27 Commits

Author SHA1 Message Date
Simon Pilgrim
325031786e [SelectionDAG] Optimize expansion for rotates/funnel shifts
If the type of a funnel shift needs to be expanded, expand it to two funnel shifts instead of regular shifts. For constant shifts, this doesn't make much difference, but for variable shifts it allows a more optimal lowering.

Also use the optimized funnel shift lowering for rotates.

Alive2: https://alive2.llvm.org/ce/z/TvHDB- / https://alive2.llvm.org/ce/z/yzPept

(Branched from D108058 as getting this completed should help unlock some other WIP patches).

Original Patch: @efriedma (Eli Friedman)

Differential Revision: https://reviews.llvm.org/D112443
2021-11-02 11:38:25 +00:00
Guozhi Wei
6599961c17 [TwoAddressInstructionPass] Improve the SrcRegMap and DstRegMap computation
This patch contains following enhancements to SrcRegMap and DstRegMap:

  1 In findOnlyInterestingUse not only check if the Reg is two address usage,
    but also check after commutation can it be two address usage.

  2 If a physical register is clobbered, remove SrcRegMap entries that are
    mapped to it.

  3 In processTiedPairs, when create a new COPY instruction, add a SrcRegMap
    entry only when the COPY instruction is coalescable. (The COPY src is
    killed)

With these enhancements isProfitableToCommute can do better commute decision,
and finally more register copies are removed.

Differential Revision: https://reviews.llvm.org/D108731
2021-10-11 15:28:31 -07:00
Matt Arsenault
4a36e96c3f RegAllocGreedy: Account for reserved registers in num regs heuristic
This simple heuristic uses the estimated live range length combined
with the number of registers in the class to switch which heuristic to
use. This was taking the raw number of registers in the class, even
though not all of them may be available. AMDGPU heavily relies on
dynamically reserved numbers of registers based on user attributes to
satisfy occupancy constraints, so the raw number is highly misleading.

There are still a few problems here. In the original testcase that
made me notice this, the live range size is incorrect after the
scheduler rearranges instructions, since the instructions don't have
the original InstrDist offsets. Additionally, I think it would be more
appropriate to use the number of disjointly allocatable registers in
the class. For the AMDGPU register tuples, there are a large number of
registers in each tuple class, but only a small fraction can actually
be allocated at the same time since they all overlap with each
other. It seems we do not have a query that corresponds to the number
of independently allocatable registers. Relatedly, I'm still debugging
some allocation failures where overlapping tuples seem to not be
handled correctly.

The test changes are mostly noise. There are a handful of x86 tests
that look like regressions with an additional spill, and a handful
that now avoid a spill. The worst looking regression is likely
test/Thumb2/mve-vld4.ll which introduces a few additional
spills. test/CodeGen/AMDGPU/soft-clause-exceeds-register-budget.ll
shows a massive improvement by completely eliminating a large number
of spills inside a loop.
2021-09-14 21:00:29 -04:00
Simon Pilgrim
778440f199 [X86] Add i128 funnel shift tests
Test coverage for D108058
2021-08-16 17:31:17 +01:00
Wang, Pengfei
c22dc71b12 [CodeGen][X86] Remove unused trivial check-prefixes from all CodeGen/X86 directory.
I had manually removed unused prefixes from CodeGen/X86 directory for more than 100 tests.
I checked the change history for each of them at the beginning, and then I mainly focused on the format since I found all of the unused prefixes were result from either insensible copy or residuum after functional update.
I think it's OK to remove the remaining X86 tests by script now. I wrote a rough script which works for me in most tests. I put it in llvm/utils temporarily for review and hope it may help other components owners.
The tests in this patch are all generated by the tool and checked by update tool for the autogenerated tests. I skimmed them and checked about 30 tests and didn't find any unexpected changes.

Reviewed By: mtrofin, MaskRay

Differential Revision: https://reviews.llvm.org/D91496
2020-11-16 09:45:55 +08:00
Jay Foad
0819a6416f [SelectionDAG] Better legalization for FSHL and FSHR
In SelectionDAGBuilder always translate the fshl and fshr intrinsics to
FSHL and FSHR (or ROTL and ROTR) instead of lowering them to shifts and
ORs. Improve the legalization of FSHL and FSHR to avoid code quality
regressions.

Differential Revision: https://reviews.llvm.org/D77152
2020-08-21 10:32:49 +01:00
Simon Pilgrim
1603106725 [TargetLowering] Improve expandFunnelShift shift amount masking
For the 'inverse shift', we currently always perform a subtraction of the original (masked) shift amount.

But for the case where we are handling power-of-2 type widths, we can replace:

(sub bw-1, (and amt, bw-1) ) -> (and (xor amt, bw-1), bw-1) -> (and ~amt, bw-1)

This allows x86 shifts to fold away the and-mask.

Followup to D77301 + D80466.

http://volta.cs.utah.edu:8080/z/Nod0Gr

Differential Revision: https://reviews.llvm.org/D80489
2020-05-24 11:25:09 +01:00
Simon Pilgrim
cc65a7a5ea [X86] Improve i8 + 'slow' i16 funnel shift codegen
This is a preliminary patch before I deal with the xor+and issue raised in D77301.

We get much better code for i8/i16 funnel shifts by concatenating the operands together and performing the shift as a double width type, it avoids repeated use of the shift amount and partial registers.

fshl(x,y,z) -> (((zext(x) << bw) | zext(y)) << (z & (bw-1))) >> bw.
fshr(x,y,z) -> (((zext(x) << bw) | zext(y)) >> (z & (bw-1))) >> bw.

Alive2: http://volta.cs.utah.edu:8080/z/CZx7Cn

This doesn't do as well for i32 cases on x86_64 (the xor+and followup patch is much better) so I haven't bothered with that.

Cases with constant amounts are more dubious as well so I haven't currently bothered with those - its these kind of 'edge' cases that put me off trying to put this in TargetLowering::expandFunnelShift.

Differential Revision: https://reviews.llvm.org/D80466
2020-05-24 08:08:53 +01:00
Jay Foad
17941437a2 [TargetLowering] Improve expansion of FSHL/FSHR
Use an extra shift-by-1 instead of a compare and select to handle the
shift-by-zero case. This sometimes saves one instruction (if the compare
couldn't be combined with a previous instruction). It also works better
on targets that don't have good select instructions.

Note that currently this change doesn't affect most targets because
expandFunnelShift is not used because funnel shift intrinsics are
lowered early in SelectionDAGBuilder. But there is work afoot to change
that; see D77152.

Differential Revision: https://reviews.llvm.org/D77301
2020-05-14 16:36:22 +01:00
Simon Pilgrim
d8f9416fdc [DAG] MatchRotate - Add funnel shift by immediate support
This patch reuses the existing MatchRotate ROTL/ROTR rotation pattern code to also recognize the more general FSHL/FSHR funnel shift patterns when we have constant shift amounts.

Differential Revision: https://reviews.llvm.org/D75114
2020-03-11 18:55:18 +00:00
Simon Pilgrim
b3b4727a3e [X86] Replace (most) X86ISD::SHLD/SHRD usage with ISD::FSHL/FSHR generic opcodes (PR39467)
For i32 and i64 cases, X86ISD::SHLD/SHRD are close enough to ISD::FSHL/FSHR that we can use them directly, we just need to account for the operand commutation for SHRD.

The i16 SHLD/SHRD case is annoying as the shift amount is modulo-32 (vs funnel shift modulo-16), so I've added X86ISD::FSHL/FSHR equivalents, which matches the generic implementation in all other terms.

Something I'm slightly concerned with is that ISD::FSHL/FSHR legality is controlled by the Subtarget.isSHLDSlow() feature flag - we don't normally use non-ISA features for this but it allows the DAG combines to continue to operate after legalization in a lot more cases.

The X86 *bits.ll changes are all affected by the same issue - we now have a "FSHR(-1,-1,amt) -> ROTR(-1,amt) -> (-1)" simplification that reduces the dependencies enough for the branch fall through code to mess up.

Differential Revision: https://reviews.llvm.org/D75748
2020-03-11 11:17:49 +00:00
Simon Pilgrim
7202d9cde9 [DAG] Combine fshl/fshr(load1,load0,c) if we have consecutive loads
As noted on D75114, if both arguments of a funnel shift are consecutive loads we are missing the opportunity to combine them into a single load.

Differential Revision: https://reviews.llvm.org/D75624
2020-03-06 11:36:18 +00:00
Simon Pilgrim
f24d90c0a6 [X86] Add tests showing failure to combine consecutive loads + FSHR into a single load
Similar to some of the regressions seen in D75114
2020-03-04 17:07:03 +00:00
Hiroshi Yamauchi
ed50e6060b [PGO][PGSO] Enable size optimizations in code gen / target passes for cold code.
Summary: Split off of D67120.

Reviewers: davidxl

Subscribers: hiraditya, asb, rbar, johnrusso, simoncook, sabuasal, niosHD, jrtc27, MaskRay, zzheng, edward-jones, rogfer01, MartinMosbeck, brucehoult, the_o, PkmX, jocewei, lenary, s.egerton, pzheng, sameer.abuasal, apazos, luismarques, llvm-commits

Tags: #llvm

Differential Revision: https://reviews.llvm.org/D71288
2019-12-13 11:01:19 -08:00
Kai Luo
b200c5180e Reland [MachineCopyPropagation] Extend MCP to do trivial copy backward propagation.
Fix assertion error
```
bool llvm::MachineOperand::isRenamable() const: Assertion `Register::isPhysicalRegister(getReg()) && "isRenamable should only be checked on physical registers"' failed.
```
by checking if the register is 0 before invoking `isRenamable`.
2019-12-05 14:32:11 +08:00
Kai Luo
3882edbe19 Revert "[MachineCopyPropagation] Extend MCP to do trivial copy backward propagation"
This reverts commit 75b3a1c318ccad0f96c38689279bc5db63e2ad05, since it
breaks bootstrap build.
2019-12-05 12:48:37 +08:00
Kai Luo
75b3a1c318 [MachineCopyPropagation] Extend MCP to do trivial copy backward propagation
Summary:
This patch mainly do such transformation
```
$R0 = OP ...
... // No read/clobber of $R0 and $R1
$R1 = COPY $R0 // $R0 is killed
```
Replace $R0 with $R1 and remove the COPY, we have
```
$R1 = OP ...
```
This transformation can also expose more opportunities for existing
copy elimination in MCP.

Differential Revision: https://reviews.llvm.org/D67794
2019-12-05 10:59:07 +08:00
Roman Lebedev
b6e376ddfa [X86] Promote i8 CMOV's (PR40965)
Summary:
@mclow.lists brought up this issue up in IRC, it came up during
implementation of libc++ `std::midpoint()` implementation (D59099)
https://godbolt.org/z/oLrHBP

Currently LLVM X86 backend only promotes i8 CMOV if it came from 2x`trunc`.
This differential proposes to always promote i8 CMOV.

There are several concerns here:
* Is this actually more performant, or is it just the ASM that looks cuter?
* Does this result in partial register stalls?
* What about branch predictor?

# Indeed, performance should be the main point here.
Let's look at a simple microbenchmark: {F8412076}
```
#include "benchmark/benchmark.h"

#include <algorithm>
#include <cmath>
#include <cstdint>
#include <iterator>
#include <limits>
#include <random>
#include <type_traits>
#include <utility>
#include <vector>

// Future preliminary libc++ code, from Marshall Clow.
namespace std {
template <class _Tp>
__inline _Tp midpoint(_Tp __a, _Tp __b) noexcept {
  using _Up = typename std::make_unsigned<typename remove_cv<_Tp>::type>::type;

  int __sign = 1;
  _Up __m = __a;
  _Up __M = __b;
  if (__a > __b) {
    __sign = -1;
    __m = __b;
    __M = __a;
  }
  return __a + __sign * _Tp(_Up(__M - __m) >> 1);
}
}  // namespace std

template <typename T>
std::vector<T> getVectorOfRandomNumbers(size_t count) {
  std::random_device rd;
  std::mt19937 gen(rd());
  std::uniform_int_distribution<T> dis(std::numeric_limits<T>::min(),
                                       std::numeric_limits<T>::max());
  std::vector<T> v;
  v.reserve(count);
  std::generate_n(std::back_inserter(v), count,
                  [&dis, &gen]() { return dis(gen); });
  assert(v.size() == count);
  return v;
}

struct RandRand {
  template <typename T>
  static std::pair<std::vector<T>, std::vector<T>> Gen(size_t count) {
    return std::make_pair(getVectorOfRandomNumbers<T>(count),
                          getVectorOfRandomNumbers<T>(count));
  }
};
struct ZeroRand {
  template <typename T>
  static std::pair<std::vector<T>, std::vector<T>> Gen(size_t count) {
    return std::make_pair(std::vector<T>(count, T(0)),
                          getVectorOfRandomNumbers<T>(count));
  }
};

template <class T, class Gen>
void BM_StdMidpoint(benchmark::State& state) {
  const size_t Length = state.range(0);

  const std::pair<std::vector<T>, std::vector<T>> Data =
      Gen::template Gen<T>(Length);
  const std::vector<T>& a = Data.first;
  const std::vector<T>& b = Data.second;
  assert(a.size() == Length && b.size() == a.size());

  benchmark::ClobberMemory();
  benchmark::DoNotOptimize(a);
  benchmark::DoNotOptimize(a.data());
  benchmark::DoNotOptimize(b);
  benchmark::DoNotOptimize(b.data());

  for (auto _ : state) {
    for (size_t i = 0; i < Length; i++) {
      const auto calculated = std::midpoint(a[i], b[i]);
      benchmark::DoNotOptimize(calculated);
    }
  }
  state.SetComplexityN(Length);
  state.counters["midpoints"] =
      benchmark::Counter(Length, benchmark::Counter::kIsIterationInvariant);
  state.counters["midpoints/sec"] =
      benchmark::Counter(Length, benchmark::Counter::kIsIterationInvariantRate);
  const size_t BytesRead = 2 * sizeof(T) * Length;
  state.counters["bytes_read/iteration"] =
      benchmark::Counter(BytesRead, benchmark::Counter::kDefaults,
                         benchmark::Counter::OneK::kIs1024);
  state.counters["bytes_read/sec"] = benchmark::Counter(
      BytesRead, benchmark::Counter::kIsIterationInvariantRate,
      benchmark::Counter::OneK::kIs1024);
}

template <typename T>
static void CustomArguments(benchmark::internal::Benchmark* b) {
  const size_t L2SizeBytes = 2 * 1024 * 1024;
  // What is the largest range we can check to always fit within given L2 cache?
  const size_t MaxLen = L2SizeBytes / /*total bufs*/ 2 /
                        /*maximal elt size*/ sizeof(T) / /*safety margin*/ 2;
  b->RangeMultiplier(2)->Range(1, MaxLen)->Complexity(benchmark::oN);
}

// Both of the values are random.
// The comparison is unpredictable.
BENCHMARK_TEMPLATE(BM_StdMidpoint, int32_t, RandRand)
    ->Apply(CustomArguments<int32_t>);
BENCHMARK_TEMPLATE(BM_StdMidpoint, uint32_t, RandRand)
    ->Apply(CustomArguments<uint32_t>);
BENCHMARK_TEMPLATE(BM_StdMidpoint, int64_t, RandRand)
    ->Apply(CustomArguments<int64_t>);
BENCHMARK_TEMPLATE(BM_StdMidpoint, uint64_t, RandRand)
    ->Apply(CustomArguments<uint64_t>);
BENCHMARK_TEMPLATE(BM_StdMidpoint, int16_t, RandRand)
    ->Apply(CustomArguments<int16_t>);
BENCHMARK_TEMPLATE(BM_StdMidpoint, uint16_t, RandRand)
    ->Apply(CustomArguments<uint16_t>);
BENCHMARK_TEMPLATE(BM_StdMidpoint, int8_t, RandRand)
    ->Apply(CustomArguments<int8_t>);
BENCHMARK_TEMPLATE(BM_StdMidpoint, uint8_t, RandRand)
    ->Apply(CustomArguments<uint8_t>);

// One value is always zero, and another is bigger or equal than zero.
// The comparison is predictable.
BENCHMARK_TEMPLATE(BM_StdMidpoint, uint32_t, ZeroRand)
    ->Apply(CustomArguments<uint32_t>);
BENCHMARK_TEMPLATE(BM_StdMidpoint, uint64_t, ZeroRand)
    ->Apply(CustomArguments<uint64_t>);
BENCHMARK_TEMPLATE(BM_StdMidpoint, uint16_t, ZeroRand)
    ->Apply(CustomArguments<uint16_t>);
BENCHMARK_TEMPLATE(BM_StdMidpoint, uint8_t, ZeroRand)
    ->Apply(CustomArguments<uint8_t>);
```

```
$ ~/src/googlebenchmark/tools/compare.py --no-utest benchmarks ./llvm-cmov-bench-OLD ./llvm-cmov-bench-NEW
RUNNING: ./llvm-cmov-bench-OLD --benchmark_out=/tmp/tmp5a5qjm
2019-03-06 21:53:31
Running ./llvm-cmov-bench-OLD
Run on (8 X 4000 MHz CPU s)
CPU Caches:
  L1 Data 16K (x8)
  L1 Instruction 64K (x4)
  L2 Unified 2048K (x4)
  L3 Unified 8192K (x1)
Load Average: 1.78, 1.81, 1.36
----------------------------------------------------------------------------------------------------
Benchmark                                          Time             CPU   Iterations UserCounters<...>
----------------------------------------------------------------------------------------------------
<...>
BM_StdMidpoint<int32_t, RandRand>/131072      300398 ns       300404 ns         2330 bytes_read/iteration=1024k bytes_read/sec=3.25083G/s midpoints=305.398M midpoints/sec=436.319M/s
BM_StdMidpoint<int32_t, RandRand>_BigO          2.29 N          2.29 N
BM_StdMidpoint<int32_t, RandRand>_RMS              2 %             2 %
<...>
BM_StdMidpoint<uint32_t, RandRand>/131072     300433 ns       300433 ns         2330 bytes_read/iteration=1024k bytes_read/sec=3.25052G/s midpoints=305.398M midpoints/sec=436.278M/s
BM_StdMidpoint<uint32_t, RandRand>_BigO         2.29 N          2.29 N
BM_StdMidpoint<uint32_t, RandRand>_RMS             2 %             2 %
<...>
BM_StdMidpoint<int64_t, RandRand>/65536       169857 ns       169858 ns         4121 bytes_read/iteration=1024k bytes_read/sec=5.74929G/s midpoints=270.074M midpoints/sec=385.828M/s
BM_StdMidpoint<int64_t, RandRand>_BigO          2.59 N          2.59 N
BM_StdMidpoint<int64_t, RandRand>_RMS              3 %             3 %
<...>
BM_StdMidpoint<uint64_t, RandRand>/65536      169770 ns       169771 ns         4125 bytes_read/iteration=1024k bytes_read/sec=5.75223G/s midpoints=270.336M midpoints/sec=386.026M/s
BM_StdMidpoint<uint64_t, RandRand>_BigO         2.59 N          2.59 N
BM_StdMidpoint<uint64_t, RandRand>_RMS             3 %             3 %
<...>
BM_StdMidpoint<int16_t, RandRand>/262144      591169 ns       591179 ns         1182 bytes_read/iteration=1024k bytes_read/sec=1.65189G/s midpoints=309.854M midpoints/sec=443.426M/s
BM_StdMidpoint<int16_t, RandRand>_BigO          2.25 N          2.25 N
BM_StdMidpoint<int16_t, RandRand>_RMS              1 %             1 %
<...>
BM_StdMidpoint<uint16_t, RandRand>/262144     591264 ns       591274 ns         1184 bytes_read/iteration=1024k bytes_read/sec=1.65162G/s midpoints=310.378M midpoints/sec=443.354M/s
BM_StdMidpoint<uint16_t, RandRand>_BigO         2.25 N          2.25 N
BM_StdMidpoint<uint16_t, RandRand>_RMS             1 %             1 %
<...>
BM_StdMidpoint<int8_t, RandRand>/524288      2983669 ns      2983689 ns          235 bytes_read/iteration=1024k bytes_read/sec=335.156M/s midpoints=123.208M midpoints/sec=175.718M/s
BM_StdMidpoint<int8_t, RandRand>_BigO           5.69 N          5.69 N
BM_StdMidpoint<int8_t, RandRand>_RMS               0 %             0 %
<...>
BM_StdMidpoint<uint8_t, RandRand>/524288     2668398 ns      2668419 ns          262 bytes_read/iteration=1024k bytes_read/sec=374.754M/s midpoints=137.363M midpoints/sec=196.479M/s
BM_StdMidpoint<uint8_t, RandRand>_BigO          5.09 N          5.09 N
BM_StdMidpoint<uint8_t, RandRand>_RMS              0 %             0 %
<...>
BM_StdMidpoint<uint32_t, ZeroRand>/131072     300887 ns       300887 ns         2331 bytes_read/iteration=1024k bytes_read/sec=3.24561G/s midpoints=305.529M midpoints/sec=435.619M/s
BM_StdMidpoint<uint32_t, ZeroRand>_BigO         2.29 N          2.29 N
BM_StdMidpoint<uint32_t, ZeroRand>_RMS             2 %             2 %
<...>
BM_StdMidpoint<uint64_t, ZeroRand>/65536      169634 ns       169634 ns         4102 bytes_read/iteration=1024k bytes_read/sec=5.75688G/s midpoints=268.829M midpoints/sec=386.338M/s
BM_StdMidpoint<uint64_t, ZeroRand>_BigO         2.59 N          2.59 N
BM_StdMidpoint<uint64_t, ZeroRand>_RMS             3 %             3 %
<...>
BM_StdMidpoint<uint16_t, ZeroRand>/262144     592252 ns       592255 ns         1182 bytes_read/iteration=1024k bytes_read/sec=1.64889G/s midpoints=309.854M midpoints/sec=442.62M/s
BM_StdMidpoint<uint16_t, ZeroRand>_BigO         2.26 N          2.26 N
BM_StdMidpoint<uint16_t, ZeroRand>_RMS             1 %             1 %
<...>
BM_StdMidpoint<uint8_t, ZeroRand>/524288      987295 ns       987309 ns          711 bytes_read/iteration=1024k bytes_read/sec=1012.85M/s midpoints=372.769M midpoints/sec=531.028M/s
BM_StdMidpoint<uint8_t, ZeroRand>_BigO          1.88 N          1.88 N
BM_StdMidpoint<uint8_t, ZeroRand>_RMS              1 %             1 %
RUNNING: ./llvm-cmov-bench-NEW --benchmark_out=/tmp/tmpPvwpfW
2019-03-06 21:56:58
Running ./llvm-cmov-bench-NEW
Run on (8 X 4000 MHz CPU s)
CPU Caches:
  L1 Data 16K (x8)
  L1 Instruction 64K (x4)
  L2 Unified 2048K (x4)
  L3 Unified 8192K (x1)
Load Average: 1.17, 1.46, 1.30
----------------------------------------------------------------------------------------------------
Benchmark                                          Time             CPU   Iterations UserCounters<...>
----------------------------------------------------------------------------------------------------
<...>
BM_StdMidpoint<int32_t, RandRand>/131072      300878 ns       300880 ns         2324 bytes_read/iteration=1024k bytes_read/sec=3.24569G/s midpoints=304.611M midpoints/sec=435.629M/s
BM_StdMidpoint<int32_t, RandRand>_BigO          2.29 N          2.29 N
BM_StdMidpoint<int32_t, RandRand>_RMS              2 %             2 %
<...>
BM_StdMidpoint<uint32_t, RandRand>/131072     300231 ns       300226 ns         2330 bytes_read/iteration=1024k bytes_read/sec=3.25276G/s midpoints=305.398M midpoints/sec=436.578M/s
BM_StdMidpoint<uint32_t, RandRand>_BigO         2.29 N          2.29 N
BM_StdMidpoint<uint32_t, RandRand>_RMS             2 %             2 %
<...>
BM_StdMidpoint<int64_t, RandRand>/65536       170819 ns       170777 ns         4115 bytes_read/iteration=1024k bytes_read/sec=5.71835G/s midpoints=269.681M midpoints/sec=383.752M/s
BM_StdMidpoint<int64_t, RandRand>_BigO          2.60 N          2.60 N
BM_StdMidpoint<int64_t, RandRand>_RMS              3 %             3 %
<...>
BM_StdMidpoint<uint64_t, RandRand>/65536      171705 ns       171708 ns         4106 bytes_read/iteration=1024k bytes_read/sec=5.68733G/s midpoints=269.091M midpoints/sec=381.671M/s
BM_StdMidpoint<uint64_t, RandRand>_BigO         2.62 N          2.62 N
BM_StdMidpoint<uint64_t, RandRand>_RMS             3 %             3 %
<...>
BM_StdMidpoint<int16_t, RandRand>/262144      592510 ns       592516 ns         1182 bytes_read/iteration=1024k bytes_read/sec=1.64816G/s midpoints=309.854M midpoints/sec=442.425M/s
BM_StdMidpoint<int16_t, RandRand>_BigO          2.26 N          2.26 N
BM_StdMidpoint<int16_t, RandRand>_RMS              1 %             1 %
<...>
BM_StdMidpoint<uint16_t, RandRand>/262144     614823 ns       614823 ns         1180 bytes_read/iteration=1024k bytes_read/sec=1.58836G/s midpoints=309.33M midpoints/sec=426.373M/s
BM_StdMidpoint<uint16_t, RandRand>_BigO         2.33 N          2.33 N
BM_StdMidpoint<uint16_t, RandRand>_RMS             4 %             4 %
<...>
BM_StdMidpoint<int8_t, RandRand>/524288      1073181 ns      1073201 ns          650 bytes_read/iteration=1024k bytes_read/sec=931.791M/s midpoints=340.787M midpoints/sec=488.527M/s
BM_StdMidpoint<int8_t, RandRand>_BigO           2.05 N          2.05 N
BM_StdMidpoint<int8_t, RandRand>_RMS               1 %             1 %
BM_StdMidpoint<uint8_t, RandRand>/524288     1071010 ns      1071020 ns          653 bytes_read/iteration=1024k bytes_read/sec=933.689M/s midpoints=342.36M midpoints/sec=489.522M/s
BM_StdMidpoint<uint8_t, RandRand>_BigO          2.05 N          2.05 N
BM_StdMidpoint<uint8_t, RandRand>_RMS              1 %             1 %
<...>
BM_StdMidpoint<uint32_t, ZeroRand>/131072     300413 ns       300416 ns         2330 bytes_read/iteration=1024k bytes_read/sec=3.2507G/s midpoints=305.398M midpoints/sec=436.302M/s
BM_StdMidpoint<uint32_t, ZeroRand>_BigO         2.29 N          2.29 N
BM_StdMidpoint<uint32_t, ZeroRand>_RMS             2 %             2 %
<...>
BM_StdMidpoint<uint64_t, ZeroRand>/65536      169667 ns       169669 ns         4123 bytes_read/iteration=1024k bytes_read/sec=5.75568G/s midpoints=270.205M midpoints/sec=386.257M/s
BM_StdMidpoint<uint64_t, ZeroRand>_BigO         2.59 N          2.59 N
BM_StdMidpoint<uint64_t, ZeroRand>_RMS             3 %             3 %
<...>
BM_StdMidpoint<uint16_t, ZeroRand>/262144     591396 ns       591404 ns         1184 bytes_read/iteration=1024k bytes_read/sec=1.65126G/s midpoints=310.378M midpoints/sec=443.257M/s
BM_StdMidpoint<uint16_t, ZeroRand>_BigO         2.26 N          2.26 N
BM_StdMidpoint<uint16_t, ZeroRand>_RMS             1 %             1 %
<...>
BM_StdMidpoint<uint8_t, ZeroRand>/524288     1069421 ns      1069413 ns          655 bytes_read/iteration=1024k bytes_read/sec=935.092M/s midpoints=343.409M midpoints/sec=490.258M/s
BM_StdMidpoint<uint8_t, ZeroRand>_BigO          2.04 N          2.04 N
BM_StdMidpoint<uint8_t, ZeroRand>_RMS              0 %             0 %
Comparing ./llvm-cmov-bench-OLD to ./llvm-cmov-bench-NEW
Benchmark                                                   Time             CPU      Time Old      Time New       CPU Old       CPU New
----------------------------------------------------------------------------------------------------------------------------------------
<...>
BM_StdMidpoint<int32_t, RandRand>/131072                 +0.0016         +0.0016        300398        300878        300404        300880
<...>
BM_StdMidpoint<uint32_t, RandRand>/131072                -0.0007         -0.0007        300433        300231        300433        300226
<...>
BM_StdMidpoint<int64_t, RandRand>/65536                  +0.0057         +0.0054        169857        170819        169858        170777
<...>
BM_StdMidpoint<uint64_t, RandRand>/65536                 +0.0114         +0.0114        169770        171705        169771        171708
<...>
BM_StdMidpoint<int16_t, RandRand>/262144                 +0.0023         +0.0023        591169        592510        591179        592516
<...>
BM_StdMidpoint<uint16_t, RandRand>/262144                +0.0398         +0.0398        591264        614823        591274        614823
<...>
BM_StdMidpoint<int8_t, RandRand>/524288                  -0.6403         -0.6403       2983669       1073181       2983689       1073201
<...>
BM_StdMidpoint<uint8_t, RandRand>/524288                 -0.5986         -0.5986       2668398       1071010       2668419       1071020
<...>
BM_StdMidpoint<uint32_t, ZeroRand>/131072                -0.0016         -0.0016        300887        300413        300887        300416
<...>
BM_StdMidpoint<uint64_t, ZeroRand>/65536                 +0.0002         +0.0002        169634        169667        169634        169669
<...>
BM_StdMidpoint<uint16_t, ZeroRand>/262144                -0.0014         -0.0014        592252        591396        592255        591404
<...>
BM_StdMidpoint<uint8_t, ZeroRand>/524288                 +0.0832         +0.0832        987295       1069421        987309       1069413
```

What can we tell from the benchmark?
* `BM_StdMidpoint<[u]int8_t, RandRand>` indeed has the worst performance.
* All `BM_StdMidpoint<uint{8,16,32}_t, ZeroRand>` are all performant, even the 8-bit case.
  That is because there we are computing mid point between zero and some random number,
  thus if the branch predictor is in use, it is in optimal situation.
* Promoting 8-bit CMOV did improve performance of `BM_StdMidpoint<[u]int8_t, RandRand>`, by -59%..-64%.

# What about branch predictor?
* `BM_StdMidpoint<uint8_t, ZeroRand>` was faster than `BM_StdMidpoint<uint{16,32,64}_t, ZeroRand>`,
  which may mean that well-predicted branch is better than `cmov`.
* Promoting 8-bit CMOV degraded performance of `BM_StdMidpoint<uint8_t, ZeroRand>`,
  `cmov` is up to +10% worse than well-predicted branch.
* However, i do not believe this is a concern. If the branch is well predicted,  then the PGO
  will also say that it is well predicted, and LLVM will happily expand cmov back into branch:
  https://godbolt.org/z/P5ufig

# What about partial register stalls?
I'm not really able to answer that.
What i can say is that if the branch is unpredictable (if it is predictable, then use PGO and you'll have branch)
in ~50% of cases you will have to pay branch misprediction penalty.
```
$ grep -i MispredictPenalty X86Sched*.td
X86SchedBroadwell.td:  let MispredictPenalty = 16;
X86SchedHaswell.td:  let MispredictPenalty = 16;
X86SchedSandyBridge.td:  let MispredictPenalty = 16;
X86SchedSkylakeClient.td:  let MispredictPenalty = 14;
X86SchedSkylakeServer.td:  let MispredictPenalty = 14;
X86ScheduleBdVer2.td:  let MispredictPenalty = 20; // Minimum branch misdirection penalty.
X86ScheduleBtVer2.td:  let MispredictPenalty = 14; // Minimum branch misdirection penalty
X86ScheduleSLM.td:  let MispredictPenalty = 10;
X86ScheduleZnver1.td:  let MispredictPenalty = 17;
```
.. which it can be as small as 10 cycles and as large as 20 cycles.
Partial register stalls do not seem to be an issue for AMD CPU's.
For intel CPU's, they should be around ~5 cycles?
Is that actually an issue here? I'm not sure.

In short, i'd say this is an improvement, at least on this microbenchmark.

Fixes [[ https://bugs.llvm.org/show_bug.cgi?id=40965 | PR40965 ]].

Reviewers: craig.topper, RKSimon, spatel, andreadb, nikic

Reviewed By: craig.topper, andreadb

Subscribers: jfb, jdoerfert, llvm-commits, mclow.lists

Tags: #llvm, #libc

Differential Revision: https://reviews.llvm.org/D59035

llvm-svn: 356300
2019-03-15 21:17:53 +00:00
Simon Pilgrim
ef7b5949e5 [X86] Lower to SHLD/SHRD on slow machines for optsize
Use consistent rules for when to lower to SHLD/SHRD for slow machines - fixes a weird issue where funnel shift gets expanded but then X86ISelLowering's combineOr sees the optsize and combines to SHLD/SHRD, but now with the modulo amount guard......

llvm-svn: 349285
2018-12-15 19:43:44 +00:00
Simon Pilgrim
53c8b1b6f7 [X86] Add optsize SHLD/SHRD tests
llvm-svn: 349284
2018-12-15 19:32:26 +00:00
Sanjay Patel
44eaa492b8 [x86] allow 8-bit adds to be promoted by convertToThreeAddress() to form LEA
This extends the code that handles 16-bit add promotion to form LEA to also allow 8-bit adds. 
That allows us to combine add ops with register moves and save some instructions. This is 
another step towards allowing add truncation in generic DAGCombiner (see D54640).

Differential Revision: https://reviews.llvm.org/D55494

llvm-svn: 348946
2018-12-12 17:58:27 +00:00
Sanjay Patel
e767bf4468 [DAGCombiner] re-enable truncation of binops
This is effectively re-committing the changes from:
rL347917 (D54640)
rL348195 (D55126)
...which were effectively reverted here:
rL348604
...because the code had a bug that could induce infinite looping
or eventual out-of-memory compilation.

The bug was that this code did not guard against transforming
opaque constants. More details are in the post-commit mailing
list thread for r347917. A reduced test for that is included
in the x86 bool-math.ll file. (I wasn't able to reduce a PPC
backend test for this, but it was almost the same pattern.)

Original commit message for r347917:

The motivating case for this is shown in:
https://bugs.llvm.org/show_bug.cgi?id=32023
and the corresponding rot16.ll regression tests.

Because x86 scalar shift amounts are i8 values, we can end up with trunc-binop-trunc
sequences that don't get folded in IR.

As the TODO comments suggest, there will be regressions if we extend this (for x86,
we mostly seem to be missing LEA opportunities, but there are likely vector folds
missing too). I think those should be considered existing bugs because this is the
same transform that we do as an IR canonicalization in instcombine. We just need
more tests to make those visible independent of this patch.

llvm-svn: 348706
2018-12-08 16:07:38 +00:00
Sanjay Patel
3af4ae9735 [DAGCombiner] disable truncation of binops by default
As discussed in the post-commit thread of r347917, this
transform is fighting with an existing transform causing
an infinite loop or out-of-memory, so this is effectively 
reverting r347917 and its follow-up r348195 while we
investigate the bug.

llvm-svn: 348604
2018-12-07 15:47:52 +00:00
Simon Pilgrim
180639afe5 [SelectionDAG] Initial support for FSHL/FSHR funnel shift opcodes (PR39467)
This is an initial patch to add a minimum level of support for funnel shifts to the SelectionDAG and to begin wiring it up to the X86 SHLD/SHRD instructions.

Some partial legalization code has been added to handle the case for 'SlowSHLD' where we want to expand instead and I've added a few DAG combines so we don't get regressions from the existing DAG builder expansion code.

Differential Revision: https://reviews.llvm.org/D54698

llvm-svn: 348353
2018-12-05 11:12:12 +00:00
Sanjay Patel
8d27144251 [DAGCombiner] narrow truncated binops
The motivating case for this is shown in:
https://bugs.llvm.org/show_bug.cgi?id=32023
and the corresponding rot16.ll regression tests.

Because x86 scalar shift amounts are i8 values, we can end up with trunc-binop-trunc 
sequences that don't get folded in IR.

As the TODO comments suggest, there will be regressions if we extend this (for x86, 
we mostly seem to be missing LEA opportunities, but there are likely vector folds 
missing too). I think those should be considered existing bugs because this is the 
same transform that we do as an IR canonicalization in instcombine. We just need 
more tests to make those visible independent of this patch.

Differential Revision: https://reviews.llvm.org/D54640

llvm-svn: 347917
2018-11-29 20:58:26 +00:00
Simon Pilgrim
f6c2fbdd1a [X86] Add codegen tests for slow-shld scalar funnel shifts
llvm-svn: 347195
2018-11-19 12:29:41 +00:00
Simon Pilgrim
96f7924fe2 [X86] Add codegen tests for scalar funnel shifts
llvm-svn: 347066
2018-11-16 17:48:52 +00:00