Complex Instruction Set Computer
(CISC) A processor where each instruction can perform several lowlevel
operations such as memory access, arithmetic operations or address calculations.
The term was coined in contrast to Reduced Instruction Set Computer.
Before the first RISC processors were designed, many computer architects were
trying to bridge the "semantic gap"  to design instruction sets to support
highlevel languages by providing "highlevel" instructions such as procedure
call and return, loop instructions such as "decrement and branch if nonzero"
and complex addressing modes to allow data structure and array accesses to be
compiled into single instructions.
While these architectures achieved their aim of allowing highlevel language
constructs to be expressed in fewer instructions, it was observed that they did
not always result in improved performance. For example, on one processor it was
discovered that it was possible to improve the performance by NOT using the
procedure call instruction but using a sequence of simpler instructions instead.
Furthermore, the more complex the instruction set, the greater the overhead of
decoding an instruction, both in execution time and silicon area. This is
particularly true for processors which used microcode to decode the (macro)
instruction. It is easier to debug a complex instruction set implemented in
microcode than one whose decoding is "hardwired" in silicon.
Examples of CISC processors are the Motorola 680x0 family and the Intel 80186
through Intel 486 and Pentium.
(19941010)
Nearby terms:
complete partial ordering « complete theory «
complete unification « Complex Instruction Set
Computer » complexity » complexity analysis »
complexity class
complexity
<algorithm> The level in difficulty in solving mathematically posed
problems as measured by the time, number of steps or arithmetic operations, or
memory space required (called time complexity, computational complexity, and
space complexity, respectively).
The interesting aspect is usually how complexity scales with the size of the
input (the "scalability"), where the size of the input is described by some
number N. Thus an algorithm may have computational complexity O(N^2) (of the
order of the square of the size of the input), in which case if the input
doubles in size, the computation will take four times as many steps. The ideal
is a constant time algorithm (O(1)) or failing that, O(N).
See also NPcomplete.
(19941020)
Nearby terms:
complete theory « complete unification « Complex
Instruction Set Computer « complexity »
complexity analysis » complexity class » complexity
measure
complexity analysis
In sructured program design, a qualitycontrol operation that counts the number
of "compares" in the logic implementing a function; a value of less than 10 is
considered acceptable.
Nearby terms:
complete unification « Complex Instruction Set
Computer « complexity « complexity analysis »
complexity class » complexity measure » complex
number
complexity class
<algorithm> A collection of algorithms or computable functions with the
same complexity.
(19960424)
Nearby terms:
Complex Instruction Set Computer « complexity «
complexity analysis « complexity class »
complexity measure » complex number » complex
programmable logic device
complexity measure
<algorithm> A quantity describing the complexity of a computation.
(19960424)
Nearby terms:
complexity « complexity analysis « complexity class
«
complexity measure » complex number » complex
programmable logic device » component
complex number
<mathematics> A number of the form x+iy where i is the square root of 1,
and x and y are real numbers, known as the "real" and "imaginary" part. Complex
numbers can be plotted as points on a twodimensional plane, known as an Argand
diagram, where x and y are the Cartesian coordinates.
An alternative, polar notation, expresses a complex number as (r e^it) where e
is the base of natural logarithms, and r and t are real numbers, known as the
magnitude and phase. The two forms are related:
r e^it = r cos(t) + i r sin(t)
= x + i y
where
x = r cos(t)
y = r sin(t)
All solutions of any polynomial equation can be expressed as complex
numbers. This is the socalled Fundamental Theorem
of Algebra, first proved by Cauchy.
Complex numbers are useful in many fields of physics, such as electromagnetism
because they are a useful way of representing a magnitude and phase as a single
quantity.
(19950410)
Nearby terms:
complexity analysis « complexity class « complexity
measure « complex number » complex
programmable logic device » component » component
architecture
complex programmable logic device
<hardware> (CPLD) A programmable circuit similar to an FPGA, but
generally on a smaller scale, invented by Xilinx, Inc.
(19980926)
Nearby terms:
complexity class « complexity measure « complex
number «
complex programmable logic device » component »
component architecture » component based development
