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Sounds like a case of "write the code to show what it should look like and show what numbers you get compared to the code they posted"? But remember that the e

Sounds like a case of "write the code to show what it should look like and show what numbers you get compared to the code they posted"? But remember that the exercise wasn't "to run the n-body simulation", or even "to run maths", but just to see how fast "plain Python" is with the release of 3.11 - using numpy and scipy, which rely on compiled code that they can hand work off to, would make any runtime value you get completely meaningless for the purposes of benchmarking pure Python =)

bee_rider 4y ago
Clearly the solution is to add a jitter to Python that identifies code that looks like an matrix multiplication, and calls numpy instead.
- deleted 4y ago
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PaulHoule 4y ago
The viability of Python for scientific work is predicated on using Python as glue code for code written in other languages. If pandas ran at the speed of Python people really would be using Julia or some other language instead of Python.
- TheRealPomax 4y ago
  Agreed! Which is why this is a benchmark of Python, not of "running scientific computations". Of course you're going to use numpy/scipy for that. Or you start using R, if you really care. This article isn't about what the code does, there are a million improvements to make if that's what we were looking at. It's about how fast a small program that uses some standard pure Python patterns runs today, compared to a few versions ago, with some "but what if not Python?". Because "you wouldn't write this code" applies to those examples as well. In JS you'd tap into a C-compiled native library the exact same way numpy/scipy does in Python. And in C, if you absolutely needed the fastest performance, the code would be full of micro optimizations and a sprinkling of assembler.
bornfreddy 4y ago
Who cares about "pure" Python? The whole point is that you have powerful libraries at your fingertips and you can leverage those to get fast code. Can you do the same in Go? JavaScript? PHP? I'm tired of people comparing languages but then leaving out the major winning points for Python. Numpy & co. are integral part of Python, no serious ddv would use just "pure" Python for numerical methods, ever. So let's compare real'world Python, shall we? I doubt Go has a chance then. /rant
- TheRealPomax 4y ago
  Turns out other people don't care if you're tired of something, because they're not you. Plenty of folks do care about how much pure Python has improved. That's how it got to the front page. If you want to test baseline performance, you want a little program that's small enough to be easily understood, but just elaborate enough to hit enough of the standard programming patterns. So it's basically irrelevant what this code actually does, we're just looking at how much faster Python has gotten. Because that's something we care about. We don't care that "go is faster" or "C is faster", we use Python and we like to know that the newer versions actually are substantially better than the older versions we used to (or still have to) work with.

bee_rider 4y ago

Clearly the solution is to add a jitter to Python that identifies code that looks like an matrix multiplication, and calls numpy instead.

deleted 4y ago
[deleted]

PaulHoule 4y ago

The viability of Python for scientific work is predicated on using Python as glue code for code written in other languages. If pandas ran at the speed of Python people really would be using Julia or some other language instead of Python.

TheRealPomax 4y ago
Agreed! Which is why this is a benchmark of Python, not of "running scientific computations". Of course you're going to use numpy/scipy for that. Or you start using R, if you really care. This article isn't about what the code does, there are a million improvements to make if that's what we were looking at. It's about how fast a small program that uses some standard pure Python patterns runs today, compared to a few versions ago, with some "but what if not Python?". Because "you wouldn't write this code" applies to those examples as well. In JS you'd tap into a C-compiled native library the exact same way numpy/scipy does in Python. And in C, if you absolutely needed the fastest performance, the code would be full of micro optimizations and a sprinkling of assembler.

bornfreddy 4y ago

Who cares about "pure" Python? The whole point is that you have powerful libraries at your fingertips and you can leverage those to get fast code. Can you do the same in Go? JavaScript? PHP? I'm tired of people comparing languages but then leaving out the major winning points for Python. Numpy & co. are integral part of Python, no serious ddv would use just "pure" Python for numerical methods, ever. So let's compare real'world Python, shall we? I doubt Go has a chance then. /rant

TheRealPomax 4y ago
Turns out other people don't care if you're tired of something, because they're not you. Plenty of folks do care about how much pure Python has improved. That's how it got to the front page. If you want to test baseline performance, you want a little program that's small enough to be easily understood, but just elaborate enough to hit enough of the standard programming patterns. So it's basically irrelevant what this code actually does, we're just looking at how much faster Python has gotten. Because that's something we care about. We don't care that "go is faster" or "C is faster", we use Python and we like to know that the newer versions actually are substantially better than the older versions we used to (or still have to) work with.