hi, i'm currently trying to find a way to generate simd code for python loops that operate on numpy arrays. The IRC is quite busy so I think I'd rather post it as an email...
I have familiarized myself with pypy and read most of the docs and read
the code in the metainterp to understand how the traces are built.
I translated pypy with --withmod-micronumpy enabled (installed numpy
pypy fork in an virtual env) and gave the jitviewer a try.
Here is a sample program that does not really make sense, but I think it
would contain opportunity to generate SIMD instructions.
```
import numpy
def invoke():
a = numpy.zeros(1024)
b = numpy.zeros(1024)
c = numpy.zeros(1024)
# (2)
for i in range(0,1024):
c[i] = a[i] + b[i]
# (3)
c = a + b
if __name__ == '__main__':
i = 0
# (1)
while i < 500:
invoke()
i += 1
```
Here is the trace of invoke visible in jitviewer (uploaded to
https://postimg.org/image/kbntluw55/full/).
Here are some questions I have that would really help me to get going:
(1) Is there a better way to get loops hot?
(2) I cannot really figure out what all those trace/loop parameters are.
obviously i can guess some but most of them do not really make sense to
me. Am I missing some settings to be able to display more information
for the trace?
In addition to that I do not really see any chance to generate a
simd loop for (2). Every array access is (arguably) guarded by an array
index overflow and I think to skip that check would be invalid.
(3) There is no trace generated for this instruction? Does this
internally call a c function?
(4) What in our opinion makes sense to vectorize with simd instructions?
Could you provide some sample loops/code (ranging from simple to complex
loops)?
I would really appreciate any help and/or push into the right direction.
Best,
Richard
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