4 ms·

> Write correct, readable, simple and maintainable software, and tune it when you're done, with benchmarks to identify the choke points If speed is a primary c

> Write correct, readable, simple and maintainable software, and tune it when you're done, with benchmarks to identify the choke points If speed is a primary concern, you can't tack it on at the end, it needs to be built in architecturally. Benchmarks applied after meeting goals of read/maintainability are only benchmarking the limits of that approach and focus. They can't capture the results of trying and benchmarking several different fundamental approaches made at the outset in order to best choose the initial direction. In this case "optimisation" is almost happening first. Sometimes the fastest approach may not be particularly maintainable, and that may be just fine if that component is not expected to require maintaining, eg, a pure C bare-metal in a bespoke and one-off embedded environment.

f1shy 2y ago
That was my way of thinking as I was junior programming.
- itishappy 2y ago
  And now...?
  - queuebert 2y ago
    They're a manager and send out daily emails reminding the coders of arbitrary deadlines.
    - f1shy 2y ago
      Who they?!
  - f1shy 2y ago
    After being burn waaay too many times with one of: 1) write only code (for the sake of “speed” 2) optimization of the wrong piece of code I do think it is much better to prioritize readability; then measure where the code has to be sped up, and then do changes, but try HARD to first find a better algorithm, and if that does not work, and more processor, or equipment is not viable or still does not work, go for less readable code, which is microoptimized
vbezhenar 2y ago
I don't know if this embedded development still alive. I'm writing firmware for nRF BLE chip which is supposed to run from battery and their SDK uses operating system. Absolutely monstrous chips with enormous RAM and Flash. Makes zero sense to optimize for anything, as long as device sleeps well.
- robxorb 2y ago
  Probably right in the broader sense, but there are still niches. Eg, for one: space deployments, where sufficiently hardened parts may lag decades behind SOTA and the environ can require a careful balance of energy/heat against run-time.
- mark_undoio 2y ago
  A little over 10 years ago I was doing some very resource-constrained embedded programming. We had been using custom chip with an 8051-compatible instruction set (plus some special purpose analogue circuitry) with a few hundred bytes of RAM. For a new project we used an ARM Cortex M0, plus some external circuitry for analogue parts. The difference was ridiculous - we were actually porting a prototype algorithm from a powerful TI device with hardware floating point. It turned out viable to simply compile the same algorithm with software emulation of floating point - the Cortex M0 could keep up. Having said all that though: the 8051 solution was so much physically smaller that the ARM just wouldn't have been viable in some products (this was more significant because having the analogue circuitry on-chip limited how small the feature size for the digital part of the silicon could be). Obviously that was quite a while ago! But even at the time, I was amazed how much difference the simpler chip made actually made to the size of the solution. The ARM would have been a total deal breaker for that first project, it would just have been too big. I could certainly believe people are still programming for applications like that where a modern CPU doesn't get a look in.
- ajross 2y ago
  It's still alive, but pushed down the layers. The OS kernel on top of which you sit still cares about things like interrupt entry latency, which means that stack usage analysis and inlining management has a home, etc... The bluetooth radio and network stacks you're using likely has performance paths that force people to look at disassembly to understand. But it's true that outside the top-level "don't make dumb design decisions" decision points, application code in the embedded world is reasonably insulated form this kind of nonsense. But that's because the folks you're standing on did the work for you.
eschneider 2y ago
Well, yes. Architect for performance, try not to do anything "dumb", but save micro-optimizations for after performance measurement.
- ragnese 2y ago
  The problem with all of these rules of thumb is that they're vague to the point of being vacuously true. Of course we all agree that "premature optimization is the root of all evil" as Knuth once said, but the saying itself is basically a tautology: if something is "premature", that already means it's wrong to do it. I'll be more impressed when I see specific advice about what kinds of "optimizations" are premature. Or, to address your reply specifically, what counts as "doing something dumb" vs. what is a "micro-optimization". And, the truth is, you can't really answer those questions without a specific project and programming language in mind. But, what I do end up seeing across domains and programming languages is that people sacrifice efficiency (which is objective and measurable, even if "micro") for a vague idea of what they consider to be "readable" (today--ask them again in six months). What I'm specifically thinking of is people writing in programming languages with eager collection types that have `map`, `filter`, etc methods, and they'll chain four or five of them together because it's "more readable" than a for-loop. The difference in readability is absolutely negligible to any programmer, but they choose to make four extra heap-allocated, temporary, arrays/lists and iterate over the N elements four or five times instead of once because it looks slightly more elegant (and I agree that it does). Is it a "micro-optimization" to just opt for the for-loop so that I don't have to benchmark how shitty the performance is in the future when we're iterating over more elements than we thought we'd ever need to? Or is it not doing something dumb? To me, it seems ridiculous to intentionally choose a sub-optimal solution when the optimal one is just as easy to write and 99% (or more) as easy to read/understand.
  - eschneider 2y ago
    Ok, a bit more detail then. :) Architecting for performance means picking your data structures, data flow, and algorithms with some thought towards efficiency for the application you have in mind. Details will vary a lot depending on context. But as many folks have said, this sort of thing can't be done after the fact. As for "doing something dumb", I've often seem fellow engineers do things like repeatedly insert into sorted data structures in a loop instead of just inserting into an unsorted structure and then sorting after the inserts. If you think about it for just a minute, it should be obvious why that's not smart (for most cases.) Stuff like that. What do I mean by "micro-optimizations"? Taking a clearly written function and spending a lot of time making it as efficient _as_possible_ (possibly at the expense of clarity) without first doing some performance analysis to see if it matters. Nobody's saying to pick suboptimal solutions at all.

f1shy 2y ago

That was my way of thinking as I was junior programming.

itishappy 2y ago
And now...?
- queuebert 2y ago
  They're a manager and send out daily emails reminding the coders of arbitrary deadlines.
  - f1shy 2y ago
    Who they?!
- f1shy 2y ago
  After being burn waaay too many times with one of: 1) write only code (for the sake of “speed” 2) optimization of the wrong piece of code I do think it is much better to prioritize readability; then measure where the code has to be sped up, and then do changes, but try HARD to first find a better algorithm, and if that does not work, and more processor, or equipment is not viable or still does not work, go for less readable code, which is microoptimized

vbezhenar 2y ago

I don't know if this embedded development still alive. I'm writing firmware for nRF BLE chip which is supposed to run from battery and their SDK uses operating system. Absolutely monstrous chips with enormous RAM and Flash. Makes zero sense to optimize for anything, as long as device sleeps well.

robxorb 2y ago
Probably right in the broader sense, but there are still niches. Eg, for one: space deployments, where sufficiently hardened parts may lag decades behind SOTA and the environ can require a careful balance of energy/heat against run-time.
mark_undoio 2y ago
A little over 10 years ago I was doing some very resource-constrained embedded programming. We had been using custom chip with an 8051-compatible instruction set (plus some special purpose analogue circuitry) with a few hundred bytes of RAM. For a new project we used an ARM Cortex M0, plus some external circuitry for analogue parts. The difference was ridiculous - we were actually porting a prototype algorithm from a powerful TI device with hardware floating point. It turned out viable to simply compile the same algorithm with software emulation of floating point - the Cortex M0 could keep up. Having said all that though: the 8051 solution was so much physically smaller that the ARM just wouldn't have been viable in some products (this was more significant because having the analogue circuitry on-chip limited how small the feature size for the digital part of the silicon could be). Obviously that was quite a while ago! But even at the time, I was amazed how much difference the simpler chip made actually made to the size of the solution. The ARM would have been a total deal breaker for that first project, it would just have been too big. I could certainly believe people are still programming for applications like that where a modern CPU doesn't get a look in.
ajross 2y ago
It's still alive, but pushed down the layers. The OS kernel on top of which you sit still cares about things like interrupt entry latency, which means that stack usage analysis and inlining management has a home, etc... The bluetooth radio and network stacks you're using likely has performance paths that force people to look at disassembly to understand. But it's true that outside the top-level "don't make dumb design decisions" decision points, application code in the embedded world is reasonably insulated form this kind of nonsense. But that's because the folks you're standing on did the work for you.

eschneider 2y ago

Well, yes. Architect for performance, try not to do anything "dumb", but save micro-optimizations for after performance measurement.

ragnese 2y ago
The problem with all of these rules of thumb is that they're vague to the point of being vacuously true. Of course we all agree that "premature optimization is the root of all evil" as Knuth once said, but the saying itself is basically a tautology: if something is "premature", that already means it's wrong to do it. I'll be more impressed when I see specific advice about what kinds of "optimizations" are premature. Or, to address your reply specifically, what counts as "doing something dumb" vs. what is a "micro-optimization". And, the truth is, you can't really answer those questions without a specific project and programming language in mind. But, what I do end up seeing across domains and programming languages is that people sacrifice efficiency (which is objective and measurable, even if "micro") for a vague idea of what they consider to be "readable" (today--ask them again in six months). What I'm specifically thinking of is people writing in programming languages with eager collection types that have `map`, `filter`, etc methods, and they'll chain four or five of them together because it's "more readable" than a for-loop. The difference in readability is absolutely negligible to any programmer, but they choose to make four extra heap-allocated, temporary, arrays/lists and iterate over the N elements four or five times instead of once because it looks slightly more elegant (and I agree that it does). Is it a "micro-optimization" to just opt for the for-loop so that I don't have to benchmark how shitty the performance is in the future when we're iterating over more elements than we thought we'd ever need to? Or is it not doing something dumb? To me, it seems ridiculous to intentionally choose a sub-optimal solution when the optimal one is just as easy to write and 99% (or more) as easy to read/understand.
- eschneider 2y ago
  Ok, a bit more detail then. :) Architecting for performance means picking your data structures, data flow, and algorithms with some thought towards efficiency for the application you have in mind. Details will vary a lot depending on context. But as many folks have said, this sort of thing can't be done after the fact. As for "doing something dumb", I've often seem fellow engineers do things like repeatedly insert into sorted data structures in a loop instead of just inserting into an unsorted structure and then sorting after the inserts. If you think about it for just a minute, it should be obvious why that's not smart (for most cases.) Stuff like that. What do I mean by "micro-optimizations"? Taking a clearly written function and spending a lot of time making it as efficient _as_possible_ (possibly at the expense of clarity) without first doing some performance analysis to see if it matters. Nobody's saying to pick suboptimal solutions at all.