Minggu, 22 Juni 2014

Parallel Computation

Parallel computing is a form of computation in which many calculations are carried out simultaneously,operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently ("in parallel"). There are several different forms of parallel computing: bit-level, instruction level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.

Distributed computing is a field of computer science that studies distributed systems. A distributed system is a software system in which components located on networked computers communicate and coordinate their actions by passing messages. The components interact with each other in order to achieve a common goal. Three significant characteristics of distributed systems are: concurrency of components, lack of a global clock, and independent failure of components. Examples of distributed systems vary from SOA-based systems to massively multiplayer online games to peer-to-peer applications.

Parallel computing is the simultaneous execution of the same task (split up and specially adapted) on multiple processors in order to obtain faster results. There are many different kinds of parallel computers (or "parallel processors"). Flynn's taxonomy classifies parallel (and serial) computers according to whether all processors execute the same instructions at the same time (single instruction/multiple data -- SIMD) or each processor executes different instructions (multiple instruction/multiple data -- MIMD). They are also distinguished by the mode used to communicate values between processors. Distributed memory machines communicate by explicit message passing, while shared memory machines have a global memory address space, through which values can be read and written by the various processors.

Thread of execution is the smallest sequence of programmed instructions that can be managed independently by an operating system scheduler. The scheduler itself is a light-weight process. The implementation of threads and processes differs from one operating system to another, but in most cases, a thread is contained inside a process. Multiple threads can exist within the same process and share resources such as memory, while different processes do not share these resources. In particular, the threads of a process share the latter's instructions (its code) and its context (the values that its variables reference at any given moment). On a single processor, multithreading is generally implemented by time-division multiplexing (as in multitasking): the processor switches between different threads. This context switching generally happens frequently enough that the user perceives the threads or tasks as running at the same time. On a multiprocessor or multi-core system, threads can be truly concurrent, with every processor or core executing a separate thread simultaneously.

CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs) that they produce.[1] CUDA gives program developers direct access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. Using CUDA, the GPUs can be used for general purpose processing (i.e., not exclusively graphics); this approach is known as GPGPU. Unlike CPUs, however, GPUs have a parallel throughput architecture that emphasizes executing many concurrent threads slowly, rather than executing a single thread very quickly.

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