What does efficiency mean in the Programming World?
There are many definitions for efficiency so for you want to know the highest programming languages you need to know the definition of “efficiency”, as well as the purpose of that program, is. You need to define “efficient”. The one that creates the densest code? Or maybe the one that creates the fastest code?
The one that creates the programs that perform the fastest (which can be *different* from “fastest code”)? And for what sort of program? Numerical number-crunching? Operating systems code? Code that’s heavy with normal expressions, or database queries.
Efficient in terms of programmer time, effort to keep updated, an effort to debug, design time machine time, support time, processing cycles, electrical energy expended, user time/user effort, etc.? All those may produce several answers, and still more variations depending on the application you are looking to build, the larger environment, the platform it’s running on, and the organization it’s for.
Breaking it further
If you are only concerned about efficiency in programming that may not depend on what language you use so much as the architecture of the system as a whole. So Efficient is opinion-based. You and I might have different opinions on what we consider efficient. You might prefer speed while I might prefer memory usage as a method to measure efficiency. For instance, I want to write code for Arduino boards that are restricted in just kilobytes of RAM.
You might prefer to write websites with a quick response time that includes huge caches of stored data. We would not agree on what is efficient as I can’t afford to waste a little bit while you don’t worry about memory but want the highest speed.
New research shows that faster languages don’t always use less electricity while some old standbys are actually pretty efficient. It’s worth noticing that interpreted languages have the highest variation sometimes causing the CPU to consume 92.2% of power or as little as 81.57%.
The researches used three different metrics from the 10 tests: energy used, time spent on the tests, and megabytes used to complete those tests. The C language was raked the highest for energy and time and fell to third on MB usage. Rust was really close on energy and time but plummeted to seventh place for Mb used. C++ was ranked third in the first two categories dipping to fifth when researches measured how many Mb is used.
The Importance of Speed for Programming
If you are looking only for speed, a program written in some kind of assembly language is probably going to be much more durable than its counterparts in C or C++ If you are looking for readability, we would suggest Java or Python. If by efficiency you mean the shortest period of code, APL would be extremely efficient if you are willing to lose that much readability.
Then there are all kinds of specialized languages. For instance, you could theoretically build a database system using, say, LISP, something like SQL is already designed for that purpose so it would be much more efficient for that project.
The importance of Processing usage?
If you’re assuming efficiency insofar as processing usage and memory, you’re going to want to look at lower-level languages like C(++), Golang, Ada, and Fortran. But in that, you lose “time efficiency”. But, for things that are critical, high usage, fail-safe, low level, efficiency is sometimes baked into the language, and you spend less time trying to eke performance out of more powerful languages because much of what you can do is baked in.
The importance of the Middle-ground?
The other efficiency is the middle-ground, where you’re looking for stability with performance, but not certainly as critical as the other. Your .NET’s, Erlang’s and Haskell’s, your Java’s, OCaml, and your JVM languages. You’ll find a lot of type-safe languages because of their security in coding that makes it efficient when you are looking to get the correct answer.
You don’t spend as much time in resource allocation and design as you would possibly with the lower-level languages and you are trading off speed to some magnitude, but you are increasing flexibility.
Importance of Performance?
For performance, we suggest you go with C and also leave C++ out because C++ often ends up covering performance costs (including inefficient partitioning of objects, parameter passing, memory allocations, vtable indirections, etc). Nowadays, C is usually better than assembler for the latest processors, unless you are an expert in the processor’s machine instructions.
There are many factors to consider. Best performance might be a function of choosing the algorithms, parallel processor arrays, processor(s), or even programmable hardware. They can have their personal programming languages or surroundings to choose from.
The importance of the time-efficiency paradigm?
The last efficiency is the time-efficiency paradigm. Wherever you hear the jokes of “How much quicker is python than Visual Basic? Possibly 3 months”, because you are capable of putting things together quickly. You’re becoming efficient in the sense that you are using some of your most important resources efficiently, and that is programmer time.
You can draw out code that does web quickly without as much startup price, but you lose performance (in some things) and flexibility with how you need to manage low-level problems. Some of these languages are based on C implementations or other low-level necessary implementations and allows you to craft the things that you want with low-level implementation, and script on top of it, to get the best of both systems.
If the programmer spends another 3 days working in a different language to make it more “efficient”, but it’s a one-off program that finishes in 6 hours, you just attached extra 2 days to the total time of getting results.
Surely assembler is the fastest and has the most efficiency as programming language because it is direct with the bare metal. There are no translation inefficiencies or generalization inefficiencies. The problems with assembler are that it is, by far, the hardest and most time-consuming method of programming and it is totally non-portable across CPU’s and (largely) OS.
C is probably #2 because it is close to the metal. It has a number of generalization inefficiencies but the benefits of development speed and portability almost always outweigh the cost.
Other languages like Java & C# use a machine simulation on top of a real CPU. In general, doing this is incredibly inefficient and costly. However, in the case of Java & C# the implementers spent a great deal of time and effort to make the compilers and virtual machines so smart that the code runs at nearly the same speed as C.
Lisp is historically an interpreter and incredibly slow. However, like Java & C#, the modern Lisp implementations are so incredibly advanced that they too run at near the speed of C.Other languages like Python are incredibly slow largely because the resources it would take to make them fast is beyond the capacity of the resources behind them.
With all languages, the CPU runs at the same speed. Machines only understand machine code. Compilers for languages like assembler, C, Lisp, etc. convert from the language the human writes (assembler, C, Lisp, etc.) into machine language that the machine can run.
The assembler has a (mainly) one-to-one relationship between a line of code and a machine instruction so there is zero waste. There is a small difference between C lines and the number of machine instructions that get generated.
Languages like Java, C#, Lisp, etc. have a huge gap between the language and the machine language but their compilers are incredibly smart about the conversion.
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