output load - definição. O que é output load. Significado, conceito
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O que (quem) é output load - definição

A PROPERTY DESCRIBING RUN-TIME COMPLEXITY OF ALGORITHMS
Output sensitivity; Output-sensitivity

Load (computing)         
  • [[htop]] displaying a significant computing load (top right: ''Load average:'')
AMOUNT OF COMPUTATIONAL WORK PERFORMED
Load average; Unix load average; System load; Load Averages; Load averages; Load Average; System load average; Load times; Loadavg; IOtop (Unix); Load (Unix)
In UNIX computing, the system load is a measure of the amount of computational work that a computer system performs. The load average represents the average system load over a period of time.
Output (economics)         
QUANTITY OF GOODS OR SERVICES PRODUCED IN A GIVEN TIME PERIOD, BY A FIRM, INDUSTRY, OR COUNTRY, WHETHER CONSUMED OR USED FOR FURTHER PRODUCTION
Netput; Economic output
Output in economics is the "quantity of goods or services produced in a given time period, by a firm, industry, or country",Alan Deardorff. output, Deardorff asspoo's Glossary of International Economics.
Dummy load         
  • Four heavy duty dummy loads used at an amplifier shootout
  • Microwave dummy load designed to attach to [[waveguide]].
DEVICE USED TO SIMULATE AN ELECTRICAL LOAD
Electronic load; Electronic Load; Dummy antenna
A dummy load is a device used to simulate an electrical load, usually for testing purposes. In radio a dummy antenna is connected to the output of a radio transmitter and electrically simulates an antenna, to allow the transmitter to be adjusted and tested without radiating radio waves.

Wikipédia

Output-sensitive algorithm

In computer science, an output-sensitive algorithm is an algorithm whose running time depends on the size of the output, instead of, or in addition to, the size of the input. For certain problems where the output size varies widely, for example from linear in the size of the input to quadratic in the size of the input, analyses that take the output size explicitly into account can produce better runtime bounds that differentiate algorithms that would otherwise have identical asymptotic complexity.