Hi,

My name is Sylvain, I am an intern at Kalray and I work on improving the GCC 
backend for the KVX target.  The KVX ISA has dedicated instructions for the 
handling of complex numbers, which cannot be selected by GCC due to how complex 
numbers are handled internally.  My goal is to make GCC able to expose to 
machine description files new patterns dealing with complex numbers.  I already 
have a proof of concept which can increase performance even on other backends 
like x86 if the new patterns are implemented.

My approach is to prevent the lowering of complex operations when the backend 
can handle it natively and work directly on complex modes (SC, DC, CDI, CSI, 
CHI, CQI).  The cplxlower pass looks for supported optabs related to complex 
numbers and use them directly.  Another advantage is that native operations can 
now go through all GIMPLE passes and preserve most optimisations like FMA 
generation.

Vectorization is also preserved with native complex operands, although some 
functions were updated. Because vectorization assumes that inner elements are 
scalar and complex cannot be considered as scalar, some functions which only 
take scalars have been adapted or duplicated to handle complex elements.

I've also changed the representation of complex numbers during the expand pass. 
 READ_COMPLEX_PART and WRITE_COMPLEX_PART have been transformed into target 
hooks, and a new hook GEN_RTX_COMPLEX allows each backend to choose its 
preferred complex representation in RTL.  The default one uses CONCAT like 
before, but the KVX backend uses registers with complex mode containing both 
real and imaginary parts.

Now each backend can add its own native complex operations with patterns in its 
machine description. The following example implements a complex multiplication 
with mode SC on the KVX backend:
(define_insn "mulsc3"
  [(set (match_operand:SC 0 "register_operand" "=r")
        (mult:SC (match_operand:SC 1 "register_operand" "r")
                 (match_operand:SC 2 "register_operand" "r")))]
  ""
  "fmulwc %0 = %1, %2"
  [(set_attr "type" "mau_fpu")]
)

The main patch affects around 1400 lines of generic code, mostly located in 
expr.cc and tree-complex.cc. These are mainly additions or the result of the 
move of READ_COMPLEX_PART and WRITE_COMPLEX_PART from expr.cc to target hooks.

I know that ARM developers have added partial support of complex instructions.  
However, since they are operating during the vectorization, and are promoting 
operations on vectors of floating point numbers that looks like operations on 
(vectors of) complex numbers, their approach misses simple cases.  At this 
point they create operations working on vector of floating point numbers which 
will be caught by dedicated define_expand later.  On the other hand, our 
approach propagates complex numbers through all the middle-end and we have an 
easier time to recombine the operations and recognize what ARM does.  Some 
choices will be needed to merge our two approaches, although I've already 
reused their work on complex rotations in my implementation.

Results:

I have tested my implementation on multiple code samples, as well as a few 
FFTs.  On a simple in-place radix-2 with precomputed twiddle seeds (2 complex 
mult, 1 add, and 1 sub per loop), the compute time has been divided by 3 when 
compiling with -O3 (because calls to __mulsc3 are replaced by native 
instructions) and shortened by 20% with -ffast-math.  In both cases, the 
achieved performance level is now on par with another version coded using 
intrinsics.  These improvements do not come exclusively from the new generated 
hardware instructions, the replacement of CONCATs to registers prevents GCC 
from generating instructions to extract the real and imaginary part into their 
own registers and recombine them later.

This new approach can also brings a performance uplift to other backends.  I 
have tried to reuse the same complex representation in rtl as KVX for x86, and 
a few patterns.  Although I still have useless moves on large programs, simple 
examples like below already show performance uplift.

_Complex float add(_Complex float a, _Complex float b)
{
  return a + b;
}

Using "-O2" the assembly produced is now on paar with llvm and looks like :

add:
        addps  %xmm1, %xmm0
        ret

Choices to be done:
  - Currently, ARM uses optab which start with "c" like "cmul" to distinguish 
between a real floating point numbers and complex numbers.  Since we keep 
complex mode, this could be simply done with mul<mode>.
  - Currently the parser does some early optimizations and lowering that could 
be moved into the cplxlower pass.  For example, i've changed a bit how complex 
rotations by 90° and 270° are processed, which are recognized in fold-const.cc. 
 A call to a new COMPLEX_ROT90/270 internal function is now inserted, which is 
then lowered or kept in the cplxlower pass.  Finally the widening_mul pass can 
generate COMPLEX_ADD_ROT90/270 internal function, which are expanded using the 
cadd90/270 optabs, else COMPLEX_ROT90/270 are expanded using new crot90/270 
optabs.
  - Currently, we have to duplicate the preferred_simd_mode since in only 
accept scalar modes, if we unify enough, we could have a new type that would be 
a union of scalar_mode and complex_mode, but we did not do it since it would 
incur many modifications.
  - Declaration of complex vector through attribute directives, this would be a 
new C extension (and clang does not support it either).
  - The KVX ISA supports some fused conjugate and operations (ex: a + 
conjf(b)), which are caught directly in the combine pass if the corresponding 
pattern in present the backend. This solution is simple, but it also mays be 
caught in the middle-end like FMAs.

Currently supported patterns:
  - all basic arithmetic operations for scalar and vector complex modes (add, 
mul, neg, ...)
  - conj<mode> for the conjugate operation, using a new conj_optab
  - crot90<mode>/crot270<mode> for complex rotations, using new optabs

I would like to have your opinion on my approach. I can send you the patch if 
you want.

Best regards,

Sylvain Noiry



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