Depends on the requirements. Writing the code in a natural and readable way should be number one.
Then you benchmark and find out what actually takes time; and then optimize from there.
At least thats my approach when working with mostly functional languages. No need obsess over the performance of something thats ran only a dozen times per second.
I do hate over engineered abstractions though. But not for performance reasons.
You need to me careful about benchmarking to find performance problems after the fact. You can get stuck in a local maxima where there is no particular cost center buts it’s all just slow.
If performance specifically is a goal there should probably at least be a theory of how it will be achieved and then that can be refined with benchmarks and profiling.
Writing the code in a natural and readable way should be number one.
I mean, even there it depends what you’re doing. A small matrix multiplication library should be fast even if it makes the code uglier. For most coders you’re right, though.
In my experience we all go through a stage at the Designed-Developer level of, having discovered things like Design Patterns, overengineering the design of the software to the point of making it near unmaintainable (for others or for ourselves 6 months down the line).
The next stage is to discover the joys of KISS and, like you described, refraining from premature optimization.
Depends on the requirements. Writing the code in a natural and readable way should be number one.
Then you benchmark and find out what actually takes time; and then optimize from there.
At least thats my approach when working with mostly functional languages. No need obsess over the performance of something thats ran only a dozen times per second.
I do hate over engineered abstractions though. But not for performance reasons.
You need to me careful about benchmarking to find performance problems after the fact. You can get stuck in a local maxima where there is no particular cost center buts it’s all just slow.
If performance specifically is a goal there should probably at least be a theory of how it will be achieved and then that can be refined with benchmarks and profiling.
I mean, even there it depends what you’re doing. A small matrix multiplication library should be fast even if it makes the code uglier. For most coders you’re right, though.
In my experience we all go through a stage at the Designed-Developer level of, having discovered things like Design Patterns, overengineering the design of the software to the point of making it near unmaintainable (for others or for ourselves 6 months down the line).
The next stage is to discover the joys of KISS and, like you described, refraining from premature optimization.