When Efficiency is Important?
The Major Question in front of quite satisfactory Programmer.......
Not every problem requires the most efficient solution available. For our purposes, the term efficient is associated with the time or space needed to perform the task. When either time or space is abundant and cheap, it may not be worth it to pay a programmer to spend a day or so working to make a program faster.
However, here are some cases where efficiency matters:
• When resources are limited, a change in algorithms could create great savings and allow limited machines (like cell phones, embedded systems, and sensor networks) to be stretched to the frontier of possibility.
• When the data is large a more efficient solution can mean the difference between a task finishing in two days versus two weeks. Examples include physics, genetics, web searches, massive online stores, and network traffic analysis.
• Real time applications: the term "real time applications" actually refers to computations that give time guarantees, versus meaning "fast." However, the quality can be increased further by choosing the appropriate algorithm.
• Computationally expensive jobs, like fluid dynamics, partial differential equations, VLSI design, and cryptanalysis can sometimes only be considered when the solution is found efficiently enough.
• When a subroutine is common and frequently used, time spent on a more efficient implementation can result in benefits for every application that uses the subroutine. Examples include sorting, searching, pseudorandom number generation, kernel operations, database queries, and graphics.
In short, it's important to save time when you do not have any time to spare.
When is efficiency unimportant? Examples of these cases include prototypes that are used only a few times, cases where the input is small, when simplicity and ease of maintenance is more important, when the area concerned is not the bottle neck, or when there's another process or area in the code that would benefit far more from efficient design and attention to the algorithm(s).
The Major Question in front of quite satisfactory Programmer.......
Not every problem requires the most efficient solution available. For our purposes, the term efficient is associated with the time or space needed to perform the task. When either time or space is abundant and cheap, it may not be worth it to pay a programmer to spend a day or so working to make a program faster.
However, here are some cases where efficiency matters:
• When resources are limited, a change in algorithms could create great savings and allow limited machines (like cell phones, embedded systems, and sensor networks) to be stretched to the frontier of possibility.
• When the data is large a more efficient solution can mean the difference between a task finishing in two days versus two weeks. Examples include physics, genetics, web searches, massive online stores, and network traffic analysis.
• Real time applications: the term "real time applications" actually refers to computations that give time guarantees, versus meaning "fast." However, the quality can be increased further by choosing the appropriate algorithm.
• Computationally expensive jobs, like fluid dynamics, partial differential equations, VLSI design, and cryptanalysis can sometimes only be considered when the solution is found efficiently enough.
• When a subroutine is common and frequently used, time spent on a more efficient implementation can result in benefits for every application that uses the subroutine. Examples include sorting, searching, pseudorandom number generation, kernel operations, database queries, and graphics.
In short, it's important to save time when you do not have any time to spare.
When is efficiency unimportant? Examples of these cases include prototypes that are used only a few times, cases where the input is small, when simplicity and ease of maintenance is more important, when the area concerned is not the bottle neck, or when there's another process or area in the code that would benefit far more from efficient design and attention to the algorithm(s).
0 comments:
Post a Comment
Your Suggestions and Reviews are valuable to us to make this blog better........