I'm not sure I understand the tradeoff. Given two tasks requiring an equal amount of work, are you saying that it's better to spend it on a 5% improvement than a 150% improvement? Sure, if all you have available are lots of 5% improvements, they'll also eventually add up. But won't you prefer to work on the biggest improvement/cost tasks first?
Perhaps it should be seen versus the "throw it all away and start over" tendency? Like in steady refactors vs. big rewrites.
Say you can either spend X amount of effort on a 50% improvement, or you can spend X/10 on each of 10 5% improvements. Sounds the same, right?
It's not. If the smaller increases compound, you get a (1.05^10=) 63% increase out of the latter situation. Plus you don't have to wait for everything to be done to get visible improvements.
>Say you can either spend X amount of effort on a 50% improvement, or you can spend X/10 on each of 10 5% improvements. Sounds the same, right?
This is the same thing. The point is that you say "10 5% improvements of state S0" in that sentence and then later on you use 10 5% improvements of state S0, S1... S9. This doesn't make any sense, could you explain to me how this would work?
> you say "10 5% improvements of state S0" in that sentence and then later on you use 10 5% improvements of state S0, S1... S9.
No, I don't.
Here's a very simplistic example. Say I've got a complicated computation with 10 nested loops, and there's a potential for a 5% speedup tweak at every nesting level, each of which takes about the same amount of effort to find; or, for the same overall effort I can speed up the whole thing by 50% by replacing the whole algorithm with a faster one.
The former case gives you a better outcome than the latter, and it doesn't matter what order you do the tweaks in, or whether you do them all at once. The point is that they can compound without caring about the previous or latter state of anything other than their own level.
Yes but the point is that the (at least I assumed so) 5% increase in speed was based on the initial performance of the system and that it didn't scale. Isn't that basically what you're talking about?
If you take two speed-ups which individually would be 5% over the initial performance and apply them both, you get 10.25%, not 10%. The point is that it can scale.
A number of discrete improvements is also more resilient. If a single perf improvement needs to be removed, only a 5% gain is removed. If the big 50% gain is monolithic it is much more susceptible to disruption.
you're statement needs a lot of qualifications. It is in only very specialized cases where this is true.
Let's say my program takes time 1.0 and is made up of part A taking 0.5 and part B taking 0.5
Defining 5% speedup is weird in the first place, but let's say 5% speedup is taking 0.95 for the program and 10% speedup is taking 0.9 for the program.
5% speedup by improving part A => 0.45 + 0.5 => 0.95 runtime
5% speedup by improving part B => 0.5 + 0.45 => 0.95 runtime
making both improvements:
0.45 + 0.45 => 0.9
So, two speed-ups which individually would be 5% over the initial performance gives us a 10% speed up.
percentage speed ups do not necessarily compound. And in practice, usually they do not.
Imagine you like something and have 40 hours implement it. Now spread those hours over one week, or over several months. Second cas will be much better because people will contribute ideas, you spend some time thinking about this task outside your job and so on. There is some overhead with constantly restarting your work, but that is negleable with good methodology.
Perhaps it should be seen versus the "throw it all away and start over" tendency? Like in steady refactors vs. big rewrites.