What is overhead in computer science

Kernel computing — A kernel connects the application software to the hardware of a computer In computing, the kernel is the main component of most computer operating systems; it is a bridge between applications and the actual data processing done at the hardware… … Wikipedia.

Scheduling computing — This article is about processes assignment in operating systems. For other uses, see Scheduling disambiguation. Scheduling is a key concept in computer multitasking, multiprocessing operating system and real time operating system designs.

Fragmentation computing — In computer storage, fragmentation is a phenomenon in which storage space is used inefficiently, reducing storage capacity and in most cases reducing the performance. The term is also used to denote the wasted space itself. There are three… … Wikipedia. Interpreter computing — In computer science, an interpreter normally means a computer program that executes, i. An interpreter may be a program that either executes the source code directly translates source… … Wikipedia.

Data Intensive Computing — is a class of parallel computing applications which use a data parallel approach to processing large volumes of data typically terabytes or petabytes in size and typically referred to as Big Data. Computing applications which devote most of their … Wikipedia. Overhead computing. The energy spent looking for food, getting it and actually eating it consumes energy and is overhead! Overhead is something wasted in order to accomplish a task.

The goal is to make overhead very very small. In computer science lets say you want to print a number, thats your task. But storing the number, the setting up the display to print it and calling routines to print it, then accessing the number from variable are all overhead.

Wikipedia has us covered :. In computer science, overhead is generally considered any combination of excess or indirect computation time, memory, bandwidth, or other resources that are required to attain a particular goal. It is a special case of engineering overhead. Overhead typically reffers to the amount of extra resources memory, processor, time, etc. For example, the overhead of inserting into a balanced Binary Tree could be much larger than the same insert into a simple Linked List the insert will take longer, use more processing power to balance the Tree, which results in a longer percieved operation time by the user.

For a programmer overhead refers to those system resources which are consumed by your code when it's running on a giving platform on a given set of input data. Usually the term is used in the context of comparing different implementations or possible implementations. For example we might say that a particular approach might incur considerable CPU overhead while another might incur more memory overhead and yet another might weighted to network overhead and entail an external dependency, for example.

The obvious approach is to loop over the inputs, keeping a running total and a count. When the last number is encountered signaled by "end of file" EOF, or some sentinel value, or some GUI buttom, whatever then we simply divide the total by the number of inputs and we're done. This approach incurs almost no overhead in terms of CPU, memory or other resources. It's a trivial task.

Another possible approach is to "slurp" the input into a list. In a particular bad implementation we might perform the sum operation using recursion but without tail-elimination. Now, in addition to the memory overhead for our list we're also introducing stack overhead which is a different sort of memory and is often a more limited resource than other forms of memory.

This shifts our local memory overhead to some other server, and incurs network overhead and external dependencies on our execution. Note that the remote server may or may not have any particular memory overhead associated with this task it might shove all the values immediately out to storage, for example.

Hypothetically might consider an implementation over some sort of cluster possibly to make the averaging of trillions of values feasible. We can also talk about the overhead incurred by factors beyond the programmer's own code. For example compilation of some code for 32 or 64 bit processors might entail greater overhead than one would see for an old 8-bit or bit architecture.

This might involve larger memory overhead alignment issues or CPU overhead where the CPU is forced to adjust bit ordering or used non-aligned instructions, etc or both. Note that the disk space taken up by your code and it's libraries, etc. Overhead is simply the more time consumption in program execution. Example ; when we call a function and its control is passed where it is defined and then its body is executed, this means that we make our CPU to run through a long process first passing the control to other place in memory and then executing there and then passing the control back to the former position , consequently it takes alot performance time, hence Overhead.

Our goals are to reduce this overhead by using the inline during function definition and calling time, which copies the content of the function at the function call hence we dont pass the control to some other location, but continue our program in a line, hence inline. You could use a dictionary. The definition is the same. But to save you time, Overhead is work required to do the productive work.

For instance, an algorithm runs and does useful work, but requires memory to do its work. This memory allocation takes time, and is not directly related to the work being done, therefore is overhead. You can check Wikipedia. But mainly when more actions or resources are used. Like if you are familiar with. NET there you can have value types and reference types. Reference types have memory overhead as they require more memory than value types. A concrete example of overhead is the difference between a "local" procedure call and a "remote" procedure call.

For example, with classic RPC and many other remote frameworks, like EJB , a function or method call looks the same to a coder whether its a local, in memory call, or a distributed, network call. So, while the core implementation will "cost the same", the "overhead" involved is quite different.

Your operating system must power your screen with the elements of your graphical user interface, or GUI, and talk to your internal and external hardware. Many of these tasks continue for as long as you keep your computer powered on and in an active state.

Each task requires at least a little bit of processor power, and thus contributes to processor overhead. If you run a virtual machine—a simulated computer entirely based in software—your computer must support your primary operating system as well as your emulated operating system.

Some computing tasks last only long enough to print a document, burn a disk, send a message or play an alert sound. Although your computer's ongoing operating processes may be responsible for some of these temporary duties, others call on software modules that take care of business and then shut down. Still more tasks remain active only as long as you keep a particular application running. Operating system or software bugs can draw too many CPU clock cycles and overtax your system unexpectedly.

If you install a new operating system or a new application version and notice your system slowing dramatically, you may want to check in at online discussion and support venues to see if other users experience the same problems.