#ProgrammingEfficiency #MemoryOptimization #CodeOptimization
Do you find yourself struggling to write efficient and optimal code that minimizes the amount of memory your computer takes up? You’re not alone! Many programmers face the challenge of balancing functionality with efficiency when writing code. But fear not, as there are practical solutions that can help you streamline your coding process and produce more efficient programs.
##Understanding the Problem
As a programmer, you may have encountered situations where your code consumes more memory than necessary, leading to slower performance and inefficiency. This can be a frustrating problem to deal with, especially when you’re trying to optimize your code for peak performance. So, how do you find which code is efficient and optimal?
###Identifying Inefficient Code
One of the first steps in addressing this issue is to identify code that is inefficient and memory-intensive. Look for repetitive or redundant lines of code, nested loops, and excessive use of variables. These are common culprits that can lead to bloated code and increased memory consumption.
###Utilizing Functions
Functions are a great way to streamline your code and make it more efficient. By breaking down your code into reusable functions, you can reduce the overall size of your program and improve readability. Functions also help optimize memory usage by allowing you to call specific tasks only when needed, rather than duplicating code throughout your program.
###Optimizing Data Structures
Choosing the right data structures can have a significant impact on the efficiency of your code. Use data structures like arrays, hash tables, and linked lists, which are optimized for memory storage and retrieval. Avoid using inefficient data structures that can lead to memory leaks and decreased performance.
##Practical Solutions
Now that you understand the problem and some potential causes of inefficiency in your code, let’s explore practical solutions to help you write more efficient and optimal programs.
1. Use comments to explain your code: Documenting your code with comments can help you understand the logic behind each line of code and identify areas for optimization.
2. Employ debugging tools: Tools like debuggers and profilers can help you identify memory leaks, bottlenecks, and inefficient code segments. Use these tools to analyze your code and make necessary optimizations.
3. Practice code refactoring: Refactoring involves restructuring your code to improve its efficiency and readability. Look for opportunities to simplify complex code, remove redundancies, and optimize algorithms for better performance.
4. Stay updated on programming best practices: Keep yourself informed about the latest trends and best practices in programming. Attend workshops, webinars, and conferences to learn new techniques for writing efficient code.
By implementing these practical solutions and staying proactive in optimizing your code, you can write more efficient and optimal programs that reduce the amount of memory your computer takes up. Remember, efficiency is key in programming, so take the time to review and refine your code for optimal performance. 🚀🤖 #OptimalCoding #EfficientProgramming
Most often they don’t, because developer time is much more valuable than execution time. If they do there are a myriad of techniques that you can apply. You can profile your code for example with valgrind, measure execution time or memory consumption, change something, measure again.
Then you can formally evaluate your program in your head, i.e. look at the source code and see where resources are allocated, where which algorithms are executed etc. Depending on the scale of the program that may or may not work well.
And one thing to note is that you never really get to “optimal”, at least in the sense that you couldn’t get any better. It’s all heuristics because finding mathematically optimal code is pretty much impossible due to the complexity.
I’m not sure I’m reading this right. Are you assuming that fewer lines of code means using less memory? That is definitely not the case.
It depends on the case, usually, you should know what is more efficient than others, ex, count records with database functions are more efficient than backend language functions/methods that returns the length of records, like length for javascript or count() for PHP.
Additionally, you can always use the debugger to find where the code is slow and try to factor it.
So the number one tool that nearly all programmers do is measure.
If a program takes too long, or uses too much memory, measure it and see how much. Figure out what part of the program is the slow part, or the part that’s using too much memory.
Sometimes the solution is as you say – using fewer lines of code.
Usually it’s a little more complicated than that. In fact, it’s quite common for a more efficient solution to require more lines of code.
As a simple example, let’s say the computer is trying to find a name in a list of a million names. It takes several seconds to loop over all of the names.
Instead, if you sort the names first (in alphabetical order), now you can find a name in the list more quickly using “binary search” – start in the middle, see if the name is smaller or larger. Then consider just half the list and do it again.
Binary search is more code than a simple loop – but for a long list, it runs more quickly.
When you take an Algorithms & Data Structures class, they teach you dozens of common techniques like this one (sorting, and binary search) and also how to analyze code to determine how many steps it takes mathematically.
Now, there are times when you want to optimize further – for example when it’s a game engine or machine learning training and it’s worth spending the extra effort to make the code even faster. In that case you need to have a much deeper understanding of how a computer works. You need to learn about machine code and how computers execute instructions, and how processors do things like reorder, pipeline, predict, and more – plus caching, multiprocessing and more. The fundamentals are all taught as part of a college degree.
A lot of working programmers don’t know all that stuff, especially if they’re self-taught. And honestly for a lot of code it doesn’t matter – it just needs to be “good enough” and there are existing functions that already handle so many common problems efficiently.
But, if you’re working on more cutting-edge or unique software, having that deep knowledge can enable you to get very significant speedups.
“Concise” code is not at all the same as “optimal” code. Being concise is mainly about ease of understanding for the programmer.
Making a program efficient is mostly about carefully considering the goal of your program (or any individual component), only doing what’s necessary to achieve that goal without unnecessary overhead or waste, and choosing appropriate algorithms and data structures to do that.
The number of functions, or the length of each line, also usually doesn’t have much of a *direct* effect to do with the efficiency at runtime.
For instance, suppose you have a function which your program calls 1,000,000 times, and each function call takes 1 microsecond to execute, for a total of 1 second. If you “inline” the function by getting rid of it and moving its code into whatever calls it, that might save 10 nanoseconds of function call overhead, which makes your program run 1% faster. But if instead, you can think of a way to call the function 1,000 times instead of 1,000,000 times, that makes your program 1000x faster.
Sometimes, reducing the amount of work the program does requires you to do more complicated logic to decide what gets done. (For instance, a hashtable is more complex to implement than a linear array, but depending on what you’re trying to do, it can allow you to retrieve individual entries much more quickly without searching through the entire array.) In that case, breaking up your code into functions is an *organizational strategy* that allows the programmer to understand the complexity.
Sometimes developer get bored, then they start doing benchmarks.
You dont, you optimize as much as your problem allows. You can’t store the number 2^64 -1in anything less than 64 bits signed or 63 bits unsigned, you can’t compress data more than 50% without loss (generally)
Even then the code you come up with might not even be the most efficient, there comes a point with any project where you have to say it is good enough, this applies to code, and physical projects.
Time spent on program development kind of is a zero sum game in some respects. You can either spend your time optimizing something to hell and back, or adding features, or bug fixing, or polishing. Any time spent doing one will take away from time doing the others.
What do you mean by the “memory that computer takes”? The computer has memory but doesn’t “take” memory..
And it all depends on what you mean by optimal? Sometimes you need code to run as quickly as possible, sometimes you need it to use as little memory as possible, sometimes you want it to use as little energy as possible. In modern systems you tend to have plenty of CPU power, lots of memory available and power usage isn’t too much of a concern and then you are just interested in getting code that works correctly – but at times one or more of the above factors are still needed and it is then that a good understanding of how the code really runs and a good knowledge of algorithms that are available comes in really useful.
I’m old enough to remember writing code for microcontrollers in the 80s, when they typically ran at a max clock speed of 8 or 10MHz, had only 4k of RAM and ROM available, you had a device that was battery powered, and you needed to process data every 50-100 microseconds! Then coding became challenging and almost always meant writing in assembler!
>I understand a good programmer should write code which is concise and optimal.
Not really, we optimise for readability really. Better to have code that other developers can actually read. Hardly any code out there is optimal, really practically *none* of it.
>But how do they find which is optimal.
We don’t, we really, really don’t.
>Is it using functions?
We use functions, but that’s nothing to do with optimisation, we use functions because that’s how you structure code.
>or taking long lines of code when they can finish it in shorter number of lines?
Absolutely not, we optimise for readability, we don’t try to make it shorter just because we can.
I really cannot stress this enough: Practically *zero* code out there is optimally written. Bear in mind:
1. Most developers are not capable of writing good code. Seriously, they’re not.
2. Lots of code is written in languages that run slowly. If you write in Python, you’re basically writing code that is going to run about 10x slower than Java, but computers are fast enough that 90% of the time it doesn’t matter.
3. In an ideal world we write code so that the next developer after we’ve moved on can reasonably read the code. That’s a good code base. We don’t try to wring out performance improvements unless we really have to.
A famous programmers said “Premature Optimization Is the Root of All Evil”, and it’s true.
[Time Complexity. ](https://www.youtube.com/watch?v=D6xkbGLQesk&pp=ygUZdGltZSBjb21wbGV4aXR5IGZyZWVjb2RlIA%3D%3D)
There is readable code, quickly written code, and resource-efficient code. There is some small overlap. If you can keep your code within that overlap, you make a lot of money.
You only optimize as much as is needed. But, programmers are basically doing operations on data. You learn what types of operations take more CPU, or RAM, and you weigh the pros and cons when picking your approach. Or, you do it the way I do it, which is write what comes naturally and seems easiest, and then you optimize performance if it is being slow or eating memory.
You want to study Algorithms, Data Structures, and imo Discrete Mathematics, to learn about this kind of stuff.
There are measurement tools, if you want a precise answer. But most of us don’t code with measurement tools embedded everywhere in the code. We instead think about the theoretical runtime and space usage. You probably have heard of O(n) and stuff like that. In most cases, just reducing the number of loops that you think the computer has to perform is enough.
Performance in lower level systems is a bit more complicated. A high quality code usually shows good understanding of object creation and linking time. Honestly it comes with a lot of experience and learning.
It’s never quite as straight forward as that. There are numerous places for inefficiencies to hide, and rarely is code ever in it’s most efficient and optimal form, because quite often you don’t want it to be. Almost without exception, faster code is more complex code. Need the same value in two places? Simpler to call the function twice, but it’s faster to call it once and share the result. It’s faster to keep things in a single function, but its much more readable to have things organized in separate functions. I would say unless the code you’re writing absolutely needs to be fast, or you know something is too slow, you’re usually better off focusing on keeping the code clean, and readable, because it’s very rare that I get done optimizing code, and end up with more maintainable code. Usually the opposite. But as others have said, optimizing is all about measuring. Seeing how many function calls are made, and if they can be reduced, or parallelized, etc. But always remember to measure is broadly as you can. If you take a function from 50ms to 50ns, that’s a huge improvement if it’s the only code running, or it’s run frequently, but if it’s a small part of a 2 minute job that gets run once a month to generate some reports, it’s literally not worth the time it took to even think about optimizing it.
You should always be aware of what your code does. An integer in Java takes up 4 bytes of memory. You might need to create a list of one million integers. That’s roughly 4 megabytes. That sounds totally fine. But if you’re using something more complex with lots of variables, memory can add up quickly. So you need to know where you use these big lists and see if it’s really necessary or if there’s another way to do it more efficiently.
Also, it’s important to free up memory when you don’t need it. If you need those 4 megabytes for a calculation, you can get rid of them when you finish. Imagine if you forget them, and then do the same calculation again, you create 4 more megabytes and they stack up. Do that a few hundred times and now you have a severe memory leak.
1. We know the concept of [asymptotic complexity](https://en.wikipedia.org/wiki/Asymptotic_computational_complexity) , and optimize our algorithms to it
2. We optimize from there by measuring, testing, and knowing things about how languages handle certain things “under the hood”
Most of the time things like number of lines of code is a trivial issue.
One approach to complexity is using something like big O notation. Essentially thinking “in the worst case scenario, how long will this thing take”
For example, looping through a list of n items, is complexity O(n), meaning as n increases, the time it takes increases proportionally.
If you iterated through the list and for each item you iterated through the list again, that would be complexity O(n^2) meaning that as n increases, the time it takes increases exponentially.
Essentially, programmers analyze different algorithms for things like searching and sorting and go with the approach that is fastest based on complexity.
There’s also the issue of memory which is different from complexity but is also taken into account to some degree.
For clarification I am not an expert on complexity analysis and this is a very surface level summary which may not be 100% accurate but you get the idea.
Realistically, you start by just making it work and then, if it seems to have performance issues, you look for places to optimize. I doubt anybody even tries to produce “optimal” code in all cases, most of the time it doesn’t really matter as long as it’s not horribly suboptimal. I mean – if we were chasing true optimality, we’d be writing everything in assembler.
Knowing how the algorithm scales and how the internals of the machine work
Generally speaking you don’t need to worry about writing optimised code unless it’s a big operation: a loop that runs many times, finding data in a big collection (100,000+ maybe, more like like millions) or stuff that loads massive objects into memory. Or if you need to be able to scale to potentially serve large numbers of users at once. Or if you’re writing code for embedded systems which normally have minimal resources.
But just writing lines of code that run one after the other in a desktop or mobile system, efficiency gains are invisible for the power of modern computers.
Also wanted to note that less code / shorter lines does not equal more efficient code. It’s more to do with the underlying methods that are being evoked, remember that a function you write is just calling to a much bigger pile of code underneath.
Also code can be slow because it uses more memory or cpu than is available, or another common issue is waiting for responses from external / remote systems. Too much storage access. Could be blocking threads ie long running code on the gui thread. There’s more stuff that can cause a program to run slow although the system may not be taxed on cpu or memory.