Abstract - this document gives a general idea of what a CPU is, and about its design, how they are implemented, the beginning of the CPUs, a brief history of them, the problem that they presents and some new investigations.
THE CPU or Central Processing Unit, is the main component of the computer, and it the component that interpret the instructions and process data in the programs stored in the computer.
The document contains a multiple choice quiz with questions about various topics in computer science. There are 47 multiple choice questions testing knowledge about topics like binary, memory, operating systems, programming languages, networks, and security. The questions are short, with single sentences providing the prompts and possible multiple choice answers.
A machine cycle consists of four steps - fetch, decode, execute, and store - that are continuously performed by a CPU at a rate of millions per second. The fetch step involves retrieving an instruction from memory, the decode step breaks down the instruction, and the execute step carries out the requested task. Word size refers to the number of bits that a computer can handle as a single unit, which is an important characteristic that influences many aspects of computer architecture and performance. Common modern word sizes are 16, 32, and 64 bits.
There are three main types of shared memory architectures: physically shared memory, virtual shared memory, and cache-only memory access (COMA). Virtual shared memory, also called distributed shared memory, logically shares memory across processors but physically distributes it. This can cause non-uniform memory access times and requires solutions for cache coherency and data consistency. Cache-coherent non-uniform memory access (CC-NUMA) machines combine the approaches of NUMA and COMA to provide a unified memory addressing scheme while improving performance. Key challenges for shared memory architectures include scalability issues due to memory contention and latency.
The document provides an introduction to high performance computing architectures. It discusses the von Neumann architecture that has been used in computers for over 40 years. It then explains Flynn's taxonomy, which classifies parallel computers based on whether their instruction and data streams are single or multiple. The main categories are SISD, SIMD, MISD, and MIMD. It provides examples of computer architectures that fall under each classification. Finally, it discusses different parallel computer memory architectures, including shared memory, distributed memory, and hybrid models.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
This document discusses parallel programming concepts including threads, synchronization, and barriers. It defines parallel programming as carrying out many calculations simultaneously. Advantages include increased computational power and speed up. Key issues in parallel programming are sharing resources between threads, and ensuring synchronization through locks and barriers. Data parallel programming is discussed where the same operation is performed on different data elements simultaneously.
Parallel computing involves solving computational problems simultaneously using multiple processors. It can save time and money compared to serial computing and allow larger problems to be solved. Parallel programs break problems into discrete parts that can be solved concurrently on different CPUs. Shared memory parallel computers allow all processors to access a global address space, while distributed memory systems require communication between separate processor memories. Hybrid systems combine shared and distributed memory architectures.
The document discusses types of parallelism in hardware, software, and applications. It covers parallel architectures like multicore processors and clusters. Flynn's taxonomy classifies computers based on instruction and data streams as SISD, SIMD, MISD, and MIMD. Memory models include shared memory and message passing. Examples show parallelization of an equation solver kernel using instruction-level, task-level, and data-level parallelism. Speedup metrics like problem-constrained and time-constrained scaling are also introduced.
The document contains a multiple choice quiz with questions about various topics in computer science. There are 47 multiple choice questions testing knowledge about topics like binary, memory, operating systems, programming languages, networks, and security. The questions are short, with single sentences providing the prompts and possible multiple choice answers.
A machine cycle consists of four steps - fetch, decode, execute, and store - that are continuously performed by a CPU at a rate of millions per second. The fetch step involves retrieving an instruction from memory, the decode step breaks down the instruction, and the execute step carries out the requested task. Word size refers to the number of bits that a computer can handle as a single unit, which is an important characteristic that influences many aspects of computer architecture and performance. Common modern word sizes are 16, 32, and 64 bits.
There are three main types of shared memory architectures: physically shared memory, virtual shared memory, and cache-only memory access (COMA). Virtual shared memory, also called distributed shared memory, logically shares memory across processors but physically distributes it. This can cause non-uniform memory access times and requires solutions for cache coherency and data consistency. Cache-coherent non-uniform memory access (CC-NUMA) machines combine the approaches of NUMA and COMA to provide a unified memory addressing scheme while improving performance. Key challenges for shared memory architectures include scalability issues due to memory contention and latency.
The document provides an introduction to high performance computing architectures. It discusses the von Neumann architecture that has been used in computers for over 40 years. It then explains Flynn's taxonomy, which classifies parallel computers based on whether their instruction and data streams are single or multiple. The main categories are SISD, SIMD, MISD, and MIMD. It provides examples of computer architectures that fall under each classification. Finally, it discusses different parallel computer memory architectures, including shared memory, distributed memory, and hybrid models.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
This document discusses parallel programming concepts including threads, synchronization, and barriers. It defines parallel programming as carrying out many calculations simultaneously. Advantages include increased computational power and speed up. Key issues in parallel programming are sharing resources between threads, and ensuring synchronization through locks and barriers. Data parallel programming is discussed where the same operation is performed on different data elements simultaneously.
Parallel computing involves solving computational problems simultaneously using multiple processors. It can save time and money compared to serial computing and allow larger problems to be solved. Parallel programs break problems into discrete parts that can be solved concurrently on different CPUs. Shared memory parallel computers allow all processors to access a global address space, while distributed memory systems require communication between separate processor memories. Hybrid systems combine shared and distributed memory architectures.
The document discusses types of parallelism in hardware, software, and applications. It covers parallel architectures like multicore processors and clusters. Flynn's taxonomy classifies computers based on instruction and data streams as SISD, SIMD, MISD, and MIMD. Memory models include shared memory and message passing. Examples show parallelization of an equation solver kernel using instruction-level, task-level, and data-level parallelism. Speedup metrics like problem-constrained and time-constrained scaling are also introduced.
This document discusses parallel computer memory architectures, including shared memory, distributed memory, and hybrid architectures. Shared memory architectures allow all processors to access a global address space and include uniform memory access (UMA) and non-uniform memory access (NUMA). Distributed memory architectures require a communication network since each processor has its own local memory without a global address space. Hybrid architectures combine shared and distributed memory by networking multiple shared memory multiprocessors.
This document compares message passing and shared memory architectures for parallel computing. It defines message passing as processors communicating through sending and receiving messages without a global memory, while shared memory allows processors to communicate through a shared virtual address space. The key difference is that message passing uses explicit communication through messages, while shared memory uses implicit communication through memory operations. It also discusses how the programming model and hardware architecture can be separated, with message passing able to support shared memory and vice versa.
This document discusses computer architecture and microprocessors. It covers early Von Neumann architecture from 1940 and its features. It then discusses improvements with 32-bit conventional microprocessors including higher data throughput, larger addressing ranges, and faster clock speeds. Additional functions were added to microprocessors like memory management units, floating point units, and interrupt controllers. The document also covers concepts like pipelining, cache memory, memory interleaving, and parallel architectures that were developed to increase processing speeds as technology advanced.
The Harvard architecture stores instructions and data in separate physical memory units. It originated from the Harvard Mark I computer which stored instructions on punched tape and data in electromechanical counters. In the Harvard architecture, the instruction and data memories can differ in width, timing, technology and addressing structures. Modern CPU designs often use a modified Harvard architecture, where the CPU contains separate instruction and data caches to enable high performance despite slower main memory access times.
This document discusses key concepts and terminologies related to parallel computing. It defines tasks, parallel tasks, serial and parallel execution. It also describes shared memory and distributed memory architectures as well as communications and synchronization between parallel tasks. Flynn's taxonomy is introduced which classifies parallel computers based on instruction and data streams as Single Instruction Single Data (SISD), Single Instruction Multiple Data (SIMD), Multiple Instruction Single Data (MISD), and Multiple Instruction Multiple Data (MIMD). Examples are provided for each classification.
This chapter discusses shared memory architecture and classifications of shared memory systems. It describes Uniform Memory Access (UMA), Non-Uniform Memory Access (NUMA), and Cache Only Memory Architecture (COMA). It also covers basic cache coherency methods like write-through, write-back, write-invalidate, and write-update. Finally, it discusses snooping protocols and cache coherency techniques used in shared memory systems.
1. This document introduces parallel computing, which involves dividing large problems into smaller concurrent tasks that can be solved simultaneously using multiple processors to reduce computation time.
2. Parallel computing systems include single machines with multi-core CPUs and computer clusters consisting of multiple interconnected machines. Common parallel programming models involve message passing between distributed memory processors.
3. Performance of parallel programs is measured by metrics like speedup and efficiency. Factors like load balancing, serial fractions of problems, and parallel overhead affect how well a problem can scale with additional processors.
NUMA (Non-Uniform Memory Access) refers to computer system architectures where the memory access time depends on the memory location relative to the processor. It improves scalability by giving each processor node its own local memory, while still allowing access to remote memories. Existing simulators aim to model NUMA systems and analyze performance and scalability by tracking remote memory access events and task execution times. The key benefit of NUMA is that it allows memory and processors to scale independently, improving performance by reducing contention on shared memory buses.
Computer architecture and organizationRafiqIslam36
This presentation summarizes the different generations of computers. It includes:
1. An introduction to computer architecture and organization.
2. Details on the five generations of computers from the first generation of vacuum tubes to the current fifth generation of artificial intelligence.
3. Information about specific first through fourth generation computers like Babbage's Analytical Engine and the IAS computer.
4. The group member names and IDs who presented on different generations.
This document provides an introduction to parallel computing. It begins with definitions of parallel computing as using multiple compute resources simultaneously to solve problems. Popular parallel architectures include shared memory, where all processors can access a common memory, and distributed memory, where each processor has its own local memory and they communicate over a network. The document discusses key parallel computing concepts and terminology such as Flynn's taxonomy, parallel overhead, scalability, and memory models including uniform memory access (UMA), non-uniform memory access (NUMA), and distributed memory. It aims to provide background on parallel computing topics before examining how to parallelize different types of programs.
The document discusses NUMA (Non-Uniform Memory Access) architecture and optimization. With NUMA, memory is divided across multiple nodes and latency depends on memory location. Local memory has the lowest latency while remote memory has higher latency. The document provides examples of local and remote memory access and discusses how process-parallel and shared-memory threading applications are affected by NUMA. It also covers NUMA-aware operating system differences, techniques for process affinity, and NUMA optimization strategies like minimizing remote memory access.
This document discusses parallel processing concepts including:
1. Parallel computing involves simultaneously using multiple processing elements to solve problems faster than a single processor. Common parallel platforms include shared-memory and message-passing architectures.
2. Key considerations for parallel platforms include the control structure for specifying parallel tasks, communication models, and physical organization including interconnection networks.
3. Scalable design principles for parallel systems include avoiding single points of failure, pushing work away from the core, and designing for maintenance and automation. Common parallel architectures include N-wide superscalar, which can dispatch N instructions per cycle, and multi-core which places multiple cores on a single processor socket.
The document provides an overview of modern operating systems. It discusses that an operating system manages hardware resources like the CPU, memory, disks, and I/O devices and provides a simpler interface for application programmers. The key functions of an operating system are to abstract the underlying hardware and manage resources. It then covers the history of operating systems from vacuum tubes to personal computers and generations like batch processing systems, time-sharing systems, and modern graphical user interface systems. It also discusses operating system concepts like processes, memory, files, I/O, and protection and different system architectures like monolithic, layered, microkernel, and virtual machines.
The document provides an overview of processes, threads, jobs and programs in Windows XP. It discusses the process and thread architecture, including fields in the executive process and thread blocks. It also summarizes thread scheduling priorities and quanta, and registry and memory improvements in Windows XP like boot time optimizations and caching of registry keys. The document also summarizes the file system architecture, including FAT16, FAT32 and NTFS, and the master file table. It concludes with a brief overview of various inter-process communication methods in Windows XP like DDE, OLE, named pipes and Windows sockets.
Michael Flynn proposed a taxonomy in 1966 to classify computer architectures based on the number of instruction streams and data streams. The four classifications are: SISD (single instruction, single data stream), SIMD (single instruction, multiple data streams), MISD (multiple instructions, single data stream), and MIMD (multiple instructions, multiple data streams). SISD corresponds to the traditional von Neumann architecture, SIMD is used for array processing, MIMD describes most modern parallel computers, and MISD has never been implemented.
This document is the preface to a textbook on computer architecture and organization written by Ian East. It provides motivation for writing the textbook, outlines the book's philosophy and content, and maps the content to typical computer science and engineering course requirements. The preface describes the lack of a suitable existing textbook for computer science students that takes a balanced, non-machine specific approach. It outlines the book's goals of providing sufficient material for introductory courses while avoiding overwhelming details, and covering fundamental concepts, new architectures, and both software and hardware topics. The content is divided into three parts moving from software to hardware concepts to processor and system organization. Over 200 diagrams illustrate concepts and over 40 exercises with solutions are included.
Linux memory management uses a combination of physical memory, virtual memory, and paging. It uses a buddy system to allocate and free physical memory pages. Each Linux process gets 3GB of virtual address space, with the remaining 1GB reserved for page tables and kernel data. Linux uses demand paging and maintains lists of frequently and infrequently used pages. It employs a clock replacement algorithm and uses both local and global page replacement policies.
The document provides an overview of parallel processing and multiprocessor systems. It discusses Flynn's taxonomy, which classifies computers as SISD, SIMD, MISD, or MIMD based on whether they process single or multiple instructions and data in parallel. The goals of parallel processing are to reduce wall-clock time and solve larger problems. Multiprocessor topologies include uniform memory access (UMA) and non-uniform memory access (NUMA) architectures.
This document provides an overview of a chapter that will define key concepts related to computers including the Turing and von Neumann models. It describes the three main components of a computer as hardware, data, and software. The chapter will also provide a brief history of computers from mechanical machines to modern generations and establish computer science as a discipline with various areas of study. The document outlines the structure of the course which will cover topics such as data representation, computer hardware, software, and data organization.
This document discusses parallel computer memory architectures, including shared memory, distributed memory, and hybrid architectures. Shared memory architectures allow all processors to access a global address space and include uniform memory access (UMA) and non-uniform memory access (NUMA). Distributed memory architectures require a communication network since each processor has its own local memory without a global address space. Hybrid architectures combine shared and distributed memory by networking multiple shared memory multiprocessors.
This document compares message passing and shared memory architectures for parallel computing. It defines message passing as processors communicating through sending and receiving messages without a global memory, while shared memory allows processors to communicate through a shared virtual address space. The key difference is that message passing uses explicit communication through messages, while shared memory uses implicit communication through memory operations. It also discusses how the programming model and hardware architecture can be separated, with message passing able to support shared memory and vice versa.
This document discusses computer architecture and microprocessors. It covers early Von Neumann architecture from 1940 and its features. It then discusses improvements with 32-bit conventional microprocessors including higher data throughput, larger addressing ranges, and faster clock speeds. Additional functions were added to microprocessors like memory management units, floating point units, and interrupt controllers. The document also covers concepts like pipelining, cache memory, memory interleaving, and parallel architectures that were developed to increase processing speeds as technology advanced.
The Harvard architecture stores instructions and data in separate physical memory units. It originated from the Harvard Mark I computer which stored instructions on punched tape and data in electromechanical counters. In the Harvard architecture, the instruction and data memories can differ in width, timing, technology and addressing structures. Modern CPU designs often use a modified Harvard architecture, where the CPU contains separate instruction and data caches to enable high performance despite slower main memory access times.
This document discusses key concepts and terminologies related to parallel computing. It defines tasks, parallel tasks, serial and parallel execution. It also describes shared memory and distributed memory architectures as well as communications and synchronization between parallel tasks. Flynn's taxonomy is introduced which classifies parallel computers based on instruction and data streams as Single Instruction Single Data (SISD), Single Instruction Multiple Data (SIMD), Multiple Instruction Single Data (MISD), and Multiple Instruction Multiple Data (MIMD). Examples are provided for each classification.
This chapter discusses shared memory architecture and classifications of shared memory systems. It describes Uniform Memory Access (UMA), Non-Uniform Memory Access (NUMA), and Cache Only Memory Architecture (COMA). It also covers basic cache coherency methods like write-through, write-back, write-invalidate, and write-update. Finally, it discusses snooping protocols and cache coherency techniques used in shared memory systems.
1. This document introduces parallel computing, which involves dividing large problems into smaller concurrent tasks that can be solved simultaneously using multiple processors to reduce computation time.
2. Parallel computing systems include single machines with multi-core CPUs and computer clusters consisting of multiple interconnected machines. Common parallel programming models involve message passing between distributed memory processors.
3. Performance of parallel programs is measured by metrics like speedup and efficiency. Factors like load balancing, serial fractions of problems, and parallel overhead affect how well a problem can scale with additional processors.
NUMA (Non-Uniform Memory Access) refers to computer system architectures where the memory access time depends on the memory location relative to the processor. It improves scalability by giving each processor node its own local memory, while still allowing access to remote memories. Existing simulators aim to model NUMA systems and analyze performance and scalability by tracking remote memory access events and task execution times. The key benefit of NUMA is that it allows memory and processors to scale independently, improving performance by reducing contention on shared memory buses.
Computer architecture and organizationRafiqIslam36
This presentation summarizes the different generations of computers. It includes:
1. An introduction to computer architecture and organization.
2. Details on the five generations of computers from the first generation of vacuum tubes to the current fifth generation of artificial intelligence.
3. Information about specific first through fourth generation computers like Babbage's Analytical Engine and the IAS computer.
4. The group member names and IDs who presented on different generations.
This document provides an introduction to parallel computing. It begins with definitions of parallel computing as using multiple compute resources simultaneously to solve problems. Popular parallel architectures include shared memory, where all processors can access a common memory, and distributed memory, where each processor has its own local memory and they communicate over a network. The document discusses key parallel computing concepts and terminology such as Flynn's taxonomy, parallel overhead, scalability, and memory models including uniform memory access (UMA), non-uniform memory access (NUMA), and distributed memory. It aims to provide background on parallel computing topics before examining how to parallelize different types of programs.
The document discusses NUMA (Non-Uniform Memory Access) architecture and optimization. With NUMA, memory is divided across multiple nodes and latency depends on memory location. Local memory has the lowest latency while remote memory has higher latency. The document provides examples of local and remote memory access and discusses how process-parallel and shared-memory threading applications are affected by NUMA. It also covers NUMA-aware operating system differences, techniques for process affinity, and NUMA optimization strategies like minimizing remote memory access.
This document discusses parallel processing concepts including:
1. Parallel computing involves simultaneously using multiple processing elements to solve problems faster than a single processor. Common parallel platforms include shared-memory and message-passing architectures.
2. Key considerations for parallel platforms include the control structure for specifying parallel tasks, communication models, and physical organization including interconnection networks.
3. Scalable design principles for parallel systems include avoiding single points of failure, pushing work away from the core, and designing for maintenance and automation. Common parallel architectures include N-wide superscalar, which can dispatch N instructions per cycle, and multi-core which places multiple cores on a single processor socket.
The document provides an overview of modern operating systems. It discusses that an operating system manages hardware resources like the CPU, memory, disks, and I/O devices and provides a simpler interface for application programmers. The key functions of an operating system are to abstract the underlying hardware and manage resources. It then covers the history of operating systems from vacuum tubes to personal computers and generations like batch processing systems, time-sharing systems, and modern graphical user interface systems. It also discusses operating system concepts like processes, memory, files, I/O, and protection and different system architectures like monolithic, layered, microkernel, and virtual machines.
The document provides an overview of processes, threads, jobs and programs in Windows XP. It discusses the process and thread architecture, including fields in the executive process and thread blocks. It also summarizes thread scheduling priorities and quanta, and registry and memory improvements in Windows XP like boot time optimizations and caching of registry keys. The document also summarizes the file system architecture, including FAT16, FAT32 and NTFS, and the master file table. It concludes with a brief overview of various inter-process communication methods in Windows XP like DDE, OLE, named pipes and Windows sockets.
Michael Flynn proposed a taxonomy in 1966 to classify computer architectures based on the number of instruction streams and data streams. The four classifications are: SISD (single instruction, single data stream), SIMD (single instruction, multiple data streams), MISD (multiple instructions, single data stream), and MIMD (multiple instructions, multiple data streams). SISD corresponds to the traditional von Neumann architecture, SIMD is used for array processing, MIMD describes most modern parallel computers, and MISD has never been implemented.
This document is the preface to a textbook on computer architecture and organization written by Ian East. It provides motivation for writing the textbook, outlines the book's philosophy and content, and maps the content to typical computer science and engineering course requirements. The preface describes the lack of a suitable existing textbook for computer science students that takes a balanced, non-machine specific approach. It outlines the book's goals of providing sufficient material for introductory courses while avoiding overwhelming details, and covering fundamental concepts, new architectures, and both software and hardware topics. The content is divided into three parts moving from software to hardware concepts to processor and system organization. Over 200 diagrams illustrate concepts and over 40 exercises with solutions are included.
Linux memory management uses a combination of physical memory, virtual memory, and paging. It uses a buddy system to allocate and free physical memory pages. Each Linux process gets 3GB of virtual address space, with the remaining 1GB reserved for page tables and kernel data. Linux uses demand paging and maintains lists of frequently and infrequently used pages. It employs a clock replacement algorithm and uses both local and global page replacement policies.
The document provides an overview of parallel processing and multiprocessor systems. It discusses Flynn's taxonomy, which classifies computers as SISD, SIMD, MISD, or MIMD based on whether they process single or multiple instructions and data in parallel. The goals of parallel processing are to reduce wall-clock time and solve larger problems. Multiprocessor topologies include uniform memory access (UMA) and non-uniform memory access (NUMA) architectures.
This document provides an overview of a chapter that will define key concepts related to computers including the Turing and von Neumann models. It describes the three main components of a computer as hardware, data, and software. The chapter will also provide a brief history of computers from mechanical machines to modern generations and establish computer science as a discipline with various areas of study. The document outlines the structure of the course which will cover topics such as data representation, computer hardware, software, and data organization.
The document discusses the von Neumann architecture, which is still the fundamental design of modern computers. It introduced the concept of a stored program, where both program instructions and data are stored in memory. This allows programs to be changed by modifying memory contents rather than rewiring the computer. The von Neumann architecture uses four main components - memory, input/output, arithmetic logic unit, and control unit - connected by a bus. The control unit directs the fetching and executing of instructions from memory in sequential order through the other components.
This document provides an overview of parallel computing concepts. It defines parallel computing as using multiple compute resources simultaneously to solve a problem by breaking it into discrete parts that can be solved concurrently. It discusses Flynn's taxonomy for classifying computer architectures based on whether their instruction and data streams are single or multiple. Shared memory, distributed memory, and hybrid memory models are described for parallel computer architectures. Programming models like shared memory, message passing, data parallel and hybrid models are covered. Reasons for using parallel computing include saving time/money, solving larger problems, providing concurrency, and limits of serial computing.
This document provides an overview of the key topics in computer science including data representation, computer hardware, software, algorithms, programming languages, and the history of computers. It introduces the von Neumann model which defines a computer as having four main subsystems: memory, arithmetic logic unit, control unit, and input/output. The model specifies that both programs and data are stored in memory and that programs are executed sequentially through instructions. The document then discusses the evolution of computer generations from mechanical to modern electronic computers based on this von Neumann model.
Tutorial on Parallel Computing and Message Passing Model - C1Marcirio Chaves
The document provides an overview of parallel computing concepts and programming models. It discusses parallel computing terminology like Flynn's taxonomy and parallel memory architectures like shared memory, distributed memory, and hybrid models. It also explains common parallel programming models including shared memory with threads, message passing with MPI, and data parallel models.
This lecture introduces computer systems architecture and components. It discusses the Von Neumann architecture and its basic components: CPU, memory, input/output. It outlines the development of computing technology from mechanical to vacuum tube to transistor-based computers. It describes different types of computers from personal computers to mainframes and supercomputers. It explains the basic components of a computer system including the processor, memory, storage, and input/output devices.
The document summarizes the Von Neumann architecture, which was proposed by John von Neumann in 1945. It consists of four main components: a memory to store both instructions and data, a processing unit to perform arithmetic and logical operations, a control unit to interpret instructions, and a bus connecting the components. This architecture is used in most modern computers but suffers from the Von Neumann bottleneck due to the shared bus, which limits throughput between the CPU and memory. Mitigation techniques include using caches, separate memory paths for data and instructions, and on-chip scratchpad memory.
INTRODUCTION TO COMPUTER SYSTEMS ARCHITECTURE1_17 December 2023.pptMozammelHaque53
This is a lecture PowerPoint slide for the students of universities worldwide who desire to learn and advance his or her knowledge and expertise on Computer systems architecture.
This document provides an overview of a computer architecture course. It defines computer architecture and discusses its importance. The course will cover fundamental concepts, the components of Von Neumann computers, and how hardware supports programming languages. Specific topics will include macro-scale components like motherboards, micro-scale internal components of microprocessors, and large-scale architectures powering data centers. The history and evolution of the field is also summarized.
In the 17th century, Blaise Pascal, a French mathematician and philosopher, invented Pascaline.
In the late 17th century, a German mathematician called Gottfried Leibnitz invented what is known as Leibnitz’ Wheel.
The first machine that used the idea of storage and programming was the Jacquard loom, invented by Joseph-Marie Jacquard at the beginning of the 19th century.
Parallel and distributed computing allows problems to be broken into discrete parts that can be solved simultaneously. This approach utilizes multiple processors that work concurrently on different parts of the problem. There are several types of parallel architectures depending on how instructions and data are distributed across processors. Shared memory systems give all processors access to a common memory space while distributed memory assigns private memory to each processor requiring explicit data transfer. Large-scale systems may combine these approaches into hybrid designs. Distributed systems extend parallelism across a network and provide users with a single, integrated view of geographically dispersed resources and computers. Key challenges for distributed systems include transparency, scalability, fault tolerance and concurrency.
This document describes the different levels of the computer hierarchy. It begins with the user level (Level 6) and high-level language level (Level 5) where users interact. It then describes lower levels including the assembly language level (Level 4), system software level (Level 3), machine level (Level 2), control level (Level 1), and digital logic level (Level 0) where circuits are implemented. The document also explains the von Neumann model of stored-program computers and how they fetch, decode, and execute instructions in sequential order. Improvements to von Neumann models include adding additional processors for increased computational power.
This document describes the different levels of the computer hierarchy. It begins with the highest level of user interaction and progresses down to the lowest level of digital logic. Each level acts as a virtual machine that performs tasks for the level above it. The document also discusses the von Neumann model of computer architecture, which introduced the concept of storing both instructions and data in memory. It describes the fetch-decode-execute cycle that powers modern computers. The document notes improvements like adding additional processors and alternative non-von Neumann models.
The document discusses computer memory organization and hierarchy. It describes:
- Main memory as the primary storage location that directly communicates with the CPU. Main memory is typically RAM.
- Auxiliary memory as secondary storage units like magnetic disks and tapes that provide backup storage.
- Cache memory as a faster memory located between the CPU and main memory that stores frequently used contents of main memory for quicker access by the CPU.
- Virtual memory as a memory management technique that allows programs to run as if they have more memory than what is physically installed by swapping contents to auxiliary memory.
The document discusses limitations of the Von Neumann architecture and opportunities for parallel processing through non-Von Neumann architectures. It provides background on the Von Neumann model and its key components. The document then explains how increasing demand for faster computers is driving researchers to explore parallel processing approaches like SIMD and MIMD that can solve larger, more complex problems by distributing work across multiple processors. It aims to describe non-Von Neumann architectures and parallel algorithms.
operating system (OS) is a crucial software that manages computer hardware and software resources while providing common services for computer programs. Below are the key aspects of an operating system:
1. Definition and Functionality
The operating system acts as an intermediary between users and the computer hardware. Its primary functions include:
Process Management: Handles the creation, scheduling, and termination of processes. It ensures efficient execution of processes, managing CPU time and process priority.
Memory Management: Manages the computer's memory, allocating space for processes and ensuring that each process has enough memory to execute properly.
File System Management: Controls the reading and writing of data, manages files on storage devices, and organizes files into directories for easy access and storage.
Device Management: Facilitates communication between the system and peripheral devices such as printers, displays, and storage devices through device drivers.
Security and Access Control: Protects data and resources from unauthorized access and ensures that only authenticated users can access the system.
2. Types of Operating Systems
Operating systems can be classified based on their capabilities and intended use:
Batch Operating Systems: Execute batches of
The document provides an overview of key concepts in computer science, including the Turing and von Neumann models of computation. It discusses the three main components of computers as hardware, data, and software. The history of computers is summarized from mechanical machines to modern electronic computers based on stored-program concepts. Some social and ethical issues related to computers are also outlined.
The document provides an overview of key concepts in computer science, including the Turing and von Neumann models of computation. It discusses the three main components of computers as hardware, data, and software. The history of computers is summarized from mechanical machines to modern electronic computers based on stored-program concepts. Some social and ethical issues related to computers are also outlined.
Similar to New Developments in the CPU Architecture (20)
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New Developments in the CPU Architecture
1. 1
Abstract - this document gives a general idea of what a CPU is,
and about its design, how they are implemented, the beginning of
the CPUs, a brief history of them, the problem that they presents
and some new investigations.
I. INTRODUCTION
HE CPU or Central Processing Unit, is the main
component of the computer, and it the component that
interpret the instructions and process data in the programs
stored in the computer.
A. Basic CPU Design
First of all, I am going to start explaining the beginnings of
the CPU architecture for a better comprehension of the new
architectures.
A good question to start could be: How does a CPU perform
chores?
At the beginning, CPU designers constructed their
processors using logic gates to execute a set of instructions to
work on. In order to use a reasonably small number of logic
gates they had to restrict the number and complexity of the
commands that their CPUs could recognize. This small set of
commands is the CPU’s instruction set.
B. The beginning
Programs in early, before the von Neumann architecture,
were hard - wired into the circuitry.
The first advance in the computer design was the
programmable computer system, which allowed to easily
rewiring the computer system using a sequence of sockets and
plug wires. A program was a set of rows of sockets, and each
one represented one operation during the execution of the
program. With this old scheme, the number of possible
instructions was limited by the number of sockets one could
physically place on each row.
Fig 1: Patch Panel Programming1
However, CPU designers quickly discovered that with a
small amount of additional logic circuitry, they could reduce
1 CPU Architecture. Chapter 4.
http://webster.cs.ucr.edu/AoA/Linux/PDFs/CPUArchitecture.pdf
the number of sockets required considerable. They did this by
assigning a numeric code to each instruction, and then
encoding that instruction as a binary number, decreasing
considerably the time of execution of a program.
Fig 2: Patch Panel Programming. Chapter 42
This provides the first advance before the Von Neumann
Architecture that consists in the concept of a stored program.
II. THE VON NEUMANN ARCHITECTURE
The architecture in which a single instruction is fetched into
the CPU, then decoded and executed is called the von
Neumann architecture, after that John von Neumann described
it in the year 1945.
Every electronic computer has been rooted in this
architecture since virtually.
A. Brief History
The first computer built with this type of architecture was
the Manchester Mark, that ran his first program in the year
1948, executing it out of its ninety six word memory, and it
executed an instruction in one point two milliseconds (some
current computers are rated in excess of one thousand million
of instructions per second).
Fig 3: The Manchester Mark.3
2 CPU Architecture. Chapter 4.
http://webster.cs.ucr.edu/AoA/Linux/PDFs/CPUArchitecture.pdf
3 The Manchester Mark I.
http://www.histoire-informatique.org/musee/2_3_7.html
New Developments in the CPU Architecture
María–Almudena García–Fraile Fraile, Student of the North East Wales Institute, NEWI
T
2. 2
Over the years, a number of computers have been claimed to
be “non-Von Neumann”, and many have been at least partially
so. More and more emphasis is being put on the necessity of
this, in order to achieve more usable and productive systems.
B. Explanation
Firstly, it is necessary to understand at all this architecture,
in order to comprehend and appreciate what new choices must
be found.
Von Neumann describes the general-purpose computing
machine containing four main organs, which are:
The Arithmetic Unit.
The Memory Unit.
The Control Unit
The Connections between them.
To von Neumann, the key to build this device was in its
ability to store the data and the intermediate results of
computation, and the instructions that brought about the
computation also.
Therefore, why not to encode the instructions into numeric
form and store instructions and data in the same memory? This
is frequently viewed as the principal contribution provided by
the new von Neumann Architecture.
He defined the control organ as that which would
automatically execute the coded instructions stored in memory.
He said that the orders and data can reside in the same memory
“if the machine can in some fashion distinguish a number from
an order”. And yet, there is no distinction between the two in
memory.
Von Neumann was actually very interested in the design of
the arithmetic unit. The capabilities of the arithmetic unit were
limited to the performance of some arbitrary subset of the
possible arithmetic operations. He observed that there is a
compromise between the desire for speed of operation and the
desire for simplicity, and this issue continued to dominate
design decisions for many years being still now a problem.
All these concepts that von Neumann gives us, provide the
foundations for all of the early computers developed, and some
of them are still with us today.
In the year 1982, Myers defines four properties that
characterize the von Neumann architecture. The first property
of his definition is that “instructions and data are distinguished
only implicitly through usage”, the second one is that “the
memory is a single memory, sequentially addressed”, the third
property is that “the memory is one-dimensional”, and finally,
the fourth property is that “the meaning of data is not stored
with it”.
C. Von Neumann Inconsistences
These aspects present inconsistencies with the high level
languages that are used today, and it is the cause of why we
can think that it is required something different to the von
Neumann Architecture.
A problem that the Von Neumann Architecture presents is
that all the data, the locations of the data and the operations
must travel between memory and CPU a word a time, and it
involves the problem of the von Neumann bottleneck.
Some advances have been introduced to solve this problem,
for example, it were introduced some concepts, as index
registers and general purpose registers, indirect addressing,
hardware interrupts, input and output in parallel with CPU
execution, virtual memory, cache memory and the use of
multiple modules.
Spite of the numerous improvements that have been
introduced to solve it this problem persists today.
D. “Non-von Neumann” Machines
The characteristics that a “non-von Neumann machine may
present are the following ones:
McKeeman proposed the “language directed”
design, in which the instructions themselves will
determine with a set of bits if they must be
operated as an integer, real, character or other data
type. Then, the computer will only need one ADD
operation, for example, and this provides more
simplified programs in terms of the bottleneck
problem (that means more expensive hardware).
Another proposal to avoid the problem of the von
Neumann bottleneck is the use of programs that
operate on structures or conceptual units, not on
words. Functions are defined without naming data,
and these functions are combined to provide a
program. An example of one language designed to
this type of architecture is LISP.
A third proposal, tries to replace the notion of
defining computation in terms of a sequence of
discrete operations. In this architecture, the
programmer defines the order in which the
operations will be executed, and the program
counter follows this order as the control executes
the instruction.
But the most difficult task connected with adapting new
architecture is that it is hard to think about them using von
Neumann oriented minds.
III. THE PIPELINE ARCHITECTURE
This architecture divides the instructions in stages in the
execution, and while one is in its stage of execution another
one can be decoded, which is a big improvement into the
computers systems architecture.
3. 3
Fig 4: Generic 4-stage pipeline4
With the pipeline architecture, the processing speed is
decreased, but data are not processes faster. It means, that the
pipeline architecture improves the processing speed of all the
work, but not the latency for each task.
IV. MULTIPROCESSOR SYSTEMS
Parallel processing usually means that we will have more
than one processor.
There are different ways to organize the processor and the
memory, some of them are the explained following.
A. Flyn’s Taxonomy
He classifies the parallel computer architectures in terms of
the concurrent instructions and data streams available in the
architecture.
He gives four categories:
SISD: Single-instruction, single-data. A single
instruction stream is executed in a single processor
to operate on data in a single memory.
MISD: Multiple-instruction, single-data. Each
processor has its control unit and its local memory,
and each one of them operates under the control of
an instruction stream.
SIMD: Single-instruction, multiple-data. Many
simple processors, each one with its local memory,
have all of them the same single computer
instructions.
MIMD: Multiple-instruction, multiple-data.
Multiple computer instructions performs actions
simultaneously on two or more pieces of data.
B. CC-NUMA
Known as cache coherent Non-Uniform Memory Access.
4 Instruction pipeline.
www.wikipedia.org
This architecture maintains cache coherence across shared
memory. It takes place using inter-processor communication
between cache controllers.
V. MEMORY PROTECTION
A. Definition
Into a computer, the memory is the location where
information that is in use by the operating system, software
programs, hardware devices, etcetera is stored.
But having memory shared between them implies collisions,
so it is necessary to protect the memory.
B. Concurrency
It is way of computing, where many instructions are carried
out simultaneously. This implies the necessity of
communication and synchronization in order to get good
parallel program performance.
This way of computing becomes some advantages, as for
example, it decreases the time necessary to process a program
and reduce the size of the memory necessary to do it, but it
also presents some disadvantages, as complexity and higher
costs.
The architectures that support concurrent programming are:
Single Processor: only one CPU.
Several Processors: two or more CPUs within a
single computer system.
Distributed Programming: different parts of a
program run at the time on two or more computers
communicated over a network.
C. Solutions for concurrency
There are many solutions offered to the problem of the
concurrency, some of them are hardware solutions and others
are implemented in software.
Hardware solutions:
Test and Set: it is used to test and write (if the
condition allows it) a memory location as part of an
operation. Returns current data and set one.
Compare and Swap: it takes two addresses and an
integer. If the first address hold a determined
integer the swaps occurs.
Software solutions:
Semaphores: its mission is to restrict access to
shared resources.
Critical Sections: we will have critical sections
that will be controlled, in order to avoid collisions.
Monitors: its mission is to synchronize two or
more tasks that use a shared resource, locking and
unlocking determined parts of the code.
4. 4
VI. SOME NEW PROCESSORS
A. Intel Core 2 Quad
Intel Core Quad is a number of processors of Intel (2007)
with 4 cores of 64 bit. Actually are 2 Core Duo in the same
socket, and it gives us four real cores.
Fig 5: Intel Core 2 Extreme QX670005
B. AMD Quad Core Chip
This microprocessor is oriented to servers and it has eight
cores in the same piece, in order to give them more power.
Fig 6: AMD Quad Core Chip6
C. Sun presents Niagara
This microprocessor is oriented to servers and it has eight
cores in the same piece, in order to give them more power.
5 Quad Core. http://www.behardware.com/art/imprimer/642/
6 Quad Core Chip.
http://www.wired.com/techbiz/it/news/2007/09/barcelona
Fig 7: Niagara Microprocessor7
VII. THE FUTURE
It is well known that there is a limit to how many cores can
be in the same chip, so it is the reason of why processor
designers are looking for new generations of chips.
Tile is the next generation of chips design, and it consists in
a number of processor cores and a router that are connected
end to end and looking like a grip map of a city. Instructions
go along their route back and forth across the chips, and
different instructions can run in parallel simultaneously
without having to wait for one another.
Intel detailed a prototype 80-core processor made up of
tiles, but it is a prototype with no immediate plans of
developing a product with it.
Nowadays, chip makers are studying parallel computing
because having so many core processors in the same chipset
will limit its capacity.
Alan Jay Smith, from the University of California says that
“everyone’s got the same problem. They have got more real
estate on the chip than they can usefully spend on a uni-
processor, and a uni-processor runs very hot”. He thinks that
“everyone is working on parallelism because you can build it
now more effectively”. And finally he adds that “people think
in a linear way. Most programs out there are linear. Converting
the software into a parallel form where you can have
computation going on in multiple processors at once is hard”.
Development tools, compilers and programmers need to
make an effort to start programming in a parallel way. The best
way should be smart compilers that automatically divide a
linear program in several parallel threads. For example C# 3.0
has some kind of automatically parallels thread code.
But it is very complicated for compiler developers to make
automatic or single instructions to use parallelism in normal
linear program. So programmers need to upgrade from Object
Oriented Programming techniques to Parallel Programming.
Service Oriented Architecture (S.O.A) helps effectively in
this effort as every user is served by a thread allowing
effectively use of each core.
VIII. CONCLUSIONS
It is true that nowadays nearly all the computer that we
know there are based into the von Neumann Architecture and
7 Niagara Microprocessor. http://bblfish.net/blog/page6.html
5. 5
it in the last years a lot of advances were made into the subject,
in order to solve the problems that this architecture presents.
There were some attempts to introduce some “non-von
Neumann” Architectures, but finally the programs developed
into them finally ran into the von Neumann Architecture.
The last investigations and developments gives as to the run
of finding new architectures to develop the parallelism, and it
gives us some new microprocessors as the Intel Core 2 Quad,
from Intel, the Quad-Core Chip from AMD and the Niagara
from Sun Microsystems.
But all of this gives as to the obstacle of how many chips
can be in the same CPU avoiding the problems of the increase
of the temperature, the increase of the complex of their circuits
and the communications between them, and it brings us to the
new investigations, the Tiles.
Even efforts in upgrading the ability of computers to handle
CPU temperature have a limit. Air cooling improved in
previous years. Nowadays there are some computers, even
personal computers, with water cooling systems. But these
improvements in heat dissipation are reaching his limit
between cost and efficiency.
A new CPU architecture is needed, others solutions do not
solve the main problem, only extends the life of Von Neumann
architecture.
Integrating more cores into a CPU chip is a complex
engineering task that only leads to a small efficiency upgrade.
High budget on Research and Development is needed for CPU
companies in order to get small amount of improvement. The
budget should be better expended in new architectures.
To sump up, I want to add, that actually some very big
corporations have the need of very powerful CPUs for their
servers. Day after day there are a lot of CPU designers
working to solve it but always, having powerful CPUs
increases the complexity and the costs.
REFERENCES
[1] CPU Architecture. Chapter Four
http://webster.cs.ucr.edu/AoA/Linux/PDFs/CPUArchitecture.pdf
[2] AMD. Next CPU Architecture will be completely different.
http://www.custompc.co.uk/news/602511/amd-next-cpu-architecture-
will-be-completely-different.html
[3] Tile is the next hot multicore chip design.
http://pcworld.about.com/od/cpuarchitecture/Tile-is-the-next-hot-
multicore.htm
[4] CS 6220: Concurrency in Hardware.
http://www.cs.usu.edu/~jerry/Classes/6220/Notes/hardware.html
[5] Concurrency Solutions.
http://www.ayende.com/Blog/archive/2008/01/08/Concurrency-
Solutions.aspx
[6] Concurrency Control.
http://en.wikipedia.org/wiki/Concurrency_control
[7] CPU Socket
http://en.wikipedia.org/wiki/List_of_CPU_sockets
[8] List of Intel Microprocessors
http://en.wikipedia.org/wiki/List_of_Intel_microprocessors
[9] List of AMD Microprocessors
http://en.wikipedia.org/wiki/List_of_AMD_microprocessors
[10] Intel Core 2 Quad
http://es.wikipedia.org/wiki/Core_2_Quad
[11] Intel Core 2 Extreme QX6700 (Quad Core) – BeHardware by Marc
Prieur
http://www.behardware.com/art/imprimer/642/
[12] AMD Pins Hopes on Barcelona. Quad-Core Chips
http://www.wired.com/techbiz/it/news/2007/09/barcelona
[13] Sun presents Niagara
http://www.vnunet.es/Actualidad/Noticias/Infraestructuras/Hardware/20
051115023
[14] The BabelFish Blog
http://bblfish.net/blog/page6.html
[15] Welcome to Hot Chips 19
http://pcworld.about.com/gi/dynamic/offsite.htm?site=http://www.hotch
ips.org/hc19/main_page.htm
[16] Application-Customisez CPU Design by Jeffrey Brown
http://www-128.ibm.com/developerworks/power/library/pa-
fpfxbox/?ca=dgr-lnxw07XBoxDesign
[17] Processor Design. An introduction.
http://www.gamezero.com/team-
0/articles/math_magic/micro/index.html