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In computer architecture, frequency scaling (also known as frequency ramping) is the technique of increasing a processor's frequency so as to enhance the performance of the system containing the processor in question. Frequency ramping was the dominant force in commodity processor performance increases from the mid-1980s until roughly the end ...
The purpose of overclocking is to increase the operating speed of a given component. [3] Normally, on modern systems, the target of overclocking is increasing the performance of a major chip or subsystem, such as the main processor or graphics controller, but other components, such as system memory or system buses (generally on the motherboard), are commonly involved.
Both dynamic voltage scaling and dynamic frequency scaling can be used to prevent computer system overheating, which can result in program or operating system crashes, and possibly hardware damage. Reducing the voltage supplied to the CPU below the manufacturer's recommended minimum setting can result in system instability.
Finally, tasks required of modern computers often emphasize quite different components, so that resolving a bottleneck for one task may not affect the performance of another. For these reasons, upgrading a CPU does not always have a dramatic effect. The concept of being CPU-bound is now one of many factors considered in modern computing ...
A graphical demo running as a benchmark of the OGRE engine. In computing, a benchmark is the act of running a computer program, a set of programs, or other operations, in order to assess the relative performance of an object, normally by running a number of standard tests and trials against it.
The performance increase of the 80286 over the 8086 (or 8088) could be more than 100% per clock cycle in many programs (i.e., a doubled performance at the same clock speed). This was a large increase, fully comparable to the speed improvements seven years later when the i486 (1989) or the original Pentium (1993) were introduced.
In practice, as more computing resources become available, they tend to get used on larger problems (larger datasets), and the time spent in the parallelizable part often grows much faster than the inherently serial work. In this case, Gustafson's law gives a less pessimistic and more realistic assessment of the parallel performance. [10]
An idle computer has a load number of 0 (the idle process is not counted). Each process using or waiting for CPU (the ready queue or run queue) increments the load number by 1. Each process that terminates decrements it by 1. Most UNIX systems count only processes in the running (on CPU) or runnable (waiting for CPU) states.