In computer science, algorithmic efficiency is a characteristic of an algorithm that is related to the number of computational resources required by the algorithm. An algorithm’s resource use must be evaluated, and the efficiency of an algorithm may be assessed based on the use of various resources. For a recurring or continuous process, algorithmic efficiency is comparable to engineering performance. We want to use as few resources as possible to maximize efficiency. However, because various resources, such as time and space complexity, cannot be directly compared, which of the proposed algorithm is judged to be more efficient typically relies on whatever efficiency metric is considered more significant.
Efficiency is dependent on two factors:
In some cases, the amount of space/memory consumed has to be examined. For example, in dealing with huge amounts of data or in programming embedded systems, memory consumed must be analyzed. Space/memory usage components are:
- Space of Instruction: Compiler, compiler settings, and target machine (CPU), all have an impact on space of instruction.
- Data Space: Data size/dynamically allocated memory, static program variables are all factors affecting data space.
- Stack Space at Runtime: Compiler, run-time function calls and recursion, local variables, and arguments all have an impact on stack space at runtime.
Obviously, the faster a program/function completes its objective, the better the algorithm. The actual running time is determined by a number of factors:
- Computer’s Speed: processor (not simply clock speed), I/O, and so on.
- Compiler: The compiler, as well as the compiler parameters.
- Amount of Data: The amount of data, for example, whether to search a lengthy or a short list.
- Actual Data: The actual data, such as whether the name is first or last in a sequential search.
Approaches to Time Efficiency
There are two ways to assess time complexity:
This employs basic categories to offer a basic notion of performance, which is also called an order of magnitude. If the algorithms are similar, the data amount is little, or performance is essential, then the next method can be explored further.
Estimation of running time
Estimation of running time depends on two things, i.e., Code Analysis and Code Execution, which are elaborated below:
Code Analysis: We can accomplish the following things by analyzing the code:
- Operation Counts: Choose the most frequently performed operation(s) and count how many times each one is performed.
- Step Counts: Calculate the total number of steps and potentially lines of code that program executes.
Code Execution: We may accomplish the following by executing the code:
- Benchmarking: Running the software on a variety of data sets and comparing the results.
- Description: A report on the number of hours spent in each routine of a program, which is used to identify and eliminate the program’s hot spots. This perception is frequently questioned. Although other description modes give data in units other than time (for example, call counts) and/or at levels of granularity besides per-routine, the concept is the same.
Three Cases of Time Efficiency
- Worst case
- Average case
- Best case
The worst-case scenario is evaluated since it provides an upper limit on projected performance. Furthermore, the average scenario is typically more difficult to establish (it is more data dependent), and it is frequently the same as the worst case. It may be essential to examine all three situations on some occasions.