Computer Architecture: Components, Types, Examples | Spiceworks

  • Computer architecture is defined as the end-to-end structure of a computer system that determines how its components interact with each other in helping execute the machine’s purpose (i.e., processing data).
  • This article explains the components of computer architecture and its key types and gives a few notable examples.

What Is Computer Architecture?

Computer architecture refers to the end-to-end structure of a computer system that determines how its components interact with each other in helping to execute the machine’s purpose (i.e., processing data), often avoiding any reference to the actual technical implementation.

Examples of Computer Architecture: Von Neumann Architecture (a) and Harvard Architecture (b)

Source: ResearchGateOpens a new window

Computers are an integral element of any organization’s infrastructure, from the equipment employees use at the office to the cell phones and wearables they use to work from home. All computers, regardless of their size, are founded on a set of principles describing how hardware and software connect to make them function. This is what constitutes computer architecture.

Computer architecture is the arrangement of the components that comprise a computer system and the engine at the core of the processes that drive its functioning. It specifies the machine interface for which programming languages and associated processors are designed.

Complex instruction set computer (CISC) and reduced instruction set computer (RISC) are the two predominant approaches to the architecture that influence how computer processors function.

CISC processors have one processing unit, auxiliary memory, and a tiny register set containing hundreds of unique commands. These processors execute a task with a single instruction, making a programmer’s work simpler since fewer lines of code are required to complete the operation. This method utilizes less memory but may need more time to execute instructions.

A reassessment led to the creation of high-performance computers based on the RISC architecture. The hardware is designed to be as basic and swift as possible, and sophisticated instructions can be executed with simpler ones.

How does computer architecture work?

Computer architecture allows a computer to compute, retain, and retrieve information. This data can be digits in a spreadsheet, lines of text in a file, dots of color in an image, sound patterns, or the status of a system such as a flash drive.

  • Purpose of computer architecture: Everything a system performs, from online surfing to printing, involves the transmission and processing of numbers. A computer’s architecture is merely a mathematical system intended to collect, transmit, and interpret numbers.
  • Data in numbers: The computer stores all data as numerals. When a developer is engrossed in machine learning code and analyzing sophisticated algorithms and data structures, it is easy to forget this.
  • Manipulating data: The computer manages information using numerical operations. It is possible to display an image on a screen by transferring a matrix of digits to the video memory, with every number reflecting a pixel of color.
  • Multifaceted functions: The components of a computer architecture include both software and hardware. The processor — hardware that executes computer programs — is the
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Late Architecture with Purposeful Programming

A lot of methods to computer software architecture believe that the architecture is planned at the commencing. However, architecture prepared in this way is hard to alter afterwards. Purposeful programming can aid achieve loose coupling to the place that progress setting up can be retained to a least, and architectural conclusions can be improved afterwards.

Michael Sperber spoke about computer software architecture and purposeful programming at OOP 2023 Digital.

Sperber gave the instance of dividing up the system’s code among the its developing blocks. This is a particularly essential kind of architectural decision to get the job done on different developing blocks separately, probably with different teams. Just one way to do this is to use Domain-Driven Design and style (DDD) for the coarse-grain creating blocks – bounded contexts:


DDD says you must discover bounded contexts by way of context mapping – at the commencing. Nevertheless, if you get the boundaries involving the contexts improper, you reduce a great deal of the added benefits. And you will get them improper, at the very least a little bit – and then it’s hard to move them afterwards.


According to Sperber, useful programming allows late architecture and cuts down coupling when compared to OOP. In purchase to defer macroarchitecture decisions, we will have to generally decouple, Sperber argued. Elements in practical programming are essentially just knowledge types and functions, and these features perform with out mutable condition, he claimed. This can make dependencies specific and coupling appreciably looser than with regular OO elements. This in convert permits us to create functionality that is independent of the macroarchitecture, Sperber explained.

Sperber produced clear that useful programming is not “just like OOP only devoid of mutable point out”. It arrives with its have procedures and tradition for domain modelling, abstraction, and software design. You can get some of the advantages just by adopting immutability in your OO undertaking. To get all of them, you need to dive further, and use a correct functional language, as Sperber described:


Useful architecture tends to make considerable use of superior abstraction, to put into practice reusable elements, and, much more importantly, supple area designs that foresee the future. In exploring and building these area products, functional programmers usually make use of the prosperous vocabulary furnished by arithmetic. The ensuing abstractions are essentially enabled by the state-of-the-art abstraction amenities made available by functional languages.


InfoQ interviewed Michael Sperber about how our latest toolbox of architectural procedures predisposes us to lousy decisions that are tricky to undo later on, and what to do about this trouble.

InfoQ: What are the difficulties of defining the macroarchitecture at the start off of a challenge?


Michael Sperber: A preferred definition of program architecture is that it’s the selections that are tricky to improve afterwards. Performing this at the beginning indicates carrying out it when you have the least data. For that reason, there’s a fantastic likelihood the choices are completely wrong.


InfoQ: What makes it

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NVIDIA Hopper GPU Architecture Accelerates Dynamic Programming Up to 40x Employing New DPX Instructions

The NVIDIA Hopper GPU architecture unveiled today at GTC will speed up dynamic programming — a dilemma-resolving procedure made use of in algorithms for genomics, quantum  computing, route optimization and more — by up to 40x with new DPX recommendations.

An instruction established constructed into NVIDIA H100 GPUs, DPX will aid builders compose code to accomplish speedups on dynamic programming algorithms in numerous industries, boosting workflows for disorder prognosis, quantum simulation, graph analytics and routing optimizations.

What Is Dynamic Programming? 

Developed in the 1950s, dynamic programming is a popular technique for resolving elaborate challenges with two critical procedures: recursion and memoization.

Recursion will involve breaking a problem down into less difficult sub-issues, saving time and computational hard work. In memoization, the solutions to these sub-troubles — which are reused various periods when solving the principal trouble — are saved. Memoization raises performance, so sub-challenges never require to be recomputed when desired later on in the main issue.

DPX instructions speed up dynamic programming algorithms by up to 7x on an NVIDIA H100 GPU, as opposed with NVIDIA Ampere architecture-based GPUs. In a node with four NVIDIA H100 GPUs, that acceleration can be boosted even additional.

Use Conditions Span Health care, Robotics, Quantum Computing, Data Science

Dynamic programming is usually applied in many optimization, info processing and omics algorithms. To date, most builders have operate these varieties of algorithms on CPUs or FPGAs — but can unlock spectacular speedups applying DPX recommendations on NVIDIA Hopper GPUs.


Omics addresses a vary of biological fields which includes genomics (concentrated on DNA), proteomics (centered on proteins) and transcriptomics (focused on RNA). These fields, which advise the essential do the job of disorder research and drug discovery, all count on algorithmic analyses that can be sped up with DPX instructions.

For example, the Smith-Waterman and Needleman-Wunsch dynamic programming algorithms are employed for DNA sequence alignment, protein classification and protein folding. Equally use a scoring system to measure how properly genetic sequences from distinct samples align.

Smith-Waterman creates extremely correct results, but can take a lot more compute resources and time than other alignment strategies. By working with DPX recommendations on a node with four NVIDIA H100 GPUs, scientists can pace this approach 35x to realize true-time processing, in which the function of base calling and alignment usually takes position at the identical charge as DNA sequencing.

This acceleration will enable democratize genomic assessment in hospitals throughout the world, bringing scientists nearer to offering people with personalized medication.

Route Optimization

Finding the optimum route for various relocating parts is necessary for autonomous robots relocating by a dynamic warehouse, or even a sender transferring facts to a number of receivers in a laptop or computer network.

To deal with this optimization trouble, builders depend on Floyd-Warshall, a dynamic programming algorithm made use of to find the shortest distances concerning all pairs of locations in a map or graph. In a server with four NVIDIA H100 GPUs, Floyd-Warshall acceleration is boosted 40x when compared to

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Does Tesla’s Centralized Pc Architecture Call for Fewer Chips?

This posting arrives to us courtesy of EVANNEX, which would make and sells aftermarket Tesla extras. The opinions expressed therein are not automatically our individual at InsideEVs, nor have we been compensated by EVANNEX to publish these articles. We find the firm’s standpoint as an aftermarket supplier of Tesla accessories exciting and are joyful to share its written content absolutely free of cost. Get pleasure from!

Posted on EVANNEX on December 29, 2021, by Charles Morris

Of all the improvements Tesla has introduced to the car market, not the minimum sizeable is the unified personal computer architecture utilised in its cars. This has enabled numerous of the fantastic Tesla-only functions that house owners rave about, and it is not way too substantially to say that it is been just one of the company’s greatest aggressive advantages. Now existing events are highlighting what could be another big profit. 

Above: A glimpse inside of the Tesla Design 3 (Supply: Tesla)

When researching my reserve, Tesla: How Elon Musk and Company Designed Electric Automobiles Cool, and Remade the Automotive and Electricity Industries, I was privileged to be equipped to interview Tesla co-founder Ian Wright, who offered some keen insights about Tesla’s devices strategy to its software program, and this turned out to be 1 of my favourite pieces of the e book. I have referred to it in at the very least a dozen article content, and many thanks to the ongoing semiconductor shortage, it seems like I’m likely to get some more mileage out of it.

The younger Tesla experienced roots in the Silicon Valley tech sector, and its autos ended up made with a single computer operating technique from the starting. This was the reverse of the way the legacy automakers had been (and primarily nevertheless are) undertaking matters. A common legacy car or truck has a patchwork of separate personal computers that control distinctive systems in the auto. “I’m searching out the window at my 2008 Volkswagen Touareg, and I wager that’s got sixty or seventy digital black containers, 3 hundred lbs of wiring harness, and program from twenty various firms in it,” Ian Wright explained to me in 2014.

Consultancy Roland Berger recently told Bloomberg that automakers have to have to redesign cars to use less semiconductors. Automakers are hoping that the hated chip lack will wind down before long, but Roland Berger predicts that intense bottlenecks will persist by means of 2022.

“Carmakers need to speed up the changeover to centralized electronic architectures and thereby transfer to advanced and top-edge nodes,” the analysts mentioned in a modern report. A shift to a central style with a solitary onboard laptop could considerably minimize the quantity of chips wanted in a automobile. Roland Berger claims the typical automobile contains some 1,400 individual chips.

Certainly, visitors, my interview with Ian Wright took location seven decades in the past. He informed me that the legacy automakers ended up “struggling” with the program structure in their automobiles, and that

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