A Paradigm For Instruction Level Parallel Processing
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Instruction-level Parallel Processors
Author: Hwa-Chung Torng
language: en
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
Release Date: 1995
Delineates the innovations and advances that led to the development of Intel's Pentium and IBM/Motorola/Apple's PowerPC, and explores the potential design and implementation of instruction-level parallelism in modern processors. Papers illustrate solutions to the true data dependency problem and the
Parallel Processing and Parallel Algorithms
Author: Seyed H Roosta
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Motivation It is now possible to build powerful single-processor and multiprocessor systems and use them efficiently for data processing, which has seen an explosive ex pansion in many areas of computer science and engineering. One approach to meeting the performance requirements of the applications has been to utilize the most powerful single-processor system that is available. When such a system does not provide the performance requirements, pipelined and parallel process ing structures can be employed. The concept of parallel processing is a depar ture from sequential processing. In sequential computation one processor is in volved and performs one operation at a time. On the other hand, in parallel computation several processors cooperate to solve a problem, which reduces computing time because several operations can be carried out simultaneously. Using several processors that work together on a given computation illustrates a new paradigm in computer problem solving which is completely different from sequential processing. From the practical point of view, this provides sufficient justification to investigate the concept of parallel processing and related issues, such as parallel algorithms. Parallel processing involves utilizing several factors, such as parallel architectures, parallel algorithms, parallel programming lan guages and performance analysis, which are strongly interrelated. In general, four steps are involved in performing a computational problem in parallel. The first step is to understand the nature of computations in the specific application domain.