[LLVMdev] IndVar widening in IndVarSimplify causing performance regression on GPU programs
Justin Holewinski
jholewinski at nvidia.com
Fri Oct 24 11:29:24 PDT 2014
On Fri, 24 Oct 2014, Jingyue Wu wrote:
> Hi,
> I noticed a significant performance regression (up to 40%) on some internal CUDA benchmarks (a reduced example presented below). The root cause of this regression seems
> that IndVarSimpilfy widens induction variables assuming arithmetics on wider integer types are as cheap as those on narrower ones. However, this assumption is wrong at
> least for the NVPTX64 target.
>
> Although the NVPTX64 target supports 64-bit arithmetics, since the actual NVIDIA GPU typically has only 32-bit integer registers, one 64-bit arithmetic typically ends up
> with two machine instructions taking care of the low 32 bits and the high 32 bits respectively. I haven't looked at other GPU targets such as R600, but I suspect this
> problem is not restricted to the NVPTX64 target.
>
> Below is a reduced example:
> __attribute__((global)) void foo(int n, int *output) {
> for (int i = 0; i < n; i += 3) {
> output[i] = i * i;
> }
> }
>
> Without widening, the loop body in the PTX (a low-level assembly-like language generated by NVPTX64) is:
> BB0_2: // =>This Inner Loop Header: Depth=1
> mul.lo.s32 %r5, %r6, %r6;
> st.u32 [%rd4], %r5;
> add.s32 %r6, %r6, 3;
> add.s64 %rd4, %rd4, 12;
> setp.lt.s32 %p2, %r6, %r3;
> @%p2 bra BB0_2;
> in which %r6 is the induction variable i.
>
> With widening, the loop body becomes:
> BB0_2: // =>This Inner Loop Header: Depth=1
> mul.lo.s64 %rd8, %rd10, %rd10;
> st.u32 [%rd9], %rd8;
> add.s64 %rd10, %rd10, 3;
> add.s64 %rd9, %rd9, 12;
> setp.lt.s64 %p2, %rd10, %rd1;
> @%p2 bra BB0_2;
>
> Although the number of PTX instructions in both versions are the same, the version with widening uses more mul.lo.s64, add.s64, and setp.lt.s64 instructions which are
> more expensive than their 32-bit counterparts. Indeed, the SASS code (disassembly of the actual machine code running on GPUs) of the version with widening looks
> significantly longer.
>
> Without widening (7 instructions):
> .L_1:
> /*0048*/ IMUL R2, R0, R0;
> /*0050*/ IADD R0, R0, 0x1;
> /*0058*/ ST.E [R4], R2;
> /*0060*/ ISETP.NE.AND P0, PT, R0, c[0x0][0x140], PT; /*0068*/ IADD R4.CC, R4, 0x4;
> /*0070*/ IADD.X R5, R5, RZ;
> /*0078*/ @P0 BRA `(.L_1);
>
> With widening (12 instructions):
> .L_1:
> /*0050*/ IMUL.U32.U32 R6.CC, R4, R4;
> /*0058*/ IADD R0, R0, -0x1;
> /*0060*/ IMAD.U32.U32.HI.X R8.CC, R4, R4, RZ;
> /*0068*/ IMAD.U32.U32.X R8, R5, R4, R8;
> /*0070*/ IMAD.U32.U32 R7, R4, R5, R8;
> /*0078*/ IADD R4.CC, R4, 0x1;
> /*0088*/ ST.E [R2], R6;
> /*0090*/ IADD.X R5, R5, RZ;
> /*0098*/ ISETP.NE.AND P0, PT, R0, RZ, PT;
> /*00a0*/ IADD R2.CC, R2, 0x4;
> /*00a8*/ IADD.X R3, R3, RZ;
> /*00b0*/ @P0 BRA `(.L_1);
>
> I hope the issue is clear up to this point. So what's a good solution to fix this issue? I am thinking of having IndVarSimplify consult TargetTransformInfo about the cost
> of integer arithmetics of different types. If operations on wider integer types are more expensive, IndVarSimplify should disable the widening.
TargetTransformInfo seems like a good place to put a hook for this.
You're right that 64-bit integer math will be slower for NVPTX targets, as
the hardware needs to emulate 64-bit integer ops with 32-bit ops.
How much is register usage affected by this in your benchmarks?
>
> Another thing I am concerned about: are there other optimizations that make similar assumptions about integer widening? Those might cause performance regression too just
> as IndVarSimplify does.
>
> Jingyue
>
>
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