Optimization is a program transformation technique, which tries to improve the code by making it consume less resources (i.e. CPU, Memory) and deliver high speed. In optimization, high-level general programming constructs are replaced by very efficient low-level programming codes.
Optimize Program Algorithm For any code, you should always allocate some time to think the right algorithm to use. So, the first task is to select and improve the algorithm which will be frequently used in the code. 2. Avoid Type Conversion Whenever possible, plan to use the same type of variables for processing.
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:
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.
Get a good profiler.
Don't bother even trying to optimize C# (really, any code) without a good profiler. It actually helps dramatically to have both a sampling and a tracing profiler on hand.
Without a good profiler, you're likely to create false optimizations, and, most importantly, optimize routines that aren't a performance problem in the first place.
The first three steps to profiling should always be 1) Measure, 2) measure, and then 3) measure....
Optimization guidelines:
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.)
When working with ORMs be aware of N+1 Selects.
List<Order> _orders = _repository.GetOrders(DateTime.Now);
foreach(var order in _orders)
{
Print(order.Customer.Name);
}
If the customers are not eagerly loaded this could result in several round trips to the database.
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