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Parallel Architectures
Published in Pranabananda Chakraborty, Computer Organisation and Architecture, 2020
Multiprocessor are systems with multiple CPUs capable of independently executing different tasks in parallel. Apart from having shared common memory or unshared distributed memories, these processors also share resources such as, communication facilities, I/O devices, system utilities, program libraries, databases, and similar others. They are operated under the control of an integrated operating system that provides interaction between processors and their programs at the job, task, file, and even in data element level. Multiprocessors can be classified in a number of ways: (i) The number of CPUs present in a system. Modestly parallel systems contain 2 to about 30 processors, while massively parallel systems can contain even thousands of such processors. (ii) The patterns of interconnections that are created between the CPUs and the memory modules and that too, whether the memory modules would be centrally shared or distributed shared. (iii) The way the multiple CPUs themselves will be interconnected with one another. (iv) The form of interconnection networks (i.e. whether static or dynamic) to be used. Still, many other aspects remain that are considered at the time of multiprocessor implementation.
Parallel Computing Models
Published in Vivek Kale, Parallel Computing Architectures and APIs, 2019
In dataflow computers, an operation to execute is governed not by the current instruction of a program but by the availability of its operands. A dataflow computer simultaneously performs all operations that can be currently done, that is, whose operands are known. Therefore, data plays an active role in computation, as its availability establishes the operation and the time when it is executed. In a multiprocessor computer, parallel computation is specified by a programmer by indicating which sequences of operations can be executed concurrently. In a dataflow computer, the possibilities of parallel execution are not specified explicitly—they come from dependencies between data, since the input data of operations is the output data (results) of operations carried out previously.
NoC and System-Level Design
Published in Hoi-Jun Yoo, Kangmin Lee, Jun Kyoung Kim, Low-Power NoC for High-Performance SoC Design, 2018
Hoi-Jun Yoo, Kangmin Lee, Jun Kyoung Kim
The SoC platform has recently evolved into Multiprocessor SoC (MPSoC). The SoC or the embedded system follows the architecture of the desktop computer because most system engineers are familiar with the PC and its software solutions. The current PC contains multiple processors, multicore CPU, DSP, and other application-specific processors—to support high computing power with less power consumption for advanced applications such as multimedia or 3D games [22]. So far, the multiprocessor or multicore has been studied in computing discipline as a part of parallel computing. The purpose of developing the multiprocessor system is to improve throughput, scalability, and reliability of the computing system. They have established a well-developed theory and set of practice on the parallel computer. They can be split into two categories, centralized computing and distributed computing, according to the locations of the hardware and software resources. Centralized computing, which has been studied to construct the super computer, is more appropriate for MPSoC. However, there is a clear difference between the multicore or multiprocessor and MPSoC. MPSoC provides a system solution like the Emotion Engine (Figure 1.30), giving full video and graphics solutions, whereas multicore or multiprocessor is just a processing block.
Big Data technologies to process spatial and attribute data when designing and operating mine-engineering systems
Published in International Journal of Image and Data Fusion, 2019
Yuri A. Stepanov, Alexander V. Stepanov
Other specific features of NoSQL solutions are as follows: Use of various types of storages.Potential development of database with no prescribed scheme.Use of multiprocessor systems.Linear scalability (performance is increased through adding processors).Innovation: ‘not only SQL’ opens up many possibilities for storing and processing data.A decrease in time required for development.Rate: even with little data, end users may make certain of decreasing system response time from hundreds of milliseconds to milliseconds (NoSQL 2018).