Hi,
On Wed, 27 Jul 2016, Papa, Florin wrote:
I have been working with NumPyPy to evaluate its performance and it
seems significantly slower compared to CPython NumPy or even PyPy NumPy
(installed with pip).
After having a brief look at the your table, I'm very confused by this
assessment:
Hi,
> Is there an official benchmark suite for NumPy or a more relevant workload to
> compare against CPython? What is NumPyPy's maturity / adoption rate from your
> knowledge?
I do not think there is. I have been looking for something similar for
over a year. It seems though people tend to mak
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 Florin,
On Wed, 27 Jul 2016, Papa, Florin wrote:
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.
Thank
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
On 27/07/2016 3:35 AM, Papa, Florin wrote:
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 4.8669