[LLVMdev] [Vectorization] Mis match in code generated
suyog sarda
sardask01 at gmail.com
Tue Nov 11 10:28:51 PST 2014
Hi Arnold, James
Thanks for getting back on this. Arnold i will look into patches provided
by you
and get back on it. Thanks for it.
Adding few more observations :
The expression tree is like
sum = (a[0] + a[2]) + (a[1] + a[3]); --> which is vectorizable with current
code.
If the array a[] if of type float/double then there is no
disturbance/canonicalization
(precision issues??) in formed tree and we get a vectorized code.
However, if the array a[] is of type int, then re-associate pass (runs in
O1)
re-organizes this into
sum = (((a[0] + a[2]) + a[1]) + a[3]).
This results in loss of vectorization opportunity as we do not vectorize
this type
of tree in current code stated earlier in the thread.
Also, we do not build vectorization tree on return in current code
(PR20035),
which does not vectorizes code like this :
return a[0] + a[1] + a[2] + a[3];
I will try to work on vectorization on return statement. Inputs are always
welcomed.
Regards,
Suyog
On Mon, Nov 10, 2014 at 9:13 PM, James Molloy <james at jamesmolloy.co.uk>
wrote:
> Hi Suyog,
>
> Thanks for looking at this. This has recently got itself onto my TODO list
> too.
>
> > I am not sure how much all this will improve the code quality for
> horizontal reduction
> > (donno how frequently such pattern of horizontal reduction from same
> array occurs in real world/SPECS).
>
> Actually the main loop of 470.lbm can be SLP vectorized like this. We have
> three parts to it: A fully unrolled reduction, a scatter/gather part (not
> really vectorizable) and a contiguous-load-scatter-store part which should
> be vectorized even when scatter stores are not available in HW.
>
>
> Cheers,
>
> James
>
> On 10 November 2014 14:30, Arnold Schwaighofer <aschwaighofer at apple.com>
> wrote:
>
>> Hi Suyog,
>>
>> the horizontal reduction code needs work. It was written long ago and was
>> never enabled so it has bit rotted somewhat and has lurking bugs.
>> For example, it could not handle reduction value nodes that were not
>> binary operations.
>>
>> It could vectorize
>>
>> (+ (* x y) (*z a) (*b c) (*d e))
>>
>> but not
>>
>>
>> (+ (load a) (load b) (load c) (load d))
>>
>>
>> With the attached patches:
>> 0001-Test-hor-redux.patch
>> 0002-Print-out-tree.patch
>>
>> We would attempt to vectorize the tree stemming from your example:
>>
>>
>> > float a[4], sum;
>> > void foo() {
>> > sum = a[0]+a[1]+a[2]+a[3];
>> > }
>>
>> but because we happen to end up with the vector tuple (a[1],
>> a[0],a[2],a[3)) in need of gathering the existing code fails. Note, that I
>> used fast-math because the horizontal reduction code assumes it can
>> reassociate freely.
>>
>> > clang -O3 test_hor_redux.c -emit-llvm -S -o - -mllvm
>> -slp-vectorize-hor-store -debug-only=SLP -ffast-math -mllvm -debug-only=SLP
>>
>> ...
>>
>> Reduced val: %1 = load float* getelementptr inbounds ([4 x float]*
>> @a, i64 0, i64 1), align 4, !tbaa !1
>> Reduced val: %0 = load float* getelementptr inbounds ([4 x float]*
>> @a, i64 0, i64 0), align 16, !tbaa !1
>> Reduced val: %2 = load float* getelementptr inbounds ([4 x float]*
>> @a, i64 0, i64 2), align 8, !tbaa !1
>> Reduced val: %3 = load float* getelementptr inbounds ([4 x float]*
>> @a, i64 0, i64 3), align 4, !tbaa !1
>> Reduction ops: %add = fadd fast float %1, %0
>> Reduction ops: %add1 = fadd fast float %add, %2
>> Reduction ops: %add2 = fadd fast float %add1, %3
>> Assertion failed: (VectorizedValue && "Need to have a vectorized tree
>> node"), function emitReduction, file
>> ../lib/Transforms/Vectorize/SLPVectorizer.cpp, line 3518.
>>
>> The reduction looks like
>>
>> (((a[1] + a[0]) + a[2]) + a[3])
>>
>> So we build a tree entry (load a[1], load a[0], load a[2], load a[3])
>> and end up with a vectorized tree entry of one who needs gathering.
>>
>> The code does not handle this case correctly (in normal slp mode there is
>> no point in "vectorizing" a single entry tree that needs gathering).
>> The patch
>> 0003-We-don-t-correctly-vectorize-single-entry-gather-nod.patch fixes this
>> by disabling vectorization in such a case but then we don't vectorize.
>> As you can see this needs more work.
>>
>> * We could reassociate reductions like the one above.
>>
>> (((a[1] + a[0]) + a[2]) + a[3])
>>
>> =>
>>
>> (((a[0] + a[1]) + a[2]) + a[3])
>>
>> Then the reduction tree builder code would not have to be clever. That
>> will only work if your initial reduction values are loads. If the loads are
>> hidden behind a deeper tree that won't help. In such a case the next
>> solution would not be so bad though.
>>
>> * We could teach the tree code to do a vector load + permute operation if
>> applicable. This will no generate the best code though. But for deeper
>> trees the overhead of the permute probably does not matter much.
>>
>>
>> Best,
>> Arnold
>>
>> On 11/09/14, suyog sarda wrote:
>> > Hi Arnold, Nadav, Hal,
>> > all,
>> >
>> >
>> > Restarting this thread with few more observations on horizontal
>> reduction store
>> > (after using flags - slp-vectorize-hor-store and -slp-vectorize-hor,
>> thanks Arnold for pointing out earlier).
>> >
>> >
>> > The SLP vectorizer does vectorize straight line code :
>> >
>> >
>> > (this code may result as part of loop unrolling as stated earlier in
>> the thread.
>> > Though for loop vectorization , trip count should be atleast 16, taking
>> smaller
>> > code here to put some findings.)
>> >
>> >
>> > float a[4], sum;
>> >
>> > void foo() {
>> >
>> > int i;
>> >
>> > for(i=0;i<4;i++);
>> >
>> > sum += a[i];
>> > }
>> >
>> > after loop unrolling -
>> >
>> > float a[4], sum;
>> > void foo() {
>> > sum = a[0]+a[1]+a[2]+a[3];
>> > }
>> >
>> >
>> > This code gets vectorized depending on how we organize the code, and
>> subsequently
>> >
>> > how the expression tree is formed.
>> >
>> >
>> > case 1:
>> >
>> > float a[4], sum;
>> > void foo() {
>> > sum = a[0]+a[1]+a[2]+a[3];
>> > }
>> >
>> >
>> > The expression tree for this will be
>> >
>> >
>> > a[0] a[1]
>> >
>> > \ /
>> > \ /
>> >
>> > + a[2]
>> >
>> > \ /
>> > \ /
>> >
>> > + a[3]
>> >
>> > \ /
>> > \ /
>> > +
>> > |
>> > |
>> > ↓
>> >
>> > sum
>> >
>> >
>> > This doesn't vectorize, because in function tryToVectorizeList(), we
>> check if the
>> >
>> > scalar instruction are of the same type. In above case, it will be fadd
>> and load,
>> >
>> > and hence no vectorization takes place.
>> >
>> >
>> >
>> > case 2:
>> >
>> > float a[4], sum;
>> > void foo() {
>> > sum = (a[0]+a[1])+(a[2]+a[3]);
>> > }
>> >
>> >
>> > The expression tree for this will be (notice brackets above):
>> >
>> >
>> > a[0] a[1] a[2] a[3]
>> >
>> > \ / \ /
>> > \ / \ /
>> > + +
>> > \ /
>> > \ /
>> > \ /
>> > +
>> > |
>> > |
>> > ↓
>> > sum
>> >
>> >
>> > In this case, in function tryToVectorizeList(), both the scalar
>> instructions are
>> >
>> > same i.e. fadd. Hence we proceed for vectorization ahead.
>> >
>> > But this still doesn't vectorizes the code. Ahead, while trying to
>> schedule bundle of code,
>> >
>> > it checks whether the loads are for consecutive memory access across
>> two subtrees from
>> > same base pointer.
>> >
>> >
>> > For above code, it checks if (a[0],a[2]) and (a[1],a[3]) are
>> consecutive memory
>> > access (which, obviously, they are not). This cancels scheduling of
>> bundle of loads,
>> > and hence calculated cost for non-consecutive loads comes greater
>> increasing overall
>> >
>> > cost of vectorization (and hence no vectorization).
>> >
>> >
>> > Seems this check for consecutive memory access was written keeping in
>> mind following sort of code :
>> >
>> > sum = (a[0] + b[0]) + (a[1] + b[1]);
>> >
>> >
>> >
>> > Finally,
>> >
>> > case 3:
>> >
>> > float a[4], sum;
>> > void foo() {
>> > sum = (a[0]+a[2])+(a[1]+a[3]);
>> > }
>> >
>> >
>> > The expression tree for this will be (notice brackets above):
>> >
>> >
>> > a[0] a[2] a[1] a[3]
>> >
>> > \ / \ /
>> > \ / \ /
>> > + +
>> > \ /
>> > \ /
>> > \ /
>> > +
>> > |
>> > |
>> > ↓
>> > sum
>> >
>> >
>> >
>> > This code gets vectorized, as tryToVectorizeList() gets both scalar
>> operation as fadd,
>> >
>> > as well as there is a consecutive memory access across subtress rooted
>> at both fadd.
>> >
>> >
>> > case 1 IR after SLP vectorization :
>> >
>> > define void @foo() #0 {
>> > entry:
>> > %0 = load float* getelementptr inbounds ([8 x float]* @a, i64 0, i64
>> 0), align 16, !tbaa !1
>> > %1 = load float* getelementptr inbounds ([8 x float]* @a, i64 0, i64
>> 1), align 4, !tbaa !1
>> > %add = fadd float %0, %1
>> > %2 = load float* getelementptr inbounds ([8 x float]* @a, i64 0, i64
>> 2), align 8, !tbaa !1
>> > %add1 = fadd float %add, %2
>> > %3 = load float* getelementptr inbounds ([8 x float]* @a, i64 0, i64
>> 3), align 4, !tbaa !1
>> > %add2 = fadd float %add1, %3
>> > store float %add2, float* @sum, align 4, !tbaa !1
>> > ret void
>> > }
>> >
>> >
>> > case 2 IR after SLP vectorization :
>> >
>> > define void @foo() #0 {
>> > entry:
>> > %0 = load float* getelementptr inbounds ([8 x float]* @a, i64 0, i64
>> 0), align 16, !tbaa !1
>> > %1 = load float* getelementptr inbounds ([8 x float]* @a, i64 0, i64
>> 1), align 4, !tbaa !1
>> > %add = fadd float %0, %1
>> > %2 = load float* getelementptr inbounds ([8 x float]* @a, i64 0, i64
>> 2), align 8, !tbaa !1
>> > %3 = load float* getelementptr inbounds ([8 x float]* @a, i64 0, i64
>> 3), align 4, !tbaa !1
>> > %add1 = fadd float %2, %3
>> > %add2 = fadd float %add, %add1
>> > store float %add2, float* @sum, align 4, !tbaa !1
>> > ret void
>> > }
>> >
>> >
>> > case 3 IR after SLP vectorization :
>> >
>> >
>> > define void @foo() #0 {
>> > entry:
>> > %0 = load <2 x float>* bitcast ([4 x float]* @a to <2 x float>*),
>> align 16, !tbaa !1
>> > %1 = load <2 x float>* bitcast (float* getelementptr inbounds ([4 x
>> float]* @a, i64 0, i64 2) to <2 x float>*), align 8, !tbaa !1
>> > %2 = fadd <2 x float> %0, %1
>> > %3 = extractelement <2 x float> %2, i32 0
>> > %4 = extractelement <2 x float> %2, i32 1
>> > %add2 = fadd float %3, %4
>> > store float %add2, float* @sum, align 4, !tbaa !1
>> > ret void
>> > }
>> >
>> >
>> >
>> > Few cases of improvements, as visible from above (noting down roughly,
>> didn't give detail thought on it yet):
>> >
>> > 1. The IR expression tree can be re-organized to cater above need (some
>> kind of balancing like AVL tree).
>> >
>> > Alternatively, parse the expression tree without changing it and
>> look upward tree if we encounter a
>> >
>> > load instruction.
>> >
>> > 2. Seems, there is a case for improvement in checking if memory access
>> are consecutive.
>> > This needs to take into consideration all the loads of all the
>> subtree .
>> > If all the loads are from same base pointer, then are the memory
>> access in same subtree as
>> > well as across the subtree consecutive. (i will try to write some
>> code on this, any suggestions are welcomed)
>> >
>> >
>> >
>> > I am not sure how much all this will improve the code quality for
>> horizontal reduction
>> > (donno how frequently such pattern of horizontal reduction from same
>> array occurs in real world/SPECS).
>> >
>> > Perhaps the best case i can think of is when loops are unrolled as
>> noted earlier in the thread.
>> >
>> >
>> > Adding few more people to pitch in !!
>> >
>> >
>> > Comments/Suggestions are most welcomed.
>> >
>> >
>> > Regards,
>> >
>> > Suyog
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> > On Sat, Sep 20, 2014 at 12:05 AM, suyog sarda <sardask01 at gmail.com <
>> sardask01 at gmail.com>> wrote:
>> >
>> > > Hi Arnold,
>> > >
>> > >
>> > > Thanks for your reply.
>> > >
>> > >
>> > > I tried test case as suggested by you.
>> > >
>> > > void foo(int *a, int *sum) {
>> > > *sum =
>> a[0]+a[1]+a[2]+a[3]+a[4]+a[5]+a[6]+a[7]+a[8]+a[9]+a[10]+a[11]+a[12]+a[13]+a[14]+a[15];
>> > > }
>> > >
>> > >
>> > > so that it has a 'store' in its IR.
>> > >
>> > >
>> > > IR before vectorization :
>> > >
>> > > target datalayout = "e-m:e-p:32:32-f64:32:64-f80:32-n8:16:32-S128"
>> > > target triple = "x86_64-pc-linux-gnu"
>> > >
>> > > ; Function Attrs: nounwind
>> > > define void @foo(i32* nocapture readonly %a, i32* nocapture %sum) #0 {
>> > > entry:
>> > > %0 = load i32* %a, align 4, !tbaa !1
>> > > %arrayidx1 = getelementptr inbounds i32* %a, i32 1
>> > > %1 = load i32* %arrayidx1, align 4, !tbaa !1
>> > > %add = add nsw i32 %1, %0
>> > > %arrayidx2 = getelementptr inbounds i32* %a, i32 2
>> > > %2 = load i32* %arrayidx2, align 4, !tbaa !1
>> > > %add3 = add nsw i32 %add, %2
>> > > %arrayidx4 = getelementptr inbounds i32* %a, i32 3
>> > > %3 = load i32* %arrayidx4, align 4, !tbaa !1
>> > > %add5 = add nsw i32 %add3, %3
>> > > %arrayidx6 = getelementptr inbounds i32* %a, i32 4
>> > > %4 = load i32* %arrayidx6, align 4, !tbaa !1
>> > > %add7 = add nsw i32 %add5, %4
>> > > %arrayidx8 = getelementptr inbounds i32* %a, i32 5
>> > > %5 = load i32* %arrayidx8, align 4, !tbaa !1
>> > > %add9 = add nsw i32 %add7, %5
>> > > %arrayidx10 = getelementptr inbounds i32* %a, i32 6
>> > > %6 = load i32* %arrayidx10, align 4, !tbaa !1
>> > > %add11 = add nsw i32 %add9, %6
>> > > %arrayidx12 = getelementptr inbounds i32* %a, i32 7
>> > > %7 = load i32* %arrayidx12, align 4, !tbaa !1
>> > > %add13 = add nsw i32 %add11, %7
>> > > %arrayidx14 = getelementptr inbounds i32* %a, i32 8
>> > > %8 = load i32* %arrayidx14, align 4, !tbaa !1
>> > > %add15 = add nsw i32 %add13, %8
>> > > %arrayidx16 = getelementptr inbounds i32* %a, i32 9
>> > > %9 = load i32* %arrayidx16, align 4, !tbaa !1
>> > > %add17 = add nsw i32 %add15, %9
>> > > %arrayidx18 = getelementptr inbounds i32* %a, i32 10
>> > > %10 = load i32* %arrayidx18, align 4, !tbaa !1
>> > > %add19 = add nsw i32 %add17, %10
>> > > %arrayidx20 = getelementptr inbounds i32* %a, i32 11
>> > > %11 = load i32* %arrayidx20, align 4, !tbaa !1
>> > > %add21 = add nsw i32 %add19, %11
>> > > %arrayidx22 = getelementptr inbounds i32* %a, i32 12
>> > > %12 = load i32* %arrayidx22, align 4, !tbaa !1
>> > > %add23 = add nsw i32 %add21, %12
>> > > %arrayidx24 = getelementptr inbounds i32* %a, i32 13
>> > > %13 = load i32* %arrayidx24, align 4, !tbaa !1
>> > > %add25 = add nsw i32 %add23, %13
>> > > %arrayidx26 = getelementptr inbounds i32* %a, i32 14
>> > > %14 = load i32* %arrayidx26, align 4, !tbaa !1
>> > > %add27 = add nsw i32 %add25, %14
>> > > %arrayidx28 = getelementptr inbounds i32* %a, i32 15
>> > > %15 = load i32* %arrayidx28, align 4, !tbaa !1
>> > > %add29 = add nsw i32 %add27, %15
>> > >
>> > >
>> > > store i32 %add29, i32* %sum, align 4, !tbaa !1
>> > > ret void
>> > > }
>> > >
>> > >
>> > >
>> > > IR after SLP vectorization with appropriate flags :
>> > >
>> > > $ opt -S -slp-vectorizer -slp-vectorize-hor=1
>> -slp-vectorize-hor-store=1 test.ll -debug
>> > >
>> > >
>> > > (I hope i am passing the args correctly to opt)
>> > >
>> > > Subtarget features: SSELevel 3, 3DNowLevel 0, 64bit 1
>> > > SLP: Analyzing blocks in foo.
>> > > SLP: Found 1 stores to vectorize.
>> > > SLP: Vectorizing a list of length = 2.
>> > > ; ModuleID = 'test.ll'
>> > > target datalayout = "e-m:e-p:32:32-f64:32:64-f80:32-n8:16:32-S128"
>> > > target triple = "x86_64-pc-linux-gnu"
>> > >
>> > > ; Function Attrs: nounwind
>> > > define void @foo(i32* nocapture readonly %a, i32* nocapture %sum) #0 {
>> > > entry:
>> > > %0 = load i32* %a, align 4, !tbaa !1
>> > > %arrayidx1 = getelementptr inbounds i32* %a, i32 1
>> > > %1 = load i32* %arrayidx1, align 4, !tbaa !1
>> > > %add = add nsw i32 %1, %0
>> > > %arrayidx2 = getelementptr inbounds i32* %a, i32 2
>> > > %2 = load i32* %arrayidx2, align 4, !tbaa !1
>> > > %add3 = add nsw i32 %add, %2
>> > > %arrayidx4 = getelementptr inbounds i32* %a, i32 3
>> > > %3 = load i32* %arrayidx4, align 4, !tbaa !1
>> > > %add5 = add nsw i32 %add3, %3
>> > > %arrayidx6 = getelementptr inbounds i32* %a, i32 4
>> > > %4 = load i32* %arrayidx6, align 4, !tbaa !1
>> > > %add7 = add nsw i32 %add5, %4
>> > > %arrayidx8 = getelementptr inbounds i32* %a, i32 5
>> > > %5 = load i32* %arrayidx8, align 4, !tbaa !1
>> > > %add9 = add nsw i32 %add7, %5
>> > > %arrayidx10 = getelementptr inbounds i32* %a, i32 6
>> > > %6 = load i32* %arrayidx10, align 4, !tbaa !1
>> > > %add11 = add nsw i32 %add9, %6
>> > > %arrayidx12 = getelementptr inbounds i32* %a, i32 7
>> > > %7 = load i32* %arrayidx12, align 4, !tbaa !1
>> > > %add13 = add nsw i32 %add11, %7
>> > > %arrayidx14 = getelementptr inbounds i32* %a, i32 8
>> > > %8 = load i32* %arrayidx14, align 4, !tbaa !1
>> > > %add15 = add nsw i32 %add13, %8
>> > > %arrayidx16 = getelementptr inbounds i32* %a, i32 9
>> > > %9 = load i32* %arrayidx16, align 4, !tbaa !1
>> > > %add17 = add nsw i32 %add15, %9
>> > > %arrayidx18 = getelementptr inbounds i32* %a, i32 10
>> > > %10 = load i32* %arrayidx18, align 4, !tbaa !1
>> > > %add19 = add nsw i32 %add17, %10
>> > > %arrayidx20 = getelementptr inbounds i32* %a, i32 11
>> > > %11 = load i32* %arrayidx20, align 4, !tbaa !1
>> > > %add21 = add nsw i32 %add19, %11
>> > > %arrayidx22 = getelementptr inbounds i32* %a, i32 12
>> > > %12 = load i32* %arrayidx22, align 4, !tbaa !1
>> > > %add23 = add nsw i32 %add21, %12
>> > > %arrayidx24 = getelementptr inbounds i32* %a, i32 13
>> > > %13 = load i32* %arrayidx24, align 4, !tbaa !1
>> > > %add25 = add nsw i32 %add23, %13
>> > > %arrayidx26 = getelementptr inbounds i32* %a, i32 14
>> > > %14 = load i32* %arrayidx26, align 4, !tbaa !1
>> > > %add27 = add nsw i32 %add25, %14
>> > > %arrayidx28 = getelementptr inbounds i32* %a, i32 15
>> > > %15 = load i32* %arrayidx28, align 4, !tbaa !1
>> > > %add29 = add nsw i32 %add27, %15
>> > >
>> > >
>> > > store i32 %add29, i32* %sum, align 4, !tbaa !1
>> > > ret void
>> > > }
>> > >
>> > >
>> > > As observed above, SLP vectorization doesn't vectorize this code.
>> > >
>> > >
>> > > I tried debugging, just mentioning what i found -
>> > > the code hits vectorizeChainsInBlock() ->
>> if(ShouldStartVectorizeHorAtStore) ->
>> > > tryToVectorize() -> tryToVectorizePair() -> tryToVectorizeList() -
>> inside this function, it iterates in the
>> > >
>> > > for loop for checking if scalar instructions are of same type. It
>> exits for 2nd iteration in the loop as
>> > >
>> > > OpCode does not match , one opcode is 'add' and second is 'load'. So
>> it exits from function
>> > > 'tryToVectorizeList()', and comes back to function
>> 'tryToVectorize()', but fails to satisfy any of the
>> > >
>> > > further 'if' in that function and returns back without vectorizing
>> any of the code.
>> > >
>> > >
>> > > I will try to dig more into it and try to understand where the code
>> can be implemented.
>> > >
>> > >
>> > > Comments/suggestions/explanations are always welcomed !!
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > > On Fri, Sep 19, 2014 at 1:49 AM, Arnold <aschwaighofer at apple.com <
>> aschwaighofer at apple.com>> wrote:
>> > >
>> > > > There is experimental code to handle horizontal reductions hidden
>> behind a flag "slp-vectorize-hor" you could try to enable that. It won't
>> currently work for your example though because we only start to look for
>> reductions at phis and stores "slp-vectorize-hor-stores".
>> > > >
>> > > >
>> > > > You could start off by rewriting your example with a store and see
>> whether the tree match works after using both flags.
>> > > >
>> > > > F(int *sum ...){
>> > > > *sum =
>> a[0]+a[1]+a[2]+a[3]+a[4]+a[5]+a[6]+a[7]+a[8]+a[9]+a[10]+a[11]+a[12]+a[13]+a[14]+a[15];
>> > > > }
>> > > >
>> > > >
>> > > > If it works we would only need to look for horizontal reductions at
>> returns.
>> > > >
>> > > >
>> > > >
>> > > >
>> > > >
>> > > >
>> > > > Sent from my iPhone
>> > > >
>> > > > On Sep 18, 2014, at 12:24 PM, suyog sarda <sardask01 at gmail.com <
>> sardask01 at gmail.com>> wrote:
>> > > >
>> > > >
>> > > >
>> > > > > Hi Nadav,
>> > > > >
>> > > > >
>> > > > > Thanks for the quick reply !!
>> > > > >
>> > > > >
>> > > > > Ok, so as of now we are lacking capability to handle flat large
>> reductions.
>> > > > >
>> > > > >
>> > > > > I did go through function vectorizeChainsInBlock() (line number
>> 2862). In this function,
>> > > > >
>> > > > > we try to vectorize if we have phi nodes in the IR (several if's
>> check for phi nodes) i.e we try to
>> > > > >
>> > > > > construct tree that starts at chains.
>> > > > >
>> > > > >
>> > > > > Any pointers on how to join multiple trees? I will also try to
>> dig more into it.
>> > > > >
>> > > > >
>> > > > > Comments/Suggestions are most welcomed !!
>> > > > >
>> > > > >
>> > > > > On Fri, Sep 19, 2014 at 12:23 AM, Nadav Rotem <nrotem at apple.com <
>> nrotem at apple.com>> wrote:
>> > > > >
>> > > > > >
>> > > > > >
>> > > > > > > On Sep 18, 2014, at 11:48 AM, suyog sarda <
>> sardask01 at gmail.com <sardask01 at gmail.com>> wrote:
>> > > > > > >
>> > > > > > > Hi,
>> > > > > > >
>> > > > > > >
>> > > > > > > I am trying to understand LLVM vectorization implementation
>> and was looking into both loop and SLP vectorization.
>> > > > > > >
>> > > > > > >
>> > > > > > > test case 1:
>> > > > > > >
>> > > > > > >
>> > > > > > > int foo(int *a) {
>> > > > > > > int sum = 0,i;
>> > > > > > > for(i=0; i<16; i++)
>> > > > > > > sum += a[i];
>> > > > > > > return sum;
>> > > > > > > }
>> > > > > > >
>> > > > > > >
>> > > > > > > This code is vectorized by loop vectorizer where we calculate
>> scalar loop cost as 4 and vector loop cost as 2.
>> > > > > > >
>> > > > > > > Since vector loop cost is less and above reduction is legal
>> to vectorize, loop vectorizer produces vectorized code :
>> > > > > > >
>> > > > > > >
>> > > > > > > IR without vectorization:
>> > > > > > >
>> > > > > > > target datalayout =
>> "e-m:e-p:32:32-f64:32:64-f80:32-n8:16:32-S128"
>> > > > > > > target triple = "x86_64-pc-linux-gnu"
>> > > > > > >
>> > > > > > > ; Function Attrs: nounwind readonly
>> > > > > > > define i32 @foo(i32* nocapture readonly %a, i32 %n) #0 {
>> > > > > > > entry:
>> > > > > > > br label %for.body
>> > > > > > >
>> > > > > > > for.body: ; preds =
>> %for.body, %entry
>> > > > > > > %i.05 = phi i32 [ 0, %entry ], [ %inc, %for.body ]
>> > > > > > > %sum.04 = phi i32 [ 0, %entry ], [ %add, %for.body ]
>> > > > > > > %arrayidx = getelementptr inbounds i32* %a, i32 %i.05
>> > > > > > > %0 = load i32* %arrayidx, align 4, !tbaa !1
>> > > > > > > %add = add nsw i32 %0, %sum.04
>> > > > > > > %inc = add nsw i32 %i.05, 1
>> > > > > > > %exitcond = icmp eq i32 %i.05, 15
>> > > > > > > br i1 %exitcond, label %for.end, label %for.body
>> > > > > > >
>> > > > > > > for.end: ; preds =
>> %for.body
>> > > > > > > ret i32 %add
>> > > > > > > }
>> > > > > > >
>> > > > > > >
>> > > > > > > IR after vectorization:
>> > > > > > >
>> > > > > > > target datalayout =
>> "e-m:e-p:32:32-f64:32:64-f80:32-n8:16:32-S128"
>> > > > > > > target triple = "x86_64-pc-linux-gnu"
>> > > > > > >
>> > > > > > > ; Function Attrs: nounwind readonly
>> > > > > > > define i32 @foo(i32* nocapture readonly %a, i32 %n) #0 {
>> > > > > > > entry:
>> > > > > > > %backedge.overflow = icmp eq i32 15, -1
>> > > > > > > %overflow.check.anchor = add i32 0, 0
>> > > > > > > br i1 %backedge.overflow, label %scalar.ph(
>> http://scalar.ph/), label %overflow.checked
>> > > > > > >
>> > > > > > > overflow.checked: ; preds =
>> %entry
>> > > > > > > br i1 false, label %middle.block, label %vector.ph(
>> http://vector.ph/)
>> > > > > > >
>> > > > > > > vector.ph(http://vector.ph/):
>> ; preds = %overflow.checked
>> > > > > > > br label %vector.body
>> > > > > > >
>> > > > > > > vector.body: ; preds =
>> %vector.body, %vector.ph(http://vector.ph/)
>> > > > > > > %index = phi i32 [ 0, %vector.ph(http://vector.ph/) ], [
>> %index.next, %vector.body ]
>> > > > > > > %vec.phi = phi <4 x i32> [ zeroinitializer, %vector.ph(
>> http://vector.ph/) ], [ %14, %vector.body ]
>> > > > > > > %broadcast.splatinsert = insertelement <4 x i32> undef, i32
>> %index, i32 0
>> > > > > > > %broadcast.splat = shufflevector <4 x i32>
>> %broadcast.splatinsert, <4 x i32> undef, <4 x i32> zeroinitializer
>> > > > > > > %induction = add <4 x i32> %broadcast.splat, <i32 0, i32 1,
>> i32 2, i32 3>
>> > > > > > > %0 = extractelement <4 x i32> %induction, i32 0
>> > > > > > > %1 = getelementptr inbounds i32* %a, i32 %0
>> > > > > > > %2 = insertelement <4 x i32*> undef, i32* %1, i32 0
>> > > > > > > %3 = extractelement <4 x i32> %induction, i32 1
>> > > > > > > %4 = getelementptr inbounds i32* %a, i32 %3
>> > > > > > > %5 = insertelement <4 x i32*> %2, i32* %4, i32 1
>> > > > > > > %6 = extractelement <4 x i32> %induction, i32 2
>> > > > > > > %7 = getelementptr inbounds i32* %a, i32 %6
>> > > > > > > %8 = insertelement <4 x i32*> %5, i32* %7, i32 2
>> > > > > > > %9 = extractelement <4 x i32> %induction, i32 3
>> > > > > > > %10 = getelementptr inbounds i32* %a, i32 %9
>> > > > > > > %11 = insertelement <4 x i32*> %8, i32* %10, i32 3
>> > > > > > > %12 = getelementptr i32* %1, i32 0
>> > > > > > > %13 = bitcast i32* %12 to <4 x i32>*
>> > > > > > > %wide.load = load <4 x i32>* %13, align 4, !tbaa !1
>> > > > > > > %14 = add nsw <4 x i32> %wide.load, %vec.phi
>> > > > > > > %15 = add nsw <4 x i32> %induction, <i32 1, i32 1, i32 1,
>> i32 1>
>> > > > > > > %16 = icmp eq <4 x i32> %induction, <i32 15, i32 15, i32
>> 15, i32 15>
>> > > > > > > %index.next = add i32 %index, 4
>> > > > > > > %17 = icmp eq i32 %index.next, 16
>> > > > > > > br i1 %17, label %middle.block, label %vector.body,
>> !llvm.loop !5
>> > > > > > >
>> > > > > > > middle.block: ; preds =
>> %vector.body, %overflow.checked
>> > > > > > > %resume.val = phi i32 [ 0, %overflow.checked ], [ 16,
>> %vector.body ]
>> > > > > > > %trunc.resume.val = phi i32 [ 0, %overflow.checked ], [ 16,
>> %vector.body ]
>> > > > > > > %rdx.vec.exit.phi = phi <4 x i32> [ zeroinitializer,
>> %overflow.checked ], [ %14, %vector.body ]
>> > > > > > > %rdx.shuf = shufflevector <4 x i32> %rdx.vec.exit.phi, <4 x
>> i32> undef, <4 x i32> <i32 2, i32 3, i32 undef, i32 undef>
>> > > > > > > %bin.rdx = add <4 x i32> %rdx.vec.exit.phi, %rdx.shuf
>> > > > > > > %rdx.shuf1 = shufflevector <4 x i32> %bin.rdx, <4 x i32>
>> undef, <4 x i32> <i32 1, i32 undef, i32 undef, i32 undef>
>> > > > > > > %bin.rdx2 = add <4 x i32> %bin.rdx, %rdx.shuf1
>> > > > > > > %18 = extractelement <4 x i32> %bin.rdx2, i32 0
>> > > > > > > %cmp.n = icmp eq i32 16, %resume.val
>> > > > > > > br i1 %cmp.n, label %for.end, label %scalar.ph(
>> http://scalar.ph/)
>> > > > > > >
>> > > > > > > scalar.ph(http://scalar.ph/):
>> ; preds = %middle.block, %entry
>> > > > > > > %bc.resume.val = phi i32 [ %resume.val, %middle.block ], [
>> 0, %entry ]
>> > > > > > > %bc.trunc.resume.val = phi i32 [ %trunc.resume.val,
>> %middle.block ], [ 0, %entry ]
>> > > > > > > %bc.merge.rdx = phi i32 [ 0, %entry ], [ %18, %middle.block
>> ]
>> > > > > > > br label %for.body
>> > > > > > >
>> > > > > > > for.body: ; preds =
>> %for.body, %scalar.ph(http://scalar.ph/)
>> > > > > > > %i.05 = phi i32 [ %bc.trunc.resume.val, %scalar.ph(
>> http://scalar.ph/) ], [ %inc, %for.body ]
>> > > > > > > %sum.04 = phi i32 [ %bc.merge.rdx, %scalar.ph(
>> http://scalar.ph/) ], [ %add, %for.body ]
>> > > > > > > %arrayidx = getelementptr inbounds i32* %a, i32 %i.05
>> > > > > > > %19 = load i32* %arrayidx, align 4, !tbaa !1
>> > > > > > > %add = add nsw i32 %19, %sum.04
>> > > > > > > %inc = add nsw i32 %i.05, 1
>> > > > > > > %exitcond = icmp eq i32 %i.05, 15
>> > > > > > > br i1 %exitcond, label %for.end, label %for.body,
>> !llvm.loop !8
>> > > > > > >
>> > > > > > > for.end: ; preds =
>> %middle.block, %for.body
>> > > > > > > %add.lcssa = phi i32 [ %add, %for.body ], [ %18,
>> %middle.block ]
>> > > > > > > ret i32 %add.lcssa
>> > > > > > > }
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > > Now for test case 2, where we unroll the loop totally :
>> > > > > > >
>> > > > > > > int foo(int *a) {
>> > > > > > > int sum = 0;
>> > > > > > > sum =
>> a[0]+a[1]+a[2]+a[3]+a[4]+a[5]+a[6]+a[7]+a[8]+a[9]+a[10]+a[11]+a[12]+a[13]+a[14]+a[15];
>> > > > > > > return sum;
>> > > > > > > }
>> > > > > > >
>> > > > > > >
>> > > > > > > This code is not vectorized by SLP vectorization.
>> > > > > > >
>> > > > > > >
>> > > > > > > IR before vectorization:
>> > > > > > >
>> > > > > > >
>> > > > > > > test.ll
>> > > > > > >
>> > > > > > >
>> > > > > > > target datalayout =
>> "e-m:e-p:32:32-f64:32:64-f80:32-n8:16:32-S128"
>> > > > > > > target triple = "x86_64-pc-linux-gnu"
>> > > > > > >
>> > > > > > > ; Function Attrs: nounwind readonly
>> > > > > > > define i32 @foo(i32* nocapture readonly %a, i32 %n) #0 {
>> > > > > > > entry:
>> > > > > > > %0 = load i32* %a, align 4, !tbaa !1
>> > > > > > > %arrayidx1 = getelementptr inbounds i32* %a, i32 1
>> > > > > > > %1 = load i32* %arrayidx1, align 4, !tbaa !1
>> > > > > > > %add = add nsw i32 %1, %0
>> > > > > > > %arrayidx2 = getelementptr inbounds i32* %a, i32 2
>> > > > > > > %2 = load i32* %arrayidx2, align 4, !tbaa !1
>> > > > > > > %add3 = add nsw i32 %add, %2
>> > > > > > > %arrayidx4 = getelementptr inbounds i32* %a, i32 3
>> > > > > > > %3 = load i32* %arrayidx4, align 4, !tbaa !1
>> > > > > > > %add5 = add nsw i32 %add3, %3
>> > > > > > > %arrayidx6 = getelementptr inbounds i32* %a, i32 4
>> > > > > > > %4 = load i32* %arrayidx6, align 4, !tbaa !1
>> > > > > > > %add7 = add nsw i32 %add5, %4
>> > > > > > > %arrayidx8 = getelementptr inbounds i32* %a, i32 5
>> > > > > > > %5 = load i32* %arrayidx8, align 4, !tbaa !1
>> > > > > > > %add9 = add nsw i32 %add7, %5
>> > > > > > > %arrayidx10 = getelementptr inbounds i32* %a, i32 6
>> > > > > > > %6 = load i32* %arrayidx10, align 4, !tbaa !1
>> > > > > > > %add11 = add nsw i32 %add9, %6
>> > > > > > > %arrayidx12 = getelementptr inbounds i32* %a, i32 7
>> > > > > > > %7 = load i32* %arrayidx12, align 4, !tbaa !1
>> > > > > > > %add13 = add nsw i32 %add11, %7
>> > > > > > > %arrayidx14 = getelementptr inbounds i32* %a, i32 8
>> > > > > > > %8 = load i32* %arrayidx14, align 4, !tbaa !1
>> > > > > > > %add15 = add nsw i32 %add13, %8
>> > > > > > > %arrayidx16 = getelementptr inbounds i32* %a, i32 9
>> > > > > > > %9 = load i32* %arrayidx16, align 4, !tbaa !1
>> > > > > > > %add17 = add nsw i32 %add15, %9
>> > > > > > > %arrayidx18 = getelementptr inbounds i32* %a, i32 10
>> > > > > > > %10 = load i32* %arrayidx18, align 4, !tbaa !1
>> > > > > > > %add19 = add nsw i32 %add17, %10
>> > > > > > > %arrayidx20 = getelementptr inbounds i32* %a, i32 11
>> > > > > > > %11 = load i32* %arrayidx20, align 4, !tbaa !1
>> > > > > > > %add21 = add nsw i32 %add19, %11
>> > > > > > > %arrayidx22 = getelementptr inbounds i32* %a, i32 12
>> > > > > > > %12 = load i32* %arrayidx22, align 4, !tbaa !1
>> > > > > > > %add23 = add nsw i32 %add21, %12
>> > > > > > > %arrayidx24 = getelementptr inbounds i32* %a, i32 13
>> > > > > > > %13 = load i32* %arrayidx24, align 4, !tbaa !1
>> > > > > > > %add25 = add nsw i32 %add23, %13
>> > > > > > > %arrayidx26 = getelementptr inbounds i32* %a, i32 14
>> > > > > > > %14 = load i32* %arrayidx26, align 4, !tbaa !1
>> > > > > > > %add27 = add nsw i32 %add25, %14
>> > > > > > > %arrayidx28 = getelementptr inbounds i32* %a, i32 15
>> > > > > > > %15 = load i32* %arrayidx28, align 4, !tbaa !1
>> > > > > > > %add29 = add nsw i32 %add27, %15
>> > > > > > > ret i32 %add29
>> > > > > > > }
>> > > > > > >
>> > > > > > > $ opt -S -slp-vectorizer -slp-vectorize-hor test.ll -debug -o
>> test2.ll
>> > > > > > >
>> > > > > > > Features:+64bit,+sse2
>> > > > > > > CPU:generic
>> > > > > > >
>> > > > > > > Subtarget features: SSELevel 3, 3DNowLevel 0, 64bit 1
>> > > > > > > SLP: Analyzing blocks in foo.
>> > > > > > >
>> > > > > > >
>> > > > > > > test2.ll (IR after SLP vectorization) :
>> > > > > > >
>> > > > > > > target datalayout =
>> "e-m:e-p:32:32-f64:32:64-f80:32-n8:16:32-S128"
>> > > > > > > target triple = "x86_64-pc-linux-gnu"
>> > > > > > >
>> > > > > > > ; Function Attrs: nounwind readonly
>> > > > > > > define i32 @foo(i32* nocapture readonly %a, i32 %n) #0 {
>> > > > > > > entry:
>> > > > > > > %0 = load i32* %a, align 4, !tbaa !1
>> > > > > > > %arrayidx1 = getelementptr inbounds i32* %a, i32 1
>> > > > > > > %1 = load i32* %arrayidx1, align 4, !tbaa !1
>> > > > > > > %add = add nsw i32 %1, %0
>> > > > > > > %arrayidx2 = getelementptr inbounds i32* %a, i32 2
>> > > > > > > %2 = load i32* %arrayidx2, align 4, !tbaa !1
>> > > > > > > %add3 = add nsw i32 %add, %2
>> > > > > > > %arrayidx4 = getelementptr inbounds i32* %a, i32 3
>> > > > > > > %3 = load i32* %arrayidx4, align 4, !tbaa !1
>> > > > > > > %add5 = add nsw i32 %add3, %3
>> > > > > > > %arrayidx6 = getelementptr inbounds i32* %a, i32 4
>> > > > > > > %4 = load i32* %arrayidx6, align 4, !tbaa !1
>> > > > > > > %add7 = add nsw i32 %add5, %4
>> > > > > > > %arrayidx8 = getelementptr inbounds i32* %a, i32 5
>> > > > > > > %5 = load i32* %arrayidx8, align 4, !tbaa !1
>> > > > > > > %add9 = add nsw i32 %add7, %5
>> > > > > > > %arrayidx10 = getelementptr inbounds i32* %a, i32 6
>> > > > > > > %6 = load i32* %arrayidx10, align 4, !tbaa !1
>> > > > > > > %add11 = add nsw i32 %add9, %6
>> > > > > > > %arrayidx12 = getelementptr inbounds i32* %a, i32 7
>> > > > > > > %7 = load i32* %arrayidx12, align 4, !tbaa !1
>> > > > > > > %add13 = add nsw i32 %add11, %7
>> > > > > > > %arrayidx14 = getelementptr inbounds i32* %a, i32 8
>> > > > > > > %8 = load i32* %arrayidx14, align 4, !tbaa !1
>> > > > > > > %add15 = add nsw i32 %add13, %8
>> > > > > > > %arrayidx16 = getelementptr inbounds i32* %a, i32 9
>> > > > > > > %9 = load i32* %arrayidx16, align 4, !tbaa !1
>> > > > > > > %add17 = add nsw i32 %add15, %9
>> > > > > > > %arrayidx18 = getelementptr inbounds i32* %a, i32 10
>> > > > > > > %10 = load i32* %arrayidx18, align 4, !tbaa !1
>> > > > > > > %add19 = add nsw i32 %add17, %10
>> > > > > > > %arrayidx20 = getelementptr inbounds i32* %a, i32 11
>> > > > > > > %11 = load i32* %arrayidx20, align 4, !tbaa !1
>> > > > > > > %add21 = add nsw i32 %add19, %11
>> > > > > > > %arrayidx22 = getelementptr inbounds i32* %a, i32 12
>> > > > > > > %12 = load i32* %arrayidx22, align 4, !tbaa !1
>> > > > > > > %add23 = add nsw i32 %add21, %12
>> > > > > > > %arrayidx24 = getelementptr inbounds i32* %a, i32 13
>> > > > > > > %13 = load i32* %arrayidx24, align 4, !tbaa !1
>> > > > > > > %add25 = add nsw i32 %add23, %13
>> > > > > > > %arrayidx26 = getelementptr inbounds i32* %a, i32 14
>> > > > > > > %14 = load i32* %arrayidx26, align 4, !tbaa !1
>> > > > > > > %add27 = add nsw i32 %add25, %14
>> > > > > > > %arrayidx28 = getelementptr inbounds i32* %a, i32 15
>> > > > > > > %15 = load i32* %arrayidx28, align 4, !tbaa !1
>> > > > > > > %add29 = add nsw i32 %add27, %15
>> > > > > > > ret i32 %add29
>> > > > > > > }
>> > > > > > >
>> > > > > > >
>> > > > > > > Shouldn't the SLP vectorizer vectorize above code? Am I
>> missing out something?
>> > > > > > >
>> > > > > > > How does loop unrolling affect the final vectorization of
>> code?
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > >
>> > > > > >
>> > > > > >
>> > > > > >
>> > > > > > Hi Suyog,
>> > > > > >
>> > > > > >
>> > > > > > Thank you for taking the time to investigate the performance of
>> the vectorizers. You are correct that the SLP-vectorizer should be able to
>> do something about the second case (the unrolled one). Please look into
>> line 2861 in the SLP-vectorizer. This is where we try to analyze reductions
>> and vectorize them. At the moment we don’t have support for a flat yet
>> large reduction that is the result of unrolled loops because the current
>> algorithm tries to construct trees that start at the chains, and it does
>> not support joining multiple trees.
>> > > > > >
>> > > > > >
>> > > > > >
>> http://llvm.org/docs/doxygen/html/SLPVectorizer_8cpp_source.html#l02861
>> > > > > >
>> > > > > >
>> > > > > > Thanks,
>> > > > > > Nadav
>> > > > > >
>> > > > > >
>> > > > > >
>> > > > > >
>> > > > > > > I tried to debug above IR. Since there is no store in above
>> IR, SLP vectorization doesn't call
>> > > > > > > vectorizeStoreChains(). In vectorizeChainsInBlock(), as there
>> is no phi node in above IR, it
>> > > > > > > also fails to vectorize the IR.
>> > > > > > >
>> > > > > > >
>> > > > > > > If it is perfectly legal to vectorize above IR, are we
>> lacking code for it right now? Or its not
>> > > > > > >
>> > > > > > > possible to vectorize above code (as far as i know, it is
>> perfectly possible to vectorize above code)?
>> > > > > > >
>> > > > > > > If its possible, can someone help me/point out specifics how
>> to start for implementing vectorization for such code?
>> > > > > > >
>> > > > > > > (We do have function calls isConsecutiveAccess() to identify
>> it. Is it a case for horizontal reduction, though the term
>> > > > > > > 'reduction' is associated with loops/phi nodes.)
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > > This brings to me another question - if we perform aggressive
>> loop unrolling, we might loose opportunity for
>> > > > > > >
>> > > > > > > vectorization as visible above !!
>> > > > > > >
>> > > > > > >
>> > > > > > > (I was looking into bug 20035, when i encountered above
>> issue. Though both are not related.)
>> > > > > > >
>> > > > > > >
>> > > > > > > Your comments/suggestions are most awaited.
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > > --
>> > > > > > > With regards,
>> > > > > > > Suyog Sarda
>> > > > > > >
>> >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> > > > > > >
>> >
>> > > > > > >
>> > > > > > >
>> > > > > >
>> > > > > >
>> > > > > >
>> > > > > >
>> > > > >
>> > > > >
>> > > > >
>> > > > >
>> > > > > --
>> > > > > With regards,
>> > > > > Suyog Sarda
>> > > > >
>> >
>> > > > >
>> > > > >
>> >
>> > > > >
>> > > > >
>> > > >
>> > > >
>> > > >
>> > > >
>> > >
>> > >
>> > >
>> > >
>> > > --
>> > > With regards,
>> > > Suyog Sarda
>> > >
>> >
>> > >
>> > >
>> >
>> > >
>> > >
>> > >
>> >
>> >
>> >
>> >
>> > --
>> > With regards,
>> > Suyog Sarda
>> >
>> >
>> >
>> >
>> >
>> >
>>
>
>
--
With regards,
Suyog Sarda
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