20 ms·

I've been lightly banging the drum the last few years that a lot of programmers don't seem to understand how fast computers are, and often ship code that is jus

by jerf 4y ago

I've been lightly banging the drum the last few years that a lot of programmers don't seem to understand how fast computers are, and often ship code that is just miserably slower than it needs to be, like the code in this article, because they simply don't realize that their code ought to be much, much faster. There's still a lot of very early-2000s ideas of how fast computers are floating around. I've wondered how much of it is the still-extensive use of dynamic scripting languages and programmers not understanding just how much performance you can throw away how quickly with those things. It isn't even just the slowdown you get just from using one at all; it's really easy to pile on several layers of indirection without really noticing it. And in the end, the code seems to run "fast enough" and nobody involved really notices that what is running in 750ms really ought to run in something more like 200us. I have a hard time using (pure) Python anymore for any task that speed is even remotely a consideration for anymore. Not only is it slow even at the best of times, but so many of its features beg you to slow down even more without thinking about it.

agumonkey 4y ago
Remember when people were counting cpu cycles and instruction size to ensure performance ?
- SV_BubbleTime 4y ago
  I program for embedded… still do that.
  - agumonkey 4y ago
    find people like you, form a club, write articles and enjoy .. 1 reader :)
Yajirobe 4y ago
Python allows one to save development time in exchange for execution time
- morelisp 4y ago
  Python hasn't saved me development time since distutils was the right and only way to build things.
- zasdffaa 4y ago
  Get beyond a certain size of python program and you lose dev time. IOW you lose both. It's not a huge size either.
  - Yajirobe 4y ago
    Like... youtube?
    - zasdffaa 4y ago
      could you elaborate please - is all of youtube including the streaming, all python?
- stonogo 4y ago
  That's only an excuse if you're sociopathically profit-oriented. The program is developed orders of magnitude fewer times than it is run. Shitty performance, like pollution, is an externality that can be ignored but should not.
  - geysersam 4y ago
    Shitty performance certainly is bad, but it is not an externality like emissions into the atmosphere. The fundamental difference is that the customer (and only the customer) is harmed by bad performance, while emissions harms everyone.
    - tylerchr 4y ago
      I'm not so sure. Emissions don't harm everyone instantly; they affect people disproportionately and only impact everyone over time as the effects accumulate. Sure, maybe bad performance only affects the customer initially, but can't you help but wonder what the cumulative opportunity cost of bad performance on civilization has been? The predominant perception among nontechnical people is that computers are fundamentally unreliable and slow. It doesn't seem unreasonable to think that might be holding up the rate of innovation.
yieldcrv 4y ago
There is a balance, like sure there is inefficient code but often its because that code is accessing an I/O resource inefficiently, and so the CPU and RAM speed of the host machine isnt the bottlebeck no matter what dumb things the programmer does So you dont need to pretty much ever reinvent or even use a hackerrank algorithm, you need to understand that the database compute instance has a fast cpu and lots of RAM too
Guest19023892 4y ago
I upgraded a desktop machine the last time I visited my family. It was a Windows 7 computer that was at least 10 years old with 4GB of ram. They wanted to use it online for basic web browsing, so I thought I'd install Windows 10 for security reasons and drop in a modern SSD to upgrade the old 7200rpm drive to make it more snappy. Well, it felt slower after the "upgrade". Clicking the start menu and opening something like the Downloads or Documents folder was basically instant before. Now, with Windows 10 and the new SSD there was a noticeable delay when opening and browsing folders. It really made me wonder how it would be running something like Windows 98 and websites of the past on modern hardware.
- askafriend 4y ago
  Let the caches warm up a little!
  - bombcar 4y ago
    This is part of it - many things are "fast enough" that were you used to have caches that would display nearly instantly, now you don't have those - it reads from disk each time it needs to show the folder, etc. This is very visible in any app that no longer maintains "local state" but instead is just a web browser to some online state (think: Electron, teams, etc). Disconnect the web or slow it down and it all goes to hell.
- moffkalast 4y ago
  That's interesting, I cloned a Win10 installation on a HDD to a sata SSD a year or two back and the speed difference was considerable. Especially something like Atom that took minutes to open before was ready to go in like 10 seconds afterwards. A lot of things remained slow though.
  - digitallyfree 4y ago
    Yeah the change from a 7200 HDD to an SSD for those 10 year old machines provides a very considerable improvement. It goes from "unusable" to "moderate" performance for general web browsing and business duties. I'm talking about Windows 10 on 4G C2Q or Phenom/Phenom II machines - they aren't fast but they're very usable with a SSD and GPU in place.
marcosdumay 4y ago
It's not only a matter of 750ms instead of 200ms. I'm astonished every time I open some tool like Visual Studio, SAP Power Designer, or Libre Office that can stay for the most part of a minute on its loading screen. What do those tools even do for that long? They can read enough data from the disk to overflow my computer's main memory a few times during it.
- chopin 4y ago
  Phone home. I suspect much of the lag is network latency.
  - marcosdumay 4y ago
    Power Designer surely is phoning home but this isn't nearly slow enough to matter here. AFAIK Visual Studio phones in during the operation, and not on startup. Libre Office almost certainly isn't phoning anywhere. I didn't include the slowest starting software that I know, Oracle SQL Developer, because it's clear that all the slowness is caused by phoning home, several times for some reason. But that's not the case for all of them. EDIT: Or, maybe it's useful to put it another way. The slowest region on the world for me to ping is around Eastern Asia and Australia. Some times, I get around 1.5s round trip time for there. A minute has around 40 of those.
  - schroeding 4y ago
    I use Visual Studio on an air-gapped machine with no (active) network cards (so Windows / winsock2 knows there is nothing that can respond and any connection should error out immediately) and it still takes almost a minute. At least VS is just kinda slow, maybe it's the XML parser :D
  - bentcorner 4y ago
    I work at a BigCorp that ships desktop software (but none of the above products) and network latency is (usually) pretty easy to extract out of the boot critical path. Blocking UI with network calls is a big no-no, and I expect any sizeable organization to have similar guidelines. Work like in the OP's article is probably the most difficult - it's work that is necessary, cannot be deferred, but is still slow. So it requires an expert to dig into it.
vbezhenar 4y ago
Back in time all you needed for perfect performance is to use C and proper algorithms. It was easy. Nowadays you need vector operations, you need to utilise GPU, you need to utilise various accelerators. For me it is black magic.
- flaviut 4y ago
  I think you're trying too hard. Something that you do a lot? Fine, write it in C/C++/Rust. It's something that costs thousands/millions of dollars of compute? Ok, maybe it's worth it for you to spend a month on, put your robe on, and start chanting in latin.
- AnIdiotOnTheNet 4y ago
  But perfect performance isn't even the benchmark, it's "not ridiculously slow". This is what is meant by "Computers are fast, but you don't know it", you don't even know how ludicrously fast computers are because so much stuff is so insanely slow. They're so fast that, in the vast majority of cases, you don't even need optimization, you just need non-pessimization: https://youtu.be/pgoetgxecw8 https://youtu.be/pgoetgxecw8
- LeifCarrotson 4y ago
  To be really fast, yes. Those are optimizations that allow you to go beyond the speed of just C and proper algorithms. But C and proper algorithms are still fast - Moore's law is going wider, yes, and single-threaded advancements aren't as impressive as they used to be, but solid C code and proper algorithms will still be faster than it was before! What's not fast is when, instead of using a hashmap when you should have used a B-tree, you instead store half the data in a relational database from one microservice and the other half on the blockchain and query it using a zero-code platform provided by a third vendor.
- Schroedingersat 4y ago
  These things only net you one or two orders of magnitude (and give you very little or even negative power efficiency gain), or maybe 3 for the gpu. This pales in comparison to the 4-6 orders of magnitude induced by thoughtless patterns, excessive abstraction, bloat, and user-hostile network round trips (this one is more like 10 orders of magnitude). Write good clean code in a way that your compiler can easily reason about to insert suitable vector operations (a little easier in c++, rust, zig etc. than c) and it's perfect performance in my book even if it isn't saturating all the cores
fmakunbound 4y ago
I’m afraid we just “deploy more pods” these days
mrtranscendence 4y ago
> And in the end, the code seems to run "fast enough" and nobody involved really notices that what is running in 750ms really ought to run in something more like 200us. Nobody has created a language that is both thousands of times faster than Python and nearly as straightforward to learn and to use. The closest thing I know of might be Julia, but that has its own performance problems and is tied closely to its AI/ML niche. Even within that niche I'm certainly not going to get most data scientists to write their code in C or C++ (or heaven forbid Rust) to solve a performance impediment that they've generally been able to work around. It's great that you've been able to switch to higher-performance languages, but not everyone can do that easily enough to make it worth doing.
- divan 4y ago
  Go hit a really sweet spot here.
- stefanos82 4y ago
  > Nobody has created a language that is both thousands of times faster than Python and nearly as straightforward to learn and to use. Not Python-based, but Lua-based is Nelua [1] If you like Lua's syntax, LISP's metaprogramming abilities, and C's performance, well there you have it! [1] https://github.com/edubart/nelua-lang https://github.com/edubart/nelua-lang
- morelisp 4y ago
  The "iterate from notebook to production" process which is common everywhere but the largest data engineering groups rules out anything with manual memory management from becoming popular with data science work. Some data scientists I know like (or even love) Scala, but that tends to blow up once it's handed over to the data engineers as Scala supports too many paradigms and just a couple DSs will probably manage to find all of them in one program. We use Go extensively for other things, and most data scientists I've worked with sketching ideas in Go liked it a lot, but the library support just isn't there, and it's not really a priority for any of the big players who are all committed to Python wrapper + C/C++/GPU core, or stock Java stacks. (The performance also isn't quite there yet compared to the top C and C++ libraries, but it's improving.)
divan 4y ago
So much this. I wonder what would be the software engineering landscape today if hardware specs were growing like 10% per year...
munificent 4y ago
I agree 100%. I wish every software engineer would spent at least a little time writing some programs in bare C and running them to get a feel for how fast a native executable can start up and run. It is breathtaking if you're used to running scripting languages and VMs. Related anecdote: My blog used to be written using Jekyll with Pygments for syntax highlighting. As the number of posts increased, it got closer and closer. Eventually, it took about 20 seconds to refresh a simple text change in a single blog post. I eventually decided to just write my own damn blog engine completely from scratch in Dart. Wrote my own template language, build graph, and syntax highlighter. By having a smart build system that knew which pages actually needed to be regenerated based on what data actually changed, I hoped to get very fast incremental rebuilds in the common case where only text inside a single post had changed. Before I got the incremental rebuild system working, I worked on getting it to just to a full build of the entire blog: every post page, pages, for each tag, date archives, and RSS support. I diffed it against the old blog to ensure it produced the same output. Once I got that working... I realized I didn't even need to implement incremental rebuilds. It could build the entire blog and every single post from scratch in less than a second. I don't know how people tolerate slow frameworks and build systems.
- jcelerier 4y ago
  > I agree 100%. I wish every software engineer would spent at least a little time writing some programs in bare C and running them to get a feel for how fast a native executable can start up and run. It is breathtaking if you're used to running scripting languages and VMs. Conversely when 99.9% of the software you use in your daily life is blazing fast C / C++, having to do anything in other stacks is a complete exercise in frustration, it feels like going back a few decades in time
  - p1esk 4y ago
    Conversely when 99.9% of the software you use in your daily life is user friendly Python, having to do anything in C/C++ is a complete exercise in frustration, it feels like going back a few decades in time
lynguist 4y ago
I agree except for the Python bit, which is factually wrong. Python allows you to program as if you’re a jazz pianist. You can improvise, iterate and have fun. And when you found a solution you just refactor it and use numba. Boom, it runs the same speed as a compiled language. I once wrote one little program that ran in 24 min without numba and ca. 8 seconds with numba.
- willis936 4y ago
  Dozens of instances of a C GUI can launch in the time it takes to launch a hello world python program.
  - lmm 4y ago
    But who wants dozens of instances of a C GUI? I'd be the first to complain about latency where it maters, but launching a Python program is perceptually instant (and significantly lower-latency than many nominally "faster" languages, IME).
  - geysersam 4y ago
    Yes but what kind of comparison is that: 1. How often do you need to execute 1000 GUI instances? 2. How often do you need to print "hello world"? The right tool for the right job.
    - willis936 4y ago
      This is a discussion about computers being slow. As in a person asks a computer to do something and the human waits while the computer does it. So python isn't the right tool for any job that involves human interaction.
      - geysersam 4y ago
        Nah that's too general. A lot of website/app backends use Django or Fastapi and they work fine. Many more use PHP, also not a language famed for extreme performance. It depends on the application. Personally I wouldn't use Python for a GUI (because I'd use JS/TS).
pjvsvsrtrxc 4y ago
And yet, even with all the evidence that modern, heavily-bloated software development is AWFUL (constant bugs and breakage because no one writing code understands any of the software sitting between them and the machine, much less understands the machine; Rowhammer, Spectre, Meltdown, and now Hertzbleed; sitting there waiting multiple seconds for something to launch up another copy of the web browser you already have running just so that you can have chat, hi Discord)... you still have all the people in the comments below trying to come up with reasons why "oh no it's actually good, the poor software developers would have to actually learn something instead of copying code off of Stack Overflow without understanding it".
- doliveira 4y ago
  During work I'm constantly reminded of how much better and snappier MSN Messenger was when compared to Teams.
yoyohello13 4y ago
I felt this pretty viscerally recently. I did Advent of Code 2021 in python last year. My day job is programming in Python so I didn't really think about the execution speed of my solutions much. As a fun exercise this year I've been doing Advent of Code 2020 in C, and my god it's crazy how much faster my solutions seem to execute. These are just little toy problems, but even still the speed difference is night and day. Although, I still find Python much easier to read and maintain, but that may just be I'm more experienced with the language.
- jeroenhd 4y ago
  > Although, I still find Python much easier to read and maintain, but that may just be I'm more experienced with the language. Python is definitely easier to read and maintain if you have loads of dependencies. C dependency management is a pain. If you can read and write a little C, you should consider giving C#/Java/Kotlin/Swift a try. They're probably an order of magnitude slower than C if you write them in a maintainable style, but they're still much faster than Python. If you're doing stuff like web APIs then ASP.NET/Spring will perform very admirably without manually optimizing code, for example. You might find that these languages are C-like enough to understand and Python-like enough to be productive in. Or you might not, but it's worth a shot! I personally believe that C is difficult if not impossible to properly to maintain long term, at least not as much as the faster alternatives. On the other hand my experience with Python is that it's one of the slowest mainstream languages out there, relying heavily on C libraries to get acceptable performance.
kossTKR 4y ago
I hope one day latency in general will be "back to normal". I still remember how fast console based computing, an old gameboy or a 90's macintosh would be - click a button and stuff would show up instantly. There was a tactility present with computers that's gone today. Today everything feels sluggish - just writing this comment on my $3000 Macbook Pro and i can feel the latency, sometimes there's even small pauses. A little when i write stuff, a lot when i drag windows. Hopefully the focus on 100hz+ screens in tech in general will put more focus on latency from click to screen print - now when resolution and interface graphics in general are close to biological limits.
- 41b696ef1113 4y ago
  >Hopefully the focus on 100hz+ screens in tech Come again? I think anything beyond 60hz still qualifies as niche. Vendors are still selling 720p laptops.
  - kllrnohj 4y ago
    Most flagship Android phones are >60hz and have been for a few years. Flagship iPhones and iPads are >60hz. Very nearly every gaming laptop is >60hz. Many new TVs are >60hz with inputs to match. These are not niche markets.
  - theandrewbailey 4y ago
    My guess is that few people have stopped to compare them. I've never knowingly seen a 100+hz screen in person, so I stopped by a local store. Sure enough, I could tell that the motion was smoother. Bought 2. After using those, I can feel my older monitors that I'm using to write this are choppy.
    - LoveMortuus 4y ago
      But do you notice the smoothness in the day to day basis or have you, in a way, crippled yourself, because now the majority of monitors feel choppy to you? Sounds a bit like the, 'Never meet your heroes', thingy.
Tade0 4y ago
> And in the end, the code seems to run "fast enough" and nobody involved really notices that what is running in 750ms really ought to run in something more like 200us. At least with Chrome's V8, the difference is not that big. Sure, it loses to C/C++, because it can't vectorize and uses orders of magnitude more memory, but at least in the Computer Language Benchmarks Game it's "just" 2-4x slower. I remember getting a faster program doing large matrix multiplication in JavaScript than in C with -o1, because V8 figured out that I'm reading from and writing to the same cell, so optimised that out, which gave it an edge, because in both cases the memory bandwidth limited the speed of execution. As for Electron and the like: half of the reason why they're slow is that document reflows are not minimized, so the underlying view engine works really, really hard to re-render the same thing over and over again. It's not nearly as visible in web apps, because these in turn are often slowed down by the HTTP connection limit(hardcoded to six in most browsers).
- jerf 4y ago
  Languages top out at around 50x, and that's the extreme of pure CPython to C. For as many factors of magnitude as I am talking about, you have to be screwing up algorithms, networks, and a whole bunch of other things too. Python and similar languages like Ruby really do make it easy to accidentally pile things on top of each other, but you can screw up in pure assembler with enough work put into it. Assembler doesn't stop you from being accidentally quadratic or using networks in a silly way.
geysersam 4y ago
More important than the language, is using the right tool for the job. If you are using the scientific Python stack, correctly, you'll have a difficult time beating that with c++. For many applications. While producing way simpler and more maintainable code.
dekhn 4y ago
I don't really find python slow for what I do (typically writing UIs around computer vision systems) but also, several years back I made a microcontroller-based self-balancing robot. It was hard to debug the PID and the sensor, so I replaced it with a Pi Zero and the main robot loop ran in python- enough to read the accelerometer, compute a PID update, and send motor instructions- 100 times a second. If there was a problem (say, another heavy process, like computer vision, running on the single CPU) it would eventually not respond fast enough and the robot would fall over. Most of the time it's not that you need a faster language, it's that you need to write faster code. I was working on a problem recently where random.choices was slow but I realized that due to the structure of my problem I could convert it to numpy and get a 100X speedup.
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1vuio0pswjnm7 4y ago
"I have a hard time using (pure) Python anymore for any task that speed is even remotely a consideration anymore. Not only is it slow even at the best of times, but so many of its features beg you to slow down even more without thinking about it."
pjmlp 4y ago
I learned that lesson with Tcl and Perl, 20 years ago. If there isn't a compiler in the box (JIT or AOT), I won't be using language XYZ, unless forced by customers. The only reason I use Python is for UNIX scripting.
solatic 4y ago
> any task that speed is even remotely a consideration for anymore How do you know whether or not speed is a consideration? Yes, OP delivered impressive efficiency gains. I'm sure he could improve the efficiency even more by dropping into pure Assembly. But is it worth it? The prime consideration is not execution speed but maintainability. The further that OP got away from pure Python, the more difficult to maintain the code became. That's a downside. Now, OP describes an important technique because in the real world, you have a performance budget. Code needs to execute at speeds that return quickly enough to the user, or long execution is financially expensive (i.e. cloud computing resources), etc. But optimizing beyond what the budget requires is wasteful in terms of time needed to do the optimization as well as harmful in terms of negatively impacting future maintainability.
- Tomis02 4y ago
  > The prime consideration is not execution speed but maintainability. Why? And how did you measure this drop in maintainability? I'm asking because I see developers prioritize _perceived_ maintainability over _measurable_ things that matter to the user (like performance).
tbran 4y ago
Haha, what C/C++ web framework should I use instead of Django/Rails/JS-whatever? Performance is a consideration, but I'm not going to reinvent a bunch of packages because of it. This kind of blanket comment that "scripting languages are too slow" makes it sound like you shouldn't use them for anything, but they are perfectly adequate for many tasks. I'm more likely to have network and DB slowdowns than problems with scripting languages.

by jerf 4y ago

agumonkey 4y ago

Remember when people were counting cpu cycles and instruction size to ensure performance ?

SV_BubbleTime 4y ago
I program for embedded… still do that.
- agumonkey 4y ago
  find people like you, form a club, write articles and enjoy .. 1 reader :)

Yajirobe 4y ago

Python allows one to save development time in exchange for execution time

morelisp 4y ago
Python hasn't saved me development time since distutils was the right and only way to build things.
zasdffaa 4y ago
Get beyond a certain size of python program and you lose dev time. IOW you lose both. It's not a huge size either.
- Yajirobe 4y ago
  Like... youtube?
  - zasdffaa 4y ago
    could you elaborate please - is all of youtube including the streaming, all python?
stonogo 4y ago
That's only an excuse if you're sociopathically profit-oriented. The program is developed orders of magnitude fewer times than it is run. Shitty performance, like pollution, is an externality that can be ignored but should not.
- geysersam 4y ago
  Shitty performance certainly is bad, but it is not an externality like emissions into the atmosphere. The fundamental difference is that the customer (and only the customer) is harmed by bad performance, while emissions harms everyone.
  - tylerchr 4y ago
    I'm not so sure. Emissions don't harm everyone instantly; they affect people disproportionately and only impact everyone over time as the effects accumulate. Sure, maybe bad performance only affects the customer initially, but can't you help but wonder what the cumulative opportunity cost of bad performance on civilization has been? The predominant perception among nontechnical people is that computers are fundamentally unreliable and slow. It doesn't seem unreasonable to think that might be holding up the rate of innovation.

yieldcrv 4y ago

There is a balance, like sure there is inefficient code but often its because that code is accessing an I/O resource inefficiently, and so the CPU and RAM speed of the host machine isnt the bottlebeck no matter what dumb things the programmer does So you dont need to pretty much ever reinvent or even use a hackerrank algorithm, you need to understand that the database compute instance has a fast cpu and lots of RAM too

Guest19023892 4y ago

I upgraded a desktop machine the last time I visited my family. It was a Windows 7 computer that was at least 10 years old with 4GB of ram. They wanted to use it online for basic web browsing, so I thought I'd install Windows 10 for security reasons and drop in a modern SSD to upgrade the old 7200rpm drive to make it more snappy. Well, it felt slower after the "upgrade". Clicking the start menu and opening something like the Downloads or Documents folder was basically instant before. Now, with Windows 10 and the new SSD there was a noticeable delay when opening and browsing folders. It really made me wonder how it would be running something like Windows 98 and websites of the past on modern hardware.

askafriend 4y ago
Let the caches warm up a little!
- bombcar 4y ago
  This is part of it - many things are "fast enough" that were you used to have caches that would display nearly instantly, now you don't have those - it reads from disk each time it needs to show the folder, etc. This is very visible in any app that no longer maintains "local state" but instead is just a web browser to some online state (think: Electron, teams, etc). Disconnect the web or slow it down and it all goes to hell.
moffkalast 4y ago
That's interesting, I cloned a Win10 installation on a HDD to a sata SSD a year or two back and the speed difference was considerable. Especially something like Atom that took minutes to open before was ready to go in like 10 seconds afterwards. A lot of things remained slow though.
- digitallyfree 4y ago
  Yeah the change from a 7200 HDD to an SSD for those 10 year old machines provides a very considerable improvement. It goes from "unusable" to "moderate" performance for general web browsing and business duties. I'm talking about Windows 10 on 4G C2Q or Phenom/Phenom II machines - they aren't fast but they're very usable with a SSD and GPU in place.

marcosdumay 4y ago

It's not only a matter of 750ms instead of 200ms. I'm astonished every time I open some tool like Visual Studio, SAP Power Designer, or Libre Office that can stay for the most part of a minute on its loading screen. What do those tools even do for that long? They can read enough data from the disk to overflow my computer's main memory a few times during it.

chopin 4y ago
Phone home. I suspect much of the lag is network latency.
- marcosdumay 4y ago
  Power Designer surely is phoning home but this isn't nearly slow enough to matter here. AFAIK Visual Studio phones in during the operation, and not on startup. Libre Office almost certainly isn't phoning anywhere. I didn't include the slowest starting software that I know, Oracle SQL Developer, because it's clear that all the slowness is caused by phoning home, several times for some reason. But that's not the case for all of them. EDIT: Or, maybe it's useful to put it another way. The slowest region on the world for me to ping is around Eastern Asia and Australia. Some times, I get around 1.5s round trip time for there. A minute has around 40 of those.
- schroeding 4y ago
  I use Visual Studio on an air-gapped machine with no (active) network cards (so Windows / winsock2 knows there is nothing that can respond and any connection should error out immediately) and it still takes almost a minute. At least VS is just kinda slow, maybe it's the XML parser :D
- bentcorner 4y ago
  I work at a BigCorp that ships desktop software (but none of the above products) and network latency is (usually) pretty easy to extract out of the boot critical path. Blocking UI with network calls is a big no-no, and I expect any sizeable organization to have similar guidelines. Work like in the OP's article is probably the most difficult - it's work that is necessary, cannot be deferred, but is still slow. So it requires an expert to dig into it.

vbezhenar 4y ago

Back in time all you needed for perfect performance is to use C and proper algorithms. It was easy. Nowadays you need vector operations, you need to utilise GPU, you need to utilise various accelerators. For me it is black magic.

flaviut 4y ago
I think you're trying too hard. Something that you do a lot? Fine, write it in C/C++/Rust. It's something that costs thousands/millions of dollars of compute? Ok, maybe it's worth it for you to spend a month on, put your robe on, and start chanting in latin.
AnIdiotOnTheNet 4y ago
But perfect performance isn't even the benchmark, it's "not ridiculously slow". This is what is meant by "Computers are fast, but you don't know it", you don't even know how ludicrously fast computers are because so much stuff is so insanely slow. They're so fast that, in the vast majority of cases, you don't even need optimization, you just need non-pessimization: https://youtu.be/pgoetgxecw8 https://youtu.be/pgoetgxecw8
LeifCarrotson 4y ago
To be really fast, yes. Those are optimizations that allow you to go beyond the speed of just C and proper algorithms. But C and proper algorithms are still fast - Moore's law is going wider, yes, and single-threaded advancements aren't as impressive as they used to be, but solid C code and proper algorithms will still be faster than it was before! What's not fast is when, instead of using a hashmap when you should have used a B-tree, you instead store half the data in a relational database from one microservice and the other half on the blockchain and query it using a zero-code platform provided by a third vendor.
Schroedingersat 4y ago
These things only net you one or two orders of magnitude (and give you very little or even negative power efficiency gain), or maybe 3 for the gpu. This pales in comparison to the 4-6 orders of magnitude induced by thoughtless patterns, excessive abstraction, bloat, and user-hostile network round trips (this one is more like 10 orders of magnitude). Write good clean code in a way that your compiler can easily reason about to insert suitable vector operations (a little easier in c++, rust, zig etc. than c) and it's perfect performance in my book even if it isn't saturating all the cores

fmakunbound 4y ago

I’m afraid we just “deploy more pods” these days

mrtranscendence 4y ago

> And in the end, the code seems to run "fast enough" and nobody involved really notices that what is running in 750ms really ought to run in something more like 200us. Nobody has created a language that is both thousands of times faster than Python and nearly as straightforward to learn and to use. The closest thing I know of might be Julia, but that has its own performance problems and is tied closely to its AI/ML niche. Even within that niche I'm certainly not going to get most data scientists to write their code in C or C++ (or heaven forbid Rust) to solve a performance impediment that they've generally been able to work around. It's great that you've been able to switch to higher-performance languages, but not everyone can do that easily enough to make it worth doing.

divan 4y ago
Go hit a really sweet spot here.
stefanos82 4y ago
> Nobody has created a language that is both thousands of times faster than Python and nearly as straightforward to learn and to use. Not Python-based, but Lua-based is Nelua [1] If you like Lua's syntax, LISP's metaprogramming abilities, and C's performance, well there you have it! [1] https://github.com/edubart/nelua-lang https://github.com/edubart/nelua-lang
morelisp 4y ago
The "iterate from notebook to production" process which is common everywhere but the largest data engineering groups rules out anything with manual memory management from becoming popular with data science work. Some data scientists I know like (or even love) Scala, but that tends to blow up once it's handed over to the data engineers as Scala supports too many paradigms and just a couple DSs will probably manage to find all of them in one program. We use Go extensively for other things, and most data scientists I've worked with sketching ideas in Go liked it a lot, but the library support just isn't there, and it's not really a priority for any of the big players who are all committed to Python wrapper + C/C++/GPU core, or stock Java stacks. (The performance also isn't quite there yet compared to the top C and C++ libraries, but it's improving.)

divan 4y ago

So much this. I wonder what would be the software engineering landscape today if hardware specs were growing like 10% per year...

munificent 4y ago

I agree 100%. I wish every software engineer would spent at least a little time writing some programs in bare C and running them to get a feel for how fast a native executable can start up and run. It is breathtaking if you're used to running scripting languages and VMs. Related anecdote: My blog used to be written using Jekyll with Pygments for syntax highlighting. As the number of posts increased, it got closer and closer. Eventually, it took about 20 seconds to refresh a simple text change in a single blog post. I eventually decided to just write my own damn blog engine completely from scratch in Dart. Wrote my own template language, build graph, and syntax highlighter. By having a smart build system that knew which pages actually needed to be regenerated based on what data actually changed, I hoped to get very fast incremental rebuilds in the common case where only text inside a single post had changed. Before I got the incremental rebuild system working, I worked on getting it to just to a full build of the entire blog: every post page, pages, for each tag, date archives, and RSS support. I diffed it against the old blog to ensure it produced the same output. Once I got that working... I realized I didn't even need to implement incremental rebuilds. It could build the entire blog and every single post from scratch in less than a second. I don't know how people tolerate slow frameworks and build systems.

jcelerier 4y ago
> I agree 100%. I wish every software engineer would spent at least a little time writing some programs in bare C and running them to get a feel for how fast a native executable can start up and run. It is breathtaking if you're used to running scripting languages and VMs. Conversely when 99.9% of the software you use in your daily life is blazing fast C / C++, having to do anything in other stacks is a complete exercise in frustration, it feels like going back a few decades in time
- p1esk 4y ago
  Conversely when 99.9% of the software you use in your daily life is user friendly Python, having to do anything in C/C++ is a complete exercise in frustration, it feels like going back a few decades in time

lynguist 4y ago

I agree except for the Python bit, which is factually wrong. Python allows you to program as if you’re a jazz pianist. You can improvise, iterate and have fun. And when you found a solution you just refactor it and use numba. Boom, it runs the same speed as a compiled language. I once wrote one little program that ran in 24 min without numba and ca. 8 seconds with numba.

willis936 4y ago
Dozens of instances of a C GUI can launch in the time it takes to launch a hello world python program.
- lmm 4y ago
  But who wants dozens of instances of a C GUI? I'd be the first to complain about latency where it maters, but launching a Python program is perceptually instant (and significantly lower-latency than many nominally "faster" languages, IME).
- geysersam 4y ago
  Yes but what kind of comparison is that: 1. How often do you need to execute 1000 GUI instances? 2. How often do you need to print "hello world"? The right tool for the right job.
  - willis936 4y ago
    This is a discussion about computers being slow. As in a person asks a computer to do something and the human waits while the computer does it. So python isn't the right tool for any job that involves human interaction.
    - geysersam 4y ago
      Nah that's too general. A lot of website/app backends use Django or Fastapi and they work fine. Many more use PHP, also not a language famed for extreme performance. It depends on the application. Personally I wouldn't use Python for a GUI (because I'd use JS/TS).

pjvsvsrtrxc 4y ago

And yet, even with all the evidence that modern, heavily-bloated software development is AWFUL (constant bugs and breakage because no one writing code understands any of the software sitting between them and the machine, much less understands the machine; Rowhammer, Spectre, Meltdown, and now Hertzbleed; sitting there waiting multiple seconds for something to launch up another copy of the web browser you already have running just so that you can have chat, hi Discord)... you still have all the people in the comments below trying to come up with reasons why "oh no it's actually good, the poor software developers would have to actually learn something instead of copying code off of Stack Overflow without understanding it".

doliveira 4y ago
During work I'm constantly reminded of how much better and snappier MSN Messenger was when compared to Teams.

yoyohello13 4y ago

I felt this pretty viscerally recently. I did Advent of Code 2021 in python last year. My day job is programming in Python so I didn't really think about the execution speed of my solutions much. As a fun exercise this year I've been doing Advent of Code 2020 in C, and my god it's crazy how much faster my solutions seem to execute. These are just little toy problems, but even still the speed difference is night and day. Although, I still find Python much easier to read and maintain, but that may just be I'm more experienced with the language.

jeroenhd 4y ago
> Although, I still find Python much easier to read and maintain, but that may just be I'm more experienced with the language. Python is definitely easier to read and maintain if you have loads of dependencies. C dependency management is a pain. If you can read and write a little C, you should consider giving C#/Java/Kotlin/Swift a try. They're probably an order of magnitude slower than C if you write them in a maintainable style, but they're still much faster than Python. If you're doing stuff like web APIs then ASP.NET/Spring will perform very admirably without manually optimizing code, for example. You might find that these languages are C-like enough to understand and Python-like enough to be productive in. Or you might not, but it's worth a shot! I personally believe that C is difficult if not impossible to properly to maintain long term, at least not as much as the faster alternatives. On the other hand my experience with Python is that it's one of the slowest mainstream languages out there, relying heavily on C libraries to get acceptable performance.

kossTKR 4y ago

I hope one day latency in general will be "back to normal". I still remember how fast console based computing, an old gameboy or a 90's macintosh would be - click a button and stuff would show up instantly. There was a tactility present with computers that's gone today. Today everything feels sluggish - just writing this comment on my $3000 Macbook Pro and i can feel the latency, sometimes there's even small pauses. A little when i write stuff, a lot when i drag windows. Hopefully the focus on 100hz+ screens in tech in general will put more focus on latency from click to screen print - now when resolution and interface graphics in general are close to biological limits.

41b696ef1113 4y ago
>Hopefully the focus on 100hz+ screens in tech Come again? I think anything beyond 60hz still qualifies as niche. Vendors are still selling 720p laptops.
- kllrnohj 4y ago
  Most flagship Android phones are >60hz and have been for a few years. Flagship iPhones and iPads are >60hz. Very nearly every gaming laptop is >60hz. Many new TVs are >60hz with inputs to match. These are not niche markets.
- theandrewbailey 4y ago
  My guess is that few people have stopped to compare them. I've never knowingly seen a 100+hz screen in person, so I stopped by a local store. Sure enough, I could tell that the motion was smoother. Bought 2. After using those, I can feel my older monitors that I'm using to write this are choppy.
  - LoveMortuus 4y ago
    But do you notice the smoothness in the day to day basis or have you, in a way, crippled yourself, because now the majority of monitors feel choppy to you? Sounds a bit like the, 'Never meet your heroes', thingy.

Tade0 4y ago

> And in the end, the code seems to run "fast enough" and nobody involved really notices that what is running in 750ms really ought to run in something more like 200us. At least with Chrome's V8, the difference is not that big. Sure, it loses to C/C++, because it can't vectorize and uses orders of magnitude more memory, but at least in the Computer Language Benchmarks Game it's "just" 2-4x slower. I remember getting a faster program doing large matrix multiplication in JavaScript than in C with -o1, because V8 figured out that I'm reading from and writing to the same cell, so optimised that out, which gave it an edge, because in both cases the memory bandwidth limited the speed of execution. As for Electron and the like: half of the reason why they're slow is that document reflows are not minimized, so the underlying view engine works really, really hard to re-render the same thing over and over again. It's not nearly as visible in web apps, because these in turn are often slowed down by the HTTP connection limit(hardcoded to six in most browsers).

jerf 4y ago
Languages top out at around 50x, and that's the extreme of pure CPython to C. For as many factors of magnitude as I am talking about, you have to be screwing up algorithms, networks, and a whole bunch of other things too. Python and similar languages like Ruby really do make it easy to accidentally pile things on top of each other, but you can screw up in pure assembler with enough work put into it. Assembler doesn't stop you from being accidentally quadratic or using networks in a silly way.

geysersam 4y ago

More important than the language, is using the right tool for the job. If you are using the scientific Python stack, correctly, you'll have a difficult time beating that with c++. For many applications. While producing way simpler and more maintainable code.

dekhn 4y ago

I don't really find python slow for what I do (typically writing UIs around computer vision systems) but also, several years back I made a microcontroller-based self-balancing robot. It was hard to debug the PID and the sensor, so I replaced it with a Pi Zero and the main robot loop ran in python- enough to read the accelerometer, compute a PID update, and send motor instructions- 100 times a second. If there was a problem (say, another heavy process, like computer vision, running on the single CPU) it would eventually not respond fast enough and the robot would fall over. Most of the time it's not that you need a faster language, it's that you need to write faster code. I was working on a problem recently where random.choices was slow but I realized that due to the structure of my problem I could convert it to numpy and get a 100X speedup.

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1vuio0pswjnm7 4y ago

"I have a hard time using (pure) Python anymore for any task that speed is even remotely a consideration anymore. Not only is it slow even at the best of times, but so many of its features beg you to slow down even more without thinking about it."

pjmlp 4y ago

I learned that lesson with Tcl and Perl, 20 years ago. If there isn't a compiler in the box (JIT or AOT), I won't be using language XYZ, unless forced by customers. The only reason I use Python is for UNIX scripting.

solatic 4y ago

> any task that speed is even remotely a consideration for anymore How do you know whether or not speed is a consideration? Yes, OP delivered impressive efficiency gains. I'm sure he could improve the efficiency even more by dropping into pure Assembly. But is it worth it? The prime consideration is not execution speed but maintainability. The further that OP got away from pure Python, the more difficult to maintain the code became. That's a downside. Now, OP describes an important technique because in the real world, you have a performance budget. Code needs to execute at speeds that return quickly enough to the user, or long execution is financially expensive (i.e. cloud computing resources), etc. But optimizing beyond what the budget requires is wasteful in terms of time needed to do the optimization as well as harmful in terms of negatively impacting future maintainability.

Tomis02 4y ago
> The prime consideration is not execution speed but maintainability. Why? And how did you measure this drop in maintainability? I'm asking because I see developers prioritize _perceived_ maintainability over _measurable_ things that matter to the user (like performance).

tbran 4y ago

Haha, what C/C++ web framework should I use instead of Django/Rails/JS-whatever? Performance is a consideration, but I'm not going to reinvent a bunch of packages because of it. This kind of blanket comment that "scripting languages are too slow" makes it sound like you shouldn't use them for anything, but they are perfectly adequate for many tasks. I'm more likely to have network and DB slowdowns than problems with scripting languages.