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Idle is a state that a computer processor is in when it is not being used by any program. Every program or task that runs on a computer system occupies a certain amount of processing time on the CPU. If the CPU has completed all tasks it is idle. Modern processors use idle time to save power.
In addition to the CPU drivers offered by AMD, several motherboard manufacturers have released software to give the end user more control over the Cool 'n' Quiet feature, as well as the other new features of AMD processors and chipsets. Using these applications, one can even control the CPU voltage explicitly. PhenomMsrTweaker (SourceForge link)
A good temperature for your desktop computer's CPU is around 120℉ when idle, and under 175℉ when under stress.
ACPI 1.0 (1996) defines a way for a CPU to go to idle "C states", but defines no frequency-scaling system. ACPI 2.0 (2000) introduces a system of P states (power-performance states) that a processor can use to communicate its possible frequency–power settings to the OS. The operating system then sets the speed as needed by switching between ...
In many applications, the CPU and other components are idle much of the time, so idle power contributes significantly to overall system power usage. When the CPU uses power management features to reduce energy use, other components, such as the motherboard and chipset, take up a larger proportion of the computer's energy.
With a single-execution-unit processor, the best CPI attainable is 1. However, with a multiple-execution-unit processor, one may achieve even better CPI values (CPI < 1). In this case, the processor is said to be superscalar. To get better CPI values without pipelining, the number of execution units must be greater than the number of stages.
Some Xeon Phi processors support four-way hyper-threading, effectively quadrupling the number of threads. [1] Before the Coffee Lake architecture, most Xeon and all desktop and mobile Core i3 and i7 supported hyper-threading while only dual-core mobile i5's supported it.
2×10 15: Nvidia DGX-2 a 2 Petaflop Machine Learning system (the newer DGX A100 has 5 Petaflop performance) 11.5×10 15: Google TPU pod containing 64 second-generation TPUs, May 2017 [9] 17.17×10 15: IBM Sequoia's LINPACK performance, June 2013 [10] 20×10 15: roughly the hardware-equivalent of the human brain according to Ray Kurzweil.
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