Tips for optimizing C#/.NET programs [closed]

Answer 1

I don't really try to optimize my code but at times I will go through and use something like reflector to put my programs back to source. It is interesting to then compare what I wrong with what the reflector will output. Sometimes I find that what I did in a more complicated form was simplified. May not optimize things but helps me to see simpler solutions to problems.

Answer 2

• Don't use magic numbers, use enumerations
• Don't hard-code values
• Use generics where possible since it's typesafe & avoids boxing & unboxing
• Use an error handler where it's absolutely needed
• Dispose, dispose, dispose. CLR wound't know how to close your database connections, so close them after use and dispose of unmanaged resources
• Use common-sense!

Answer 3

Optimization guidelines:

1. Don't do it unless you need to
2. Don't do it if it's cheaper to throw new hardware at the problem instead of a developer
3. Don't do it unless you can measure the changes in a production-equivalent environment
4. Don't do it unless you know how to use a CPU and a Memory profiler
5. Don't do it if it's going to make your code unreadable or unmaintainable

As processors continue to get faster the main bottleneck in most applications isn't CPU, it's bandwidth: bandwidth to off-chip memory, bandwidth to disk and bandwidth to net.

Start at the far end: use YSlow to see why your web site is slow for end-users, then move back and fix you database accesses to be not too wide (columns) and not too deep (rows).

In the very rare cases where it's worth doing anything to optimize CPU usage be careful that you aren't negatively impacting memory usage: I've seen 'optimizations' where developers have tried to use memory to cache results to save CPU cycles. The net effect was to reduce the available memory to cache pages and database results which made the application run far slower! (See rule about measuring.)

I've also seen cases where a 'dumb' un-optimized algorithm has beaten a 'clever' optimized algorithm. Never underestimate how good compiler-writers and chip-designers have become at turning 'inefficient' looping code into super efficient code that can run entirely in on-chip memory with pipelining. Your 'clever' tree-based algorithm with an unwrapped inner loop counting backwards that you thought was 'efficient' can be beaten simply because it failed to stay in on-chip memory during execution. (See rule about measuring.)

Answer 4

OK, I have got to throw in my favorite: If the task is long enough for human interaction, use a manual break in the debugger.

Vs. a profiler, this gives you a call stack and variable values you can use to really understand what's going on.

Do this 10-20 times and you get a good idea of what optimization might really make a difference.

Answer 5

It seems like optimization is a lost art these days.

There was once a day when manufacture of, say, microscopes was practiced as an art. The optical principles were poorly understood. There was no standarization of parts. The tubes and gears and lenses had to be made by hand, by highly skilled workers.

These days microscopes are produced as an engineering discipline. The underlying principles of physics are extremely well understood, off-the-shelf parts are widely available, and microscope-building engineers can make informed choices as to how to best optimize their instrument to the tasks it is designed to perform.

That performance analysis is a "lost art" is a very, very good thing. That art was practiced as an art. Optimization should be approached for what it is: an engineering problem solvable through careful application of solid engineering principles.

I have been asked dozens of times over the years for my list of "tips and tricks" that people can use to optimize their vbscript / their jscript / their active server pages / their VB / their C# code. I always resist this. Emphasizing "tips and tricks" is exactly the wrong way to approach performance. That way leads to code which is hard to understand, hard to reason about, hard to maintain, that is typically not noticably faster than the corresponding straightforward code.

The right way to approach performance is to approach it as an engineering problem like any other problem:

• Set meaningful, measurable, customer-focused goals.
• Build test suites to test your performance against these goals under realistic but controlled and repeatable conditions.
• If those suites show that you are not meeting your goals, use tools such as profilers to figure out why.
• Optimize the heck out of what the profiler identifies as the worst-performing subsystem. Keep profiling on every change so that you clearly understand the performance impact of each.
• Repeat until one of three things happens (1) you meet your goals and ship the software, (2) you revise your goals downwards to something you can achieve, or (3) your project is cancelled because you could not meet your goals.

This is the same as you'd solve any other engineering problem, like adding a feature -- set customer focused goals for the feature, track progress on making a solid implementation, fix problems as you find them through careful debugging analysis, keep iterating until you ship or fail. Performance is a feature.

Performance analysis on complex modern systems requires discipline and focus on solid engineering principles, not on a bag full of tricks that are narrowly applicable to trivial or unrealistic situations. I have never once solved a real-world performance problem through application of tips and tricks.

Answer 6

Tell the compiler what to do, not how to do it. As an example, foreach (var item in list) is better than for (int i = 0; i < list.Count; i++) and m = list.Max(i => i.value); is better than list.Sort(i => i.value); m = list[list.Count - 1];.

By telling the system what you want to do it can figure out the best way to do it. LINQ is good because its results aren't computed until you need them. If you only ever use the first result, it doesn't have to compute the rest.

Ultimately (and this applies to all programming) minimize loops and minimize what you do in loops. Even more important is to minimize the number of loops inside your loops. What's the difference between an O(n) algorithm and an O(n^2) algorithm? The O(n^2) algorithm has a loop inside of a loop.