invariant locations using !invariant.load and TBAA's constant flags

Pass Ordering
^^^^^^^^^^^^^

One of the most common mistakes made by new language frontend projects is to 
use the existing -O2 or -O3 pass pipelines as is.  These pass pipelines make a
good starting point for an optimizing compiler for any language, but they have 
been carefully tuned for C and C++, not your target language.  You will almost 
certainly need to use a custom pass order to achieve optimal performance.  A 
couple specific suggestions:

#. For languages with numerous rarely executed guard conditions (e.g. null 
   checks, type checks, range checks) consider adding an extra execution or 
   two of LoopUnswith and LICM to your pass order.  The standard pass order, 
   which is tuned for C and C++ applications, may not be sufficient to remove 
   all dischargeable checks from loops.

#. If you language uses range checks, consider using the IRCE pass.  It is not 
   currently part of the standard pass order.

#. A useful sanity check to run is to run your optimized IR back through the 
   -O2 pipeline again.  If you see noticeable improvement in the resulting IR, 
   you likely need to adjust your pass order.


I Still Can't Find What I'm Looking For
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

If you didn't find what you were looking for above, consider proposing an piece
of metadata which provides the optimization hint you need.  Such extensions are
relatively common and are generally well received by the community.  You will 
need to ensure that your proposal is sufficiently general so that it benefits 
others if you wish to contribute it upstream.

You should also consider describing the problem you're facing on `llvm-dev 
<http://lists.llvm.org/mailman/listinfo/llvm-dev>`_ and asking for advice.  
It's entirely possible someone has encountered your problem before and can 
give good advice.  If there are multiple interested parties, that also 
increases the chances that a metadata extension would be well received by the
community as a whole.  

Adding to this document
=======================

If you run across a case that you feel deserves to be covered here, please send
a patch to `llvm-commits
<http://lists.llvm.org/mailman/listinfo/llvm-commits>`_ for review.

If you have questions on these items, please direct them to `llvm-dev 
<http://lists.llvm.org/mailman/listinfo/llvm-dev>`_.  The more relevant 
context you are able to give to your question, the more likely it is to be 
answered.