Instead of saying "you are writing Python, but you are not writing Python," it might be better to phrase it as, "you are just using Python to orchestrate highly performant C programs."
To be precise, if you are writing "you are just using to orchestrate highly performant machine code". It's a fact of programming life and not specific to Python.
@@miraculixxs I would not frame compiled languages as orchestrating assembly code? You are creating the assembly code youself (by cooperating with the compiler)
Wow, cutting out the questions at the end made the Q&A feel so streamlined. I hope more lecture videos start doing that. Probably saved all viewers 5+ minutes! Emery gives some fantastic lectures, please invite him back in the future.
Wow! Big thank you to Prof. Berger and his team for the ground braking profiler from someone who had to upgrade RAM to 64Gb for data science projects. Just tried it and it is a masterpiece. The visual representation of the profiler opens doors into code optimization for everybody.
I have a script with a native backend, pure python backend, and numpy backend that I tested with this profiler. Very interesting results. The numpy implementation, and the pure python implementation both had around 31% native code, despite the numpy implementation being around twice as fast. Scalene showed me a few good places to optimize, and I was able to cut the pure python implementation down from 35 to 25 seconds. The c backend was still about 100x faster (0.2 seconds), with the output "Scalene: Program did not run for long enough to profile."
Informative, funny without being lame. The run-time graph was great motivation. It might be even more compelling if you work an actual problem then weave how Scalene addresses the gap in the current Python profiler landscape.
Small update - Py-spy has multiprocessing. Just use --subprocesses to catch those. I liked way better same output but rendered by pyroscope. Comparison function being really helpfull.
Love that he mentioned Dennard Scaling. My Parallel Programming prof introduced us to this back in 2016 and made the argument that it was actually the driver for increasing need for parallelized programs.
I was very surprised to see that the `np.array(range(10**7))` in the code example was never addressed. On my machine, removing the redundant `np.array` call from the `random` line only saved about 2.5 seconds while changing `np.array(range(10**7))` to `np.arange(10**7)` saved almost 8 seconds. Of course the first optimization saved memory, which the second didn’t really do.
You know the reason for it and think its too obviouse or do you want the answer? In case of 2: "np.array(range(10**7))" creates a python (slow) list via python range function (slow) that than needs to be cast (slow) into a numpy c-optimised object. "np.arange(10**7)" passes the argument 10**7 directly into the numpy C-optimised code and creates the array inside using a fast function.
Violent Agreement haha, super nice tool, makes python even more valuable. The ability to see what your code does is what I need most in a programing language.
I am certainly going to try this. I still prefer C++, but Python has many benefits, especially the ease of getting to work cross platform. I can write a bit of Python, including optimized modules on a windows 64 bit laptop and then move it to an arm based small form factor computer running Linux. That is certainly possible using C/C++, but there are more steps in compilation and some basic IO issues that always need solving.
I currently do not have access to my big boy pc that has all my big python codebases so I can't be 100% sure but from testing my scripts and such it seems like a cool profiler. Much better than the ones I tried to use before.
Seems interesting, but I couldn’t get it to work on Windows. Every time I run it, I get a different “Error in program being profiled”. I still get a report, but it’s pretty useless when the program fails after a second.
I laughed when he used a Rotate to turn into a table, making a subtle reference to this "GoingNative 2013 C++ Seasoning" Talk by Sean Parent: th-cam.com/video/W2tWOdzgXHA/w-d-xo.html Edit: Amazing talk as always, have watched Emery talks for years now, and he always deliver the goods!
I love that people are finally coming around to the realization that performance is, in fact, still important... and the best way to produce performant Python code is to not code in Python.
Great talk! But in my opinion Python does damage the high performance ecosystem (period). While there is a small group of (let's call them) experts (I'd count myself as one) who have spent years in learning language internals and ways to make Python fast (Cython, Numba, numpy gymnastics, Dask, C/C++/Fortran wrappers and so on), the high-level APIs Python offers always come with limitations, are very complex and hard to maintain (lot of different technologies/languages) and newcomers will write code with terrible performance when they implement whatever they need and can't find. That's fact, I am doing code-reviews and reveal from week to week code which runs 10000x slower than it could on clusters with thousands of CPUs, that's just insane. I use python for at least 10 years in scientific programming and have written tons of packages but our students and PhD candidates struggle to squeeze out moderate performance out of Python and need a lot of training. I think it's a waste of time and resources and it's just beating a dead horse. It's time to move forward and be open for alternatives which solve a lot of these issues, like Julia.
Pretty much left out not at all metal languages that have really good JITs and built in optimization options such as one of the oldest languages of all, Lisp.
LISP used to be considered a slower language, but that was when C was considered "high level". If you want maximum performance specifically, C or C++ or rust are king with lisp still being slower in most metrics.
And Forth. You had FORTH in 1970, and it's really the most "metal" of any of them. Or, rather, it's *as* metal as assembly, and far more so than the other ones you listed.
Mid 80s computers: Conveniently forgot the Amiga ... whose chip set was used with Video Toaster which started a special effects revolution in television & film, before Commodore managed to bankrupt the brand.
Well, this was nothing I hadn't learned first hand from 10 years of writing python. TBH, not sure why things like Cython and PyPy aren't more popular. It's an easy way to get a huge performance increase without really doing much hand-optimization.
Admittedly, I'm not that informed of what python is "generally" used for, but from my experience mostly working with ml or data processing stuff, you mostly use it to call libraries anyways, so it almost makes sense that existing profilers don't do a lot of things. Like ofc you don't care about c time, or gpu usage, or memory leaks; those you generally assume are handled by people who wrote your libraries. And if you are writing libraries for python, I'd assume you would be profiling your stuff in the language you are actually writing in, not in python. Now having typed that, I'm guess that last assumption is wrong somehow?
In complex data processing pipelines you still need to know which commands take the most time to complete so you can search for ways to replace them, their arguments or even their order and oftentimes you may increase performance by a few hundred percent this way.
Looking at the steaming pile of (censored) that the Python interpreter is and the 15 million people who use it, I can't help but wonder why nobody seems to care to actually improve the interpreter. You could get a ton of mileage from actually creating a modern interpreter, like V8 for JavaScript. Instead, people say, "nah, just just Python to orchestrate FFI calls". 😂
Scalene is a statistical sampler so it does not generally distort executions of programs being profiled. The way that Scalene profilers multiprocessing does not interfere with the programs being executed, instead aggregating information from each process only when profiling ends.
Instead of saying "you are writing Python, but you are not writing Python," it might be better to phrase it as, "you are just using Python to orchestrate highly performant C programs."
yes I often explain it as being a conductor.
To be precise, if you are writing "you are just using to orchestrate highly performant machine code". It's a fact of programming life and not specific to Python.
@@miraculixxs Salty python programmer detected. 😂👌
@@miraculixxs I would not frame compiled languages as orchestrating assembly code?
You are creating the assembly code youself (by cooperating with the compiler)
@@diogoantunes5473 The compiler is a Jerk! But the mofo is right all the time XD
Wow, cutting out the questions at the end made the Q&A feel so streamlined. I hope more lecture videos start doing that. Probably saved all viewers 5+ minutes!
Emery gives some fantastic lectures, please invite him back in the future.
Also he repeated the question, which was good.
Wow! Big thank you to Prof. Berger and his team for the ground braking profiler from someone who had to upgrade RAM to 64Gb for data science projects. Just tried it and it is a masterpiece. The visual representation of the profiler opens doors into code optimization for everybody.
I have a script with a native backend, pure python backend, and numpy backend that I tested with this profiler. Very interesting results. The numpy implementation, and the pure python implementation both had around 31% native code, despite the numpy implementation being around twice as fast. Scalene showed me a few good places to optimize, and I was able to cut the pure python implementation down from 35 to 25 seconds. The c backend was still about 100x faster (0.2 seconds), with the output "Scalene: Program did not run for long enough to profile."
Informative, funny without being lame. The run-time graph was great motivation. It might be even more compelling if you work an actual problem then weave how Scalene addresses the gap in the current Python profiler landscape.
Hey, the guy that made the randomizer and Coz! I like this guy, excited for the talk :)
I love Emery Berger, such a charismatic guy and is always working on interesting projects.
Small update - Py-spy has multiprocessing. Just use --subprocesses to catch those. I liked way better same output but rendered by pyroscope. Comparison function being really helpfull.
27:00 ooh is that why sometimes, it's impossible to CTRL+C a running python program ?
Love that he mentioned Dennard Scaling. My Parallel Programming prof introduced us to this back in 2016 and made the argument that it was actually the driver for increasing need for parallelized programs.
"1 core too hot. 128 cores do trick." -Your professor
was very confused when the size of c++ map was compared to a python dictionary. just checked but `sizeof(std::unordered_map) is 56`
I was very surprised to see that the `np.array(range(10**7))` in the code example was never addressed.
On my machine, removing the redundant `np.array` call from the `random` line only saved about 2.5 seconds while changing `np.array(range(10**7))` to `np.arange(10**7)` saved almost 8 seconds. Of course the first optimization saved memory, which the second didn’t really do.
You know the reason for it and think its too obviouse or do you want the answer? In case of 2:
"np.array(range(10**7))" creates a python (slow) list via python range function (slow) that than needs to be cast (slow) into a numpy c-optimised object.
"np.arange(10**7)" passes the argument 10**7 directly into the numpy C-optimised code and creates the array inside using a fast function.
Violent Agreement haha, super nice tool, makes python even more valuable. The ability to see what your code does is what I need most in a programing language.
I love Emery's talks, always so informative and well-structured
Great talk and tool too! Will be trying it out for sure, thanks
Great talk and great profiler!
I am certainly going to try this. I still prefer C++, but Python has many benefits, especially the ease of getting to work cross platform.
I can write a bit of Python, including optimized modules on a windows 64 bit laptop and then move it to an arm based small form factor computer running Linux. That is certainly possible using C/C++, but there are more steps in compilation and some basic IO issues that always need solving.
You might want to try Julia!
Incredibly well explained and engaging. Good stuff 👍
I currently do not have access to my big boy pc that has all my big python codebases so I can't be 100% sure but from testing my scripts and such it seems like a cool profiler. Much better than the ones I tried to use before.
Seems interesting, but I couldn’t get it to work on Windows. Every time I run it, I get a different “Error in program being profiled”. I still get a report, but it’s pretty useless when the program fails after a second.
I laughed when he used a Rotate to turn into a table, making a subtle reference to this "GoingNative 2013 C++ Seasoning" Talk by Sean Parent: th-cam.com/video/W2tWOdzgXHA/w-d-xo.html
Edit: Amazing talk as always, have watched Emery talks for years now, and he always deliver the goods!
Hadn't heard of this guy. Sounds like I'll be binge watching all his stuff now - like I did when I came across Dave Beazley years ago.
“…or in the case of numpy, FORTRAN, at the end of the day, not a lie.” This quote at 16:30.
How is that the case?
I love that people are finally coming around to the realization that performance is, in fact, still important... and the best way to produce performant Python code is to not code in Python.
Totally agree. Now you have people programming embedded systems with micropython.
No. Internet throughput still makes Python performance irrelevant, mr. Gamer. So do Python compilers.
Yo. I use scalene everyday. Its dope. Nice to see Emery giving talk. :D
Great talk! But in my opinion Python does damage the high performance ecosystem (period). While there is a small group of (let's call them) experts (I'd count myself as one) who have spent years in learning language internals and ways to make Python fast (Cython, Numba, numpy gymnastics, Dask, C/C++/Fortran wrappers and so on), the high-level APIs Python offers always come with limitations, are very complex and hard to maintain (lot of different technologies/languages) and newcomers will write code with terrible performance when they implement whatever they need and can't find. That's fact, I am doing code-reviews and reveal from week to week code which runs 10000x slower than it could on clusters with thousands of CPUs, that's just insane. I use python for at least 10 years in scientific programming and have written tons of packages but our students and PhD candidates struggle to squeeze out moderate performance out of Python and need a lot of training. I think it's a waste of time and resources and it's just beating a dead horse. It's time to move forward and be open for alternatives which solve a lot of these issues, like Julia.
Do you know the Python implementation of GraalVM?
Why would he say the guy is lying when he claims to like GIL? I've found it very helpful when working with databases that run with threading
This is exactly the same with any language that is not C++ or lower
Pretty much left out not at all metal languages that have really good JITs and built in optimization options such as one of the oldest languages of all, Lisp.
LISP used to be considered a slower language, but that was when C was considered "high level". If you want maximum performance specifically, C or C++ or rust are king with lisp still being slower in most metrics.
How to use this with fastapi. Documentation is not clear about how to use it with web frameworks?
And Forth. You had FORTH in 1970, and it's really the most "metal" of any of them. Or, rather, it's *as* metal as assembly, and far more so than the other ones you listed.
RE: the intro: what about Lisp?
Mid 80s computers: Conveniently forgot the Amiga ... whose chip set was used with Video Toaster which started a special effects revolution in television & film, before Commodore managed to bankrupt the brand.
Well, this was nothing I hadn't learned first hand from 10 years of writing python. TBH, not sure why things like Cython and PyPy aren't more popular. It's an easy way to get a huge performance increase without really doing much hand-optimization.
Admittedly, I'm not that informed of what python is "generally" used for, but from my experience mostly working with ml or data processing stuff, you mostly use it to call libraries anyways, so it almost makes sense that existing profilers don't do a lot of things.
Like ofc you don't care about c time, or gpu usage, or memory leaks; those you generally assume are handled by people who wrote your libraries. And if you are writing libraries for python, I'd assume you would be profiling your stuff in the language you are actually writing in, not in python. Now having typed that, I'm guess that last assumption is wrong somehow?
In complex data processing pipelines you still need to know which commands take the most time to complete so you can search for ways to replace them, their arguments or even their order and oftentimes you may increase performance by a few hundred percent this way.
Looking at the steaming pile of (censored) that the Python interpreter is and the 15 million people who use it, I can't help but wonder why nobody seems to care to actually improve the interpreter. You could get a ton of mileage from actually creating a modern interpreter, like V8 for JavaScript. Instead, people say, "nah, just just Python to orchestrate FFI calls". 😂
Most important Python optimization tip: Don't use it, use something like Flogram instead
it is really a very bad idea to do multiprocess profiling. You increase system noise, that's why not a single tool implements multiprocessing.
What's the alternative?
Scalene is a statistical sampler so it does not generally distort executions of programs being profiled. The way that Scalene profilers multiprocessing does not interfere with the programs being executed, instead aggregating information from each process only when profiling ends.