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Applications of paralleL processing
This document discusses various applications of parallel processing. It describes how parallel processing is used in numeric weather prediction to forecast weather by processing large amounts of observational data. It is also used in oceanography and astrophysics to study oceans and conduct particle simulations. Other applications mentioned include socioeconomic modeling, finite element analysis, artificial intelligence, seismic exploration, genetic engineering, weapon research, medical imaging, remote sensing, energy exploration, and more. The document also discusses loosely coupled and tightly coupled multiprocessors and the differences between the two approaches.
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Applications of paralleL processing
- 1. Applications of Parallel Processing Apresentation by chinmay terse vivek ashokan rahul nair rahul agarwal
- 2. Numeric weather prediction NWP uses mathematical models of atmosphere and oceans Taking current observations of weather and processing these data with computer models to forecast the future state of weather. Uses data assimilation to produce outputs
- 3. Oceanography and Astrophysics Used to study wealth of ocean using multiprocessors having large computational power with low power requirements. ROMS were used originally but now MPI programming methods are used. Computational astrophysics refers to the methods and computing tools developed and used in astrophysics research. PIC ,PM and n-body simulations are different important techniques for computational astrophysics.
- 4. Socio Economics Parallelprocessing is used for modelling of a economy of a nation/world. Programs system which involves cluster computing device to implement parallel algorithms of scenario calculations ,optimization are used in such economic models. Such program system serves for conducting multi-scenario calculations to design a suitable development strategy for a region.
- 5. Finite element analysis FEA is a numeric method commonly used for multiphysics problem. Used in design of huge structures like ships, dams, supersonic jets etc. In FEA extremely large amount of partial differential equations are to solved concurrently and hence parallel processing elements are used.
- 6. Artificial Intelligence andAutomation AI is the intelligence exhibited by machines or software. AI systems requires large amount of parallel comptuting for which they are used. Four types 1.Image processing 2.Expert Systems 3.Natural Language Processing(NLP) 4.Pattern Recognition
- 7. Seismic Exploration Itis a method of exploration that uses the principles of seismology to estimate the properties of the Earth's subsurface from reflected seismic waves. When a seismic wave travelling through the Earth encounters an interface between two materials, some of the wave energy will reflect off the interface which are analysed to determine underground strata. Such large number of reflected waves are analysed using sensors and parallel architecture
- 8. Genetic Engineering Itis the direct manipulation of an organism's genome using biotechnology for eg. Dna sequence analysis. Several of these analysis produce huge amounts of information which becomes difficult to hande using single processing units because of which parallel processing algorithms are used
- 9. Weapon Research andDefense Computer clusters are used in simulations that show a nuclear weapon's performance in precise molecular detail, tools that are used for national defense. Parallel computing is required which are needed to more efficiently certify nuclear weapons,to accurately show molecular-scale reactions taking place over milliseconds, or thousandths of a second. They are also used in plutonium research to study its behaviour under high pressure whose alloys are then used for making explosives
- 10. Medical Applications Parallelcomputing is used in medical image processing Used for scanning human body and scanning human brain Used in MRI reconstruction Used for vertebra detection and segmentation in X-ray images Used for brain fiber tracking
- 11. Remote Sensing Applications It is a software application that processes remote sensing data. Remote sensing applications read specialized file formats that contain sensor image data, georeferencing information, and sensor metadata. Computer analysis of such remotely sensed earth resources data has many applications in agriculture,forestry etc. Explosive amounts of pictorial information needs to be processed in this area.
- 12. Energy Resource Exploration Resource Exploration is a method to gather and manage information about energy resources like oil,natural gas etc. Computers here help in the discovery and management of such energy resources. This sector maintains the records of global energy crisis and also helps in ensuring nuclear reactor safety
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- 14. Loosely coupled multiprocessors In loosely coupled multiprocessor each processor has its own memory (i.e. faster access to its local or own memory). processor interact through interconnection structure or by message passing using standard primitive functions send() & receive(). The access to remote memory attached to other processors takes longer time due to added delay through the interconnection network. It is characterized by non-uniform memory access time. It is easier to organize & write operating system for loosely coupled systems.
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- 16. Tightly Coupled Multiprocessors Also known as shared memory multiprocessors. In this system, there is a single system-wise primary memory that is shared by all processors. Communication between processors takes places through shared memory. Every processor has unmapped local memories which is used to store kernel data. High interaction between processors. Shared memory modules can communicate through PMIN.
- 17. DIFFERNCE BETWEEN TIGHTLYAND LOOSELY COUPLED MULTIPROCESSOR Tightly coupled multiprocessor 1)Multiprocessor systems contain multiple CPUs that are connected at the bus level. These CPUs may have access to a central shared memory (SMP or UMA), or may participate in a memory hierarchy with both local and shared memory (NUMA). 2) Tightly-coupled systems perform better & are physically smaller than loosely-coupled system 3) More expensive. 4) In a tightly-coupled system, the delay Experienced , when a message is sent from one computer to another is short and data rate is high; that is, the number of bits per second that can be transferred is large. Loosely-coupled multiprocessor 1)This system(often referred to as clusters) are based on multiple standalone single or dual process or commodity computers interconnected via a high speed communication system . 2) Loosely coupled system is just Opposite of tightly coupled system. 3) Less expensive. 4)In a loosely-coupled system, the opposite is true: The inter-machine message delay is large and the data rate is low.
- 18. DIFFERNCE BETWEEN TIGHTLYAND LOOSELY COUPLED MULTIPROCESSOR Tightly coupled multiprocessor 5) Tightly-coupled systems tend to be much more energy efficient than clusters. This is because considerable economies can be realized by designing components to work together from the beginning in tightly- coupled systems. 6) For example two computers connected by a2400 bit/sec modem over the telephone system are certain to be loosely coupled. Loosely-coupled multiprocessor 5)loosely-coupled systems use components that were not necessarily intended specifically for use in such systems. 6)For example, two CPU chips on the same printed circuit board and connected by wire etched onto the board are likely to be tightly Coupled.
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