Hi Frank,
You would need to compile pypy with “--lldebug”, then run your workload in GDB
to see where it crashes. Another option is to obtain a core dump (if you cannot
run the workload directly in GDB), then analyze the core dump in GDB. Please
see [1] and [2] for this.
[1]
http://stackoverf
Hello,
I read this announcement [1] saying that "over 99% of the upstream numpy test
suite" is passed. Is this when using pypy with the upstream numpy (thanks to
the incremental improvements brought to cpyext) or is it when using pypy with
numpypy?
I also found this link [2], tracking numpypy
> We usually hang out on IRC, you can find me there most evenings European time.
> The zip file is not a very iterative-freindly format for improving the
> benchmarks, how can
> I contribute to your work?
> - There should be some kind of shell script that downloads and installs the
> packages f
Hi Armin,
>The table also shows that PyPy NumPyPy is really slower, even with
>vectorization enabled.
>It seems that the current focus of our work, on continuing to improve cpyext
>instead of
>numpypy, is a good idea.
Does this mean that the main direction is to support NumPy (through improving
I am sorry, I mistakenly switched the header of the table, the middle column is
actually the result for PyPy NumPyPy. The correct table is this:
Benchmark CPython NumPy PyPy NumPyPy PyPy NumPy
cauchy 1 5.838852812 4.866947551
pointbypoint1 4.922654347
Hi Yury,
The table contains run time values, normalized to the CPython Numpy results.
This means that a value of 1 is equal to the CPython NumPy result, less than 1
means faster than CPython NumPy and more than 1 is slower than CPython NumPy.
Let's consider the following line in the table:
Benc
Hi all,
This is Florin Papa from the Dynamic Scripting Languages Team at Intel
Corporation.
I am trying to build pypy to use the refcount garbage collector, for testing
purposes. I am following the indications here [1], but the following command
fails:
pypy ../../rpython/bin/rpython -O2 --gc=
