Computer science,Scalar processor,Flynn's taxonomy,vector processor,superscalar processor,Parallel computing,Memory hierarchy,Virtual memory,Shared memory,advanced architecture and parallel processing - onclick786

Computer science,Scalar processor,Flynn's taxonomy,vector processor,superscalar processor,Parallel computing,Memory hierarchy,Virtual memory,Shared memory,advanced architecture and parallel processing  

Computer science


Computer science is the scientific and practical approach to computation and its applications. It is the systematic study of the feasibility, structure, expression, and mechanization of the methodical procedures (or algorithms) that underlie the acquisition, representation, processing, storage, communication of, and access to information. An alternate, more succinct definition of computer science is the study of automating algorithmic processes that scale. A computer scientist specializes in the theory of computation and the design of computational systems.


Scalar processor

Scalar processors represent a class of computer processors. A scalar processor processes only one datum at a time, with typical data items being integers or floating point numbers). A scalar processor is classified as a SISD processor (Single Instructions, Single Data) in Flynn's taxonomy.



Flynn's taxonomy

Flynn's taxonomy is a classification of computer architectures, proposed by Michael J. Flynn in 1966. The classification system has stuck, and has been used as a tool in design of modern processors and their functionalities. Since the rise of multiprocessing central processing units (CPUs), a multiprogramming context has evolved as an extension of the classification system.


vector processor

vector processor a single instruction operates simultaneously on multiple data items (referred to as "SIMD"). The difference is analogous to the difference between scalar and vector arithmetic.

superscalar processor

A superscalar processor, on the other hand, executes more than one instruction during a clock cycle by simultaneously dispatching multiple instructions to redundant functional units on the processor. Each functional unit is not a separate CPU core but an execution resource within a single CPU such as an arithmetic logic unit, a bit shifter, or a multiplier.

A superscalar processor is a CPU that implements a form of parallelism called instruction-level parallelism within a single processor. It therefore allows for more throughput (the number of instructions that can be executed in a unit of time) than would otherwise be possible at a given clock rate. A superscalar processor can execute more than one instruction during a clock cycle by simultaneously dispatching multiple instructions to different execution units on the processor. Each execution unit is not a separate processor (or a core if the processor is a multi-core processor), but an execution resource within a single CPU such as an arithmetic logic unit.

Parallel computing

Parallel computing is a type of computation in which many calculations are carried out simultaneously, or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. 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.



Memory hierarchy

In computer architecture the memory hierarchy is a concept used for storing & discussing performance issues in computer architectural design, algorithm predictions, and the lower level programming constructs such as involving locality of reference. The memory hierarchy in computer storage distinguishes each level in the hierarchy by response time. Since response time, complexity, and capacity are related, the levels may also be distinguished by their performance and controlling technologies.


Virtual memory

In computing, virtual memory is a memory management technique that is implemented using both hardware and software. It maps memory addresses used by a program, called virtual addresses, into physical addresses in computer memory. Main storage as seen by a process or task appears as a contiguous address space or collection of contiguous segments. The operating system manages virtual address spaces and the assignment of real memory to virtual memory. Address translation hardware in the CPU, often referred to as a memory management unit or MMU, automatically translates virtual addresses to physical addresses. Software within the operating system may extend these capabilities to provide a virtual address space that can exceed the capacity of real memory and thus reference more memory than is physically present in the computer.


Shared memory

In computer science, shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs. Depending on context, programs may run on a single processor or on multiple separate processors.



















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Ice cream,Gelato,ice cream vs gelato differences,Ice cream headache,head freezing sensation ,ice cream and head cold - onclick786

Ice cream,Ice cream headache,head freezing sensation ,ice cream and head cold ,ice cream vs gelato differences

Ice cream headache

A brain freeze, also known as ice-cream headache, cold-stimulus headache, trigeminal headache or its given scientific name sphenopalatine ganglioneuralgia (meaning "nerve pain of the sphenopalatine ganglion"), is a form of brief pain or headache commonly associated with consumption (particularly quick consumption) of cold beverages or foods such as ice cream and ice pops. It is caused by having something cold touch the roof of the mouth (palate), and is believed to result from a nerve response causing rapid constriction and swelling of blood vessels[3] or a "referring" of pain from the roof of the mouth to the head.The rate of intake for cold foods has been studied as a contributing factor. Ice-cream headache is distinct from dentin hypersensitivity, a type of pain that can occur under similar circumstances.

ice cream

Ice cream (derived from earlier iced cream or cream ice) is a sweetened frozen food typically eaten as a snack or dessert. It is usually made from dairy products, such as milk and cream, and often combined with fruits or other ingredients and flavours. It is typically sweetened with sucrose, corn syrup, cane sugar, beet sugar, and/or other sweeteners. Typically, flavourings and colourings are added in addition to stabilizers. The mixture is stirred to incorporate air spaces and cooled below the freezing point of water to prevent detectable ice crystals from forming. The result is a smooth, semi-solid foam that is solid at very low temperatures (<35 °F / 2 °C). It becomes more malleable as its temperature increases.

Gelato

Gelato is the Italian word for ice cream. It starts out with a similar custard base asice cream, but has a higher proportion of milk and a lower proportion of cream and eggs (or no eggs at all). It is churned at a much slower rate, incorporating less air and leaving the gelato denser than ice cream.

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Data mining,Data warehouse, Data Warehouse Overview,Data mart,Online analytical processing (OLAP),Online transaction processing (OLTP),Predictive analysis,Machine learning,Clustering,Bayesian networks,Genetic algorithms - onclick786

Data mining,Data warehouse, Data Warehouse Overview,Data mart,Online analytical processing (OLAP),Online transaction processing (OLTP),Predictive analysis,Machine learning,Clustering,Bayesian networks,Genetic algorithms,

Data mining

Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in largedata sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model andinference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.

Data warehouse

In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting anddata analysis, and is considered as a core component of business intelligence environment. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating analytical reports for knowledge workers throughout the enterprise. Examples of reports could range from annual and quarterly comparisons and trends to detailed daily sales analysis.The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before it is used in the DW for reporting.

                                                                               Data Warehouse Overview

Data mart

A data mart is a simple form of a data warehouse that is focused on a single subject (or functional area) hence, they draw data from a limited number of sources such as sales, finance or marketing. Data marts are often built and controlled by a single department within an organization. The sources could be internal operational systems, a central data warehouse, or external data. Denormalization is the norm for data modeling techniques in this system. Given that data marts generally cover only a subset of the data contained in a data warehouse, they are often easier and faster to implement.

Online analytical processing (OLAP)

OLAP is characterized by a relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems, response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. OLAP databases store aggregated, historical data in multi-dimensional schemas (usually star schemas). OLAP systems typically have data latency of a few hours, as opposed to data marts, where latency is expected to be closer to one day.The OLAP approach is used to analyze multidimensional data from multiple sources and perspectives. The three basic operations in OLAP are : Roll-up (Consolidation), Drill-down and Slicing & Dicing.

Online transaction processing (OLTP)

OLTP is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). OLTP systems emphasize very fast query processing and maintainingdata integrity in multi-access environments. For OLTP systems, effectiveness is measured by the number of transactions per second. OLTP databases contain detailed and current data. The schema used to store transactional databases is the entity model (usually 3NF).Normalization is the norm for data modeling techniques in this system.

Predictive analysis

Predictive analysis is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes. Predictive analysis is different from OLAP in that OLAP focuses on historical data analysis and is reactive in nature, while predictive analysis focuses on the future. These systems are also used for CRM (customer relationship management).

Machine learning

Machine learning is a subfield of computer science (more particularly soft computing) that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.

Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. These are

• Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
• Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
• Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal. Another example is learning to play a game by playing against an opponent.

Between supervised and unsupervised learning is semi-supervised learning, where the teacher gives an incomplete training signal: a training set with some (often many) of the target outputs missing. Transduction is a special case of this principle where the entire set of problem instances is known at learning time, except that part of the targets are missing.

Clustering

Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to some predesignated criterion or criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated for example by internal compactness (similarity between members of the same cluster) and separation between different clusters. Other methods are based on estimated density and graph connectivity. Clustering is a method of unsupervised learning, and a common technique for statistical data analysis.

Bayesian networks

A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning.

Genetic algorithms

A genetic algorithm (GA) is a search heuristic that mimics the process of natural selection, and uses methods such as mutation and crossover to generate new genotype in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms found some uses in the 1980s and 1990s. Vice versa, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.

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