451–460 of 819 posts
With Nixos I switch between Gnome 40 (I do like the Gnome workflow) and i3 w/ some Xfce4 packages, but lately on my older machine the performance of Gnome (especially while running Firefox) is so sluggish in comparison that I may have switched back permanently now.
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Is the speed of light really the speed of causality? Would the effect of gravity (the lack of) affect Earth earlier than us perceiving the lack of light.
Nope. All physical effects are bounded by the speed of light. (As far as anyone knows, anyway.) The only weird one is quantum entanglement, but even then information transfer doesn't travel faster than light and that's about all I know on that subject.
For anyone wondering why that is, I found this explanation super interesting:
https://www.forbes.com/sites/startswithabang/2020/01/02/no-w...
I was working in C, and looking back I came up with a quite performant solution mostly by accident: all the memory allocated up front in a very cache-friendly way.
The first time I ran the program, it finished in a couple seconds. I was sure something must have failed, so I looked at the output to try to find the error, but to my surprise it was totally correct. I added some debug statements to check that all the data was indeed being read, and it was working totally as expected.
I think before then I had a mental model of a little person inside the CPU looking over each line of code and dutifully executing it, and that was a real eye-opener about how computers actually work.
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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
As a person who uses both languages for various needs, I disagree. Things which takes minutes in optimized C++ will probably take days in Python, even if I use the "accelerated" libraries for matrix operations and other math I implement in C++. Lastly, people think C++ is not user friendly. No, it certainly is. It needs being careful, yes, but a lot of things can be done in less lines then people expect.
I’m gonna need an example because I do not believe this whatsoever.
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 m…
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.
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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…
Throw in more RAM and Windows 10 will likely feel snappier than Windows 7 did. It's probable the old Windows 7 install was 32-bit while your fresh install of 10 would have defaulted to 64-bit. That combined with 10's naturally higher memory requirements means the system has less overhead to work with.
And 4GB is enough for a blank windows 10 install doing some OS things and browsing. I don't think more memory helps that scenario.
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As a person who uses both languages for various needs, I disagree. Things which takes minutes in optimized C++ will probably take days in Python, even if I use the "accelerated" libraries for matrix operations and other math I implement in C++. Lastly, people think C++ is not user friendly. No, it certainly is. It needs being careful, yes, but a lot of things can be done in less lines then people expect.
> Things which takes minutes in optimized C++ will probably take days in Python, even if I use the "accelerated" libraries for matrix operations and other math I’m gonna need an example because I do not believe this whatsoever.
Let's say I'm making a lot of numerical calculations which are fed from a lockless queue with atomic operations to any number of cores you want, where your performance is limited by the CPU cores' FPU performance and the memory bandwidth (in terms of both transfer speed and queries that bus can handle per second).
As I noted below, that code can complete 1.7 million complete evaluations per core, per second on older (2014 level) hardware, until your memory controller congests with all the requests. I need to run benchmarks on a newer set of hardware to get new numbers, however I seriously lack the time today to do so and provide you new numbers.
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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…
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.
But if any parts of 10 are sufficiently badly coded compared to 7, that will overcome the drive. And some parts definitely are, especially in the start menu code.
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> If you hold up a sign with, say, a multiplication, a CPU will produce the result before light reaches a person a few metres away. The latency on multiplication (register input to register output) is 5-clock ticks, and many computers are 4GHz or 5GHz these days. 5-clock cycles at 5GHz is 1ns, which is 30-centimeters of light travel. If we include L1 cache read and L1 cache write, IIRC its 4 clock cycles for read + 4…
This reminds me of that “todo” I wrote for myself a long time ago. These days processors come with bigger L1,L2, and L3 caches. Would it be possible for a program that works on a tiny bit of data(few KB) to load it all up in the cache and provide ultimate response times?! Are there any directives to the Operating System to say - “here keep this data in the fastest accessible L[1,2,3] please”?
Some architectures (e.g. GPUs) provide local "scratchpad" memories instead of (or in addition to) caches. These are separate uninitialized adressable memory region with similar access times to a L2/L1 cache.
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I remember a video of a guy running an old version of Visual C++ on an equally old version of Windows, in a VM on modern hardware, to try Windows development "the old way". It took about one frame to launch. One. Frame. By the way, Apple isn't much better. Xcode takes around 15 seconds to launch on an M1 Max. edit: probably this video https://youtu.be/j_4iTovYJtc?t=282
One. Frame. Of. What? I've never heard of someone describing how long something took like this without at least defining the frame rate.