Concept
Parallel computing
Related concepts (78)
Parallel algorithm
In computer science, a parallel algorithm, as opposed to a traditional serial algorithm, is an algorithm which can do multiple operations in a given time. It has been a tradition of computer science to describe serial algorithms in abstract machine models, often the one known as random-access machine. Similarly, many computer science researchers have used a so-called parallel random-access machine (PRAM) as a parallel abstract machine (shared-memory).
Glossary of computer hardware terms
This glossary of computer hardware terms is a list of definitions of terms and concepts related to computer hardware, i.e. the physical and structural components of computers, architectural issues, and peripheral devices.
Automatic vectorization
Automatic vectorization, in parallel computing, is a special case of automatic parallelization, where a computer program is converted from a scalar implementation, which processes a single pair of operands at a time, to a vector implementation, which processes one operation on multiple pairs of operands at once. For example, modern conventional computers, including specialized supercomputers, typically have vector operations that simultaneously perform operations such as the following four additions (via SIMD or SPMD hardware): However, in most programming languages one typically writes loops that sequentially perform additions of many numbers.
Systolic array
In parallel computer architectures, a systolic array is a homogeneous network of tightly coupled data processing units (DPUs) called cells or nodes. Each node or DPU independently computes a partial result as a function of the data received from its upstream neighbours, stores the result within itself and passes it downstream. Systolic arrays were first used in Colossus, which was an early computer used to break German Lorenz ciphers during World War II. Due to the classified nature of Colossus, they were independently invented or rediscovered by H.
Memory access pattern
In computing, a memory access pattern or IO access pattern is the pattern with which a system or program reads and writes memory on secondary storage. These patterns differ in the level of locality of reference and drastically affect cache performance, and also have implications for the approach to parallelism and distribution of workload in shared memory systems. Further, cache coherency issues can affect multiprocessor performance, which means that certain memory access patterns place a ceiling on parallelism (which manycore approaches seek to break).
Loop unrolling
Loop unrolling, also known as loop unwinding, is a loop transformation technique that attempts to optimize a program's execution speed at the expense of its size, which is an approach known as space–time tradeoff. The transformation can be undertaken manually by the programmer or by an optimizing compiler. On modern processors, loop unrolling is often counterproductive, as the increased code size can cause more cache misses; cf. Duff's device.
Software lockout
In multiprocessor computer systems, software lockout is the issue of performance degradation due to the idle wait times spent by the CPUs in kernel-level critical sections. Software lockout is the major cause of scalability degradation in a multiprocessor system, posing a limit on the maximum useful number of processors. To mitigate the phenomenon, the kernel must be designed to have its critical sections as short as possible, therefore decomposing each data structure in smaller substructures.
Compile time
In computer science, compile time (or compile-time) describes the time window during which a language's statements are converted into 0s and 1s for the computer to understand. The term is used as an adjective to describe concepts related to the context of program compilation, as opposed to concepts related to the context of program execution (runtime). For example, compile-time requirements are programming language requirements that must be met by source code before compilation and compile-time properties are properties of the program that can be reasoned about during compilation.
Scalar processor
Scalar processors are a class of computer processors that process only one data item at a time. Typical data items include integers and floating point numbers. A scalar processor is classified as a single instruction, single data (SISD) processor in Flynn's taxonomy. The Intel 486 is an example of a scalar processor. It is to be contrasted with a vector processor where a single instruction operates simultaneously on multiple data items (and thus is referred to as a single instruction, multiple data (SIMD) processor).
Explicit parallelism
In computer programming, explicit parallelism is the representation of concurrent computations by means of primitives in the form of special-purpose directives or function calls. Most parallel primitives are related to process synchronization, communication or task partitioning. As they seldom contribute to actually carry out the intended computation of the program, their computational cost is often considered as parallelization overhead. The advantage of explicit parallel programming is the absolute programmer control over the parallel execution.