This document discusses various models of parallel computer architectures. It begins with an overview of Flynn's taxonomy, which classifies computer systems based on the number of instruction and data streams. The main categories are SISD, SIMD, MIMD, and MISD. It then covers parallel computer models in more detail, including shared-memory multiprocessors, distributed-memory multicomputers, classifications based on interconnection networks and parallelism. It provides examples of different parallel architectures and references papers on advanced computer architecture and parallel processing.
This presentation discusses Flynn's taxonomy for classifying computer architectures. Flynn's taxonomy uses two concepts - parallelism in instruction streams and parallelism in data streams. There are four possible combinations: SISD (Single Instruction Single Data), SIMD (Single Instruction Multiple Data), MISD (Multiple Instruction Single Data), and MIMD (Multiple Instruction Multiple Data). The presentation provides examples and descriptions of each classification type.
The document discusses different types of parallel architectures including SISD, SIMD, MISD, and MIMD. SISD refers to a single instruction single data stream and includes traditional uniprocessors. SIMD uses a single instruction on multiple data streams, like in vector processors. MISD has multiple instructions on a single data stream, like systolic arrays. MIMD uses multiple instructions and data streams, including traditional multiprocessors and networks of workstations. The document explores techniques for exploiting parallelism in different architectures and trends towards superscalar designs and instruction-level parallelism. It argues future systems will require even more parallelism to continue improving performance.
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 discusses parallel processing architectures SIMD and MIMD. It defines serial processing as executing tasks sequentially on a single CPU. Parallel processing uses multiple CPUs concurrently by dividing a problem into parts. SIMD involves multiple processors executing the same instruction on different data simultaneously. MIMD uses multiple autonomous processors executing different instructions on different data, allowing more flexibility. Examples of each model are provided.
The document discusses Flynn's taxonomy for classifying computer architectures based on the number of instruction streams and data streams. It describes the four classifications: SISD (single instruction, single data), SIMD (single instruction, multiple data), MISD (multiple instruction, single data), and MIMD (multiple instruction, multiple data). SISD refers to classical von Neumann architectures that execute one instruction on individual data sequentially. SIMD executes a single instruction on multiple data values in parallel using multiple processors. MIMD allows different programs to execute different instructions on different data simultaneously using multiple processors.
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 document discusses techniques for deterministic replay of multithreaded programs. It describes how recording shared memory ordering information can enable replay that reproduces data races and concurrency bugs. Specifically, it outlines using a directory-based approach to track read-write dependencies between threads and reduce the log size through transitive reduction of dependencies.
Multiprocessors(performance and synchronization issues)Gaurav Dalvi
This document discusses performance and synchronization issues in multiprocessor systems. It describes shared memory architectures like UMA, NUMA and distributed shared memory. It discusses factors that affect cache performance like CPU count, cache size and block size. It also discusses synchronization mechanisms like locks, flags and barriers that are used to synchronize access to shared resources. Different hardware primitives for synchronization are described, including atomic exchange, test-and-set, and load-linked/store-conditional instructions.
This presentation discusses Flynn's taxonomy for classifying computer architectures. Flynn's taxonomy uses two concepts - parallelism in instruction streams and parallelism in data streams. There are four possible combinations: SISD (Single Instruction Single Data), SIMD (Single Instruction Multiple Data), MISD (Multiple Instruction Single Data), and MIMD (Multiple Instruction Multiple Data). The presentation provides examples and descriptions of each classification type.
The document discusses different types of parallel architectures including SISD, SIMD, MISD, and MIMD. SISD refers to a single instruction single data stream and includes traditional uniprocessors. SIMD uses a single instruction on multiple data streams, like in vector processors. MISD has multiple instructions on a single data stream, like systolic arrays. MIMD uses multiple instructions and data streams, including traditional multiprocessors and networks of workstations. The document explores techniques for exploiting parallelism in different architectures and trends towards superscalar designs and instruction-level parallelism. It argues future systems will require even more parallelism to continue improving performance.
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 discusses parallel processing architectures SIMD and MIMD. It defines serial processing as executing tasks sequentially on a single CPU. Parallel processing uses multiple CPUs concurrently by dividing a problem into parts. SIMD involves multiple processors executing the same instruction on different data simultaneously. MIMD uses multiple autonomous processors executing different instructions on different data, allowing more flexibility. Examples of each model are provided.
The document discusses Flynn's taxonomy for classifying computer architectures based on the number of instruction streams and data streams. It describes the four classifications: SISD (single instruction, single data), SIMD (single instruction, multiple data), MISD (multiple instruction, single data), and MIMD (multiple instruction, multiple data). SISD refers to classical von Neumann architectures that execute one instruction on individual data sequentially. SIMD executes a single instruction on multiple data values in parallel using multiple processors. MIMD allows different programs to execute different instructions on different data simultaneously using multiple processors.
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 document discusses techniques for deterministic replay of multithreaded programs. It describes how recording shared memory ordering information can enable replay that reproduces data races and concurrency bugs. Specifically, it outlines using a directory-based approach to track read-write dependencies between threads and reduce the log size through transitive reduction of dependencies.
Multiprocessors(performance and synchronization issues)Gaurav Dalvi
This document discusses performance and synchronization issues in multiprocessor systems. It describes shared memory architectures like UMA, NUMA and distributed shared memory. It discusses factors that affect cache performance like CPU count, cache size and block size. It also discusses synchronization mechanisms like locks, flags and barriers that are used to synchronize access to shared resources. Different hardware primitives for synchronization are described, including atomic exchange, test-and-set, and load-linked/store-conditional instructions.
This document discusses vector processing and multiprocessor principles. It explains that vector processing performs operations on vectors to gain speedups of 10-20x over scalar processing. Multiprocessor systems use two or more CPUs for advantages like reduced costs, increased reliability and throughput. They can implement techniques like multitasking, multithreading and multiprogramming to execute multiple tasks simultaneously.
Multithreading allows exploiting thread-level parallelism (TLP) to improve processor utilization. There are several categories of multithreading:
- Superscalar simultaneous multithreading interleaves instructions from multiple threads within a single out-of-order processor core to reduce idle resources.
- Coarse-grained multithreading switches between threads on long-latency events like cache misses to hide latency.
- Fine-grained multithreading interleaves threads at a finer instruction granularity in in-order cores.
- Multiprocessing physically separates threads onto multiple processor cores.
This document discusses parallel processing and the evolution of computer systems. It covers several topics:
- The evolution of computer systems from vacuum tubes to integrated circuits, organized into generations.
- Concepts of parallel processing including Flynn's classification of computer architectures based on instruction and data streams.
- Parallel processing mechanisms in uniprocessor systems including pipelining and memory hierarchies.
- Three classes of parallel computer structures: pipeline computers, array processors, and multiprocessor systems.
- Architectural classification schemes including Flynn's, Feng's based on serial vs parallel processing, and Handler's based on parallelism levels.
This document discusses multiprocessor and multicomputer systems. It defines a multiprocessor system as having more than one processor that shares common memory, while a multicomputer has more than one processor each with local memory. Processors may be closely coupled on a shared bus or loosely coupled distributed on a network. The document also covers Flynn's taxonomy of computer architectures and examples of single instruction single data stream (SISD), single instruction multiple data stream (SIMD), multiple instruction single data stream (MISD), and multiple instruction multiple data stream (MIMD) systems.
Multi-threaded processor architectures can improve parallelism at both the instruction-level and thread-level. Simultaneous multi-threading (SMT) allows multiple threads to issue and execute instructions simultaneously by dynamically sharing processor resources. SMT reduces underutilization of functional units and improves performance over multiprocessors. Multi-threaded designs are well-suited for digital signal processing applications that can benefit from parallel execution at multiple levels. Examples of multi-threaded real-time operating systems that support parallel DSP applications are discussed.
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 discusses different types of multistage networks that can connect multiple inputs to multiple outputs. It describes multistage networks, blocking networks, and nonblocking networks. It then focuses on the multistage Omega network, providing details on its structure, routing approach, and limitations. The document also discusses hypercube interconnection networks and their properties. Finally, it covers cache coherence in multiprocessor systems, describing update and invalidate protocols as well as snoopy cache and directory-based approaches to maintaining coherence.
This document discusses multiple processor systems including multiprocessors, multicomputers, and distributed systems. It covers topics such as multiprocessor hardware architectures, operating systems, scheduling, synchronization, and communication in these systems. It also discusses distributed system middleware including document-based systems like the web, file system-based systems like AFS, shared object systems like CORBA and Globe, and coordination-based systems like Linda and Jini.
The document discusses multithreading and how it can be used to exploit thread-level parallelism (TLP) in processors designed for instruction-level parallelism (ILP). There are two main approaches for multithreading - fine-grained and coarse-grained. Fine-grained switches threads every instruction while coarse-grained switches on long stalls. Simultaneous multithreading (SMT) allows a processor to issue instructions from multiple threads in the same cycle by treating instructions from different threads as independent. This converts TLP into additional ILP to better utilize the resources of superscalar and multicore processors.
This document discusses multiple processor systems including shared-memory multiprocessors, message-passing multicomputers, and wide area distributed systems. It describes different multiprocessor architectures like UMA and NUMA and challenges like heat dissipation. It also covers topics like multiprocessing operating systems, synchronization, scheduling, and communication in multicomputer systems.
The document discusses different types of parallel computer architectures, including shared-memory multiprocessors. It describes taxonomy of parallel computers including SISD, SIMD, MISD, and MIMD models. For shared-memory multiprocessors, it outlines consistency models including strict, sequential, processor, weak and release consistency. It also discusses UMA and NUMA architectures, cache coherence protocols like MESI, and examples of multiprocessors using crossbar switches or multistage networks.
In this presentation, you will learn the fundamentals of Multi Processors and Multi Computers in only a few minutes.
Meanings, features, attributes, applications, and examples of multiprocessors and multi computers.
So, let's get started. If you enjoy this and find the information beneficial, please like and share it with your friends.
The document discusses the evolution of thread implementations in Linux, including the original LinuxThreads implementation which used a 1:1 threading model with each thread as a separate process and its limitations. It then describes the design and goals of the Native POSIX Threads Library (NPTL) implementation which uses a 1:1 model mapping threads to lightweight processes managed by the kernel to improve performance and scalability while maintaining POSIX compliance. Key threading functions like pthread_create and pthread_join are also outlined.
The document discusses multiple processor organizations including:
- SISD (single instruction, single data stream) using a single processor.
- SIMD (single instruction, multiple data stream) using multiple processors executing the same instruction on different data simultaneously.
- MISD (multiple instruction, single data stream) transmitting data to multiple processors each executing different instructions.
- MIMD (multiple instruction, multiple data stream) using a set of processors executing different instruction sequences on different data sets like SMPs, clusters and NUMA systems.
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.
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.
This document discusses different types of multiple processor systems including multiprocessors, multicomputers, and distributed systems. It covers topics such as multiprocessor hardware architectures, operating systems, scheduling, communication software, remote procedure calls, distributed shared memory, and middleware for coordination between distributed systems.
The document provides an introduction to parallel programming concepts. It discusses Flynn's taxonomy of parallel systems including SISD, SIMD, MIMD, and MISD models. It then gives examples of adding two vectors in parallel using different models like SISD, SIMD, SIMD-SIMT, MIMD-MPMD, and MIMD-SPMD. It also covers parallel programming concepts like synchronization, computational patterns like map, reduce, and pipeline, and data usage patterns like gather, scatter, subdivide and pack. Finally, it provides an overview of CUDA hardware including its memory model, execution model using kernels, blocks, threads and warps. It highlights some caveats of CUDA like data
This document discusses vector processing and multiprocessor principles. It explains that vector processing performs operations on vectors to gain speedups of 10-20x over scalar processing. Multiprocessor systems use two or more CPUs for advantages like reduced costs, increased reliability and throughput. They can implement techniques like multitasking, multithreading and multiprogramming to execute multiple tasks simultaneously.
Multithreading allows exploiting thread-level parallelism (TLP) to improve processor utilization. There are several categories of multithreading:
- Superscalar simultaneous multithreading interleaves instructions from multiple threads within a single out-of-order processor core to reduce idle resources.
- Coarse-grained multithreading switches between threads on long-latency events like cache misses to hide latency.
- Fine-grained multithreading interleaves threads at a finer instruction granularity in in-order cores.
- Multiprocessing physically separates threads onto multiple processor cores.
This document discusses parallel processing and the evolution of computer systems. It covers several topics:
- The evolution of computer systems from vacuum tubes to integrated circuits, organized into generations.
- Concepts of parallel processing including Flynn's classification of computer architectures based on instruction and data streams.
- Parallel processing mechanisms in uniprocessor systems including pipelining and memory hierarchies.
- Three classes of parallel computer structures: pipeline computers, array processors, and multiprocessor systems.
- Architectural classification schemes including Flynn's, Feng's based on serial vs parallel processing, and Handler's based on parallelism levels.
This document discusses multiprocessor and multicomputer systems. It defines a multiprocessor system as having more than one processor that shares common memory, while a multicomputer has more than one processor each with local memory. Processors may be closely coupled on a shared bus or loosely coupled distributed on a network. The document also covers Flynn's taxonomy of computer architectures and examples of single instruction single data stream (SISD), single instruction multiple data stream (SIMD), multiple instruction single data stream (MISD), and multiple instruction multiple data stream (MIMD) systems.
Multi-threaded processor architectures can improve parallelism at both the instruction-level and thread-level. Simultaneous multi-threading (SMT) allows multiple threads to issue and execute instructions simultaneously by dynamically sharing processor resources. SMT reduces underutilization of functional units and improves performance over multiprocessors. Multi-threaded designs are well-suited for digital signal processing applications that can benefit from parallel execution at multiple levels. Examples of multi-threaded real-time operating systems that support parallel DSP applications are discussed.
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 discusses different types of multistage networks that can connect multiple inputs to multiple outputs. It describes multistage networks, blocking networks, and nonblocking networks. It then focuses on the multistage Omega network, providing details on its structure, routing approach, and limitations. The document also discusses hypercube interconnection networks and their properties. Finally, it covers cache coherence in multiprocessor systems, describing update and invalidate protocols as well as snoopy cache and directory-based approaches to maintaining coherence.
This document discusses multiple processor systems including multiprocessors, multicomputers, and distributed systems. It covers topics such as multiprocessor hardware architectures, operating systems, scheduling, synchronization, and communication in these systems. It also discusses distributed system middleware including document-based systems like the web, file system-based systems like AFS, shared object systems like CORBA and Globe, and coordination-based systems like Linda and Jini.
The document discusses multithreading and how it can be used to exploit thread-level parallelism (TLP) in processors designed for instruction-level parallelism (ILP). There are two main approaches for multithreading - fine-grained and coarse-grained. Fine-grained switches threads every instruction while coarse-grained switches on long stalls. Simultaneous multithreading (SMT) allows a processor to issue instructions from multiple threads in the same cycle by treating instructions from different threads as independent. This converts TLP into additional ILP to better utilize the resources of superscalar and multicore processors.
This document discusses multiple processor systems including shared-memory multiprocessors, message-passing multicomputers, and wide area distributed systems. It describes different multiprocessor architectures like UMA and NUMA and challenges like heat dissipation. It also covers topics like multiprocessing operating systems, synchronization, scheduling, and communication in multicomputer systems.
The document discusses different types of parallel computer architectures, including shared-memory multiprocessors. It describes taxonomy of parallel computers including SISD, SIMD, MISD, and MIMD models. For shared-memory multiprocessors, it outlines consistency models including strict, sequential, processor, weak and release consistency. It also discusses UMA and NUMA architectures, cache coherence protocols like MESI, and examples of multiprocessors using crossbar switches or multistage networks.
In this presentation, you will learn the fundamentals of Multi Processors and Multi Computers in only a few minutes.
Meanings, features, attributes, applications, and examples of multiprocessors and multi computers.
So, let's get started. If you enjoy this and find the information beneficial, please like and share it with your friends.
The document discusses the evolution of thread implementations in Linux, including the original LinuxThreads implementation which used a 1:1 threading model with each thread as a separate process and its limitations. It then describes the design and goals of the Native POSIX Threads Library (NPTL) implementation which uses a 1:1 model mapping threads to lightweight processes managed by the kernel to improve performance and scalability while maintaining POSIX compliance. Key threading functions like pthread_create and pthread_join are also outlined.
The document discusses multiple processor organizations including:
- SISD (single instruction, single data stream) using a single processor.
- SIMD (single instruction, multiple data stream) using multiple processors executing the same instruction on different data simultaneously.
- MISD (multiple instruction, single data stream) transmitting data to multiple processors each executing different instructions.
- MIMD (multiple instruction, multiple data stream) using a set of processors executing different instruction sequences on different data sets like SMPs, clusters and NUMA systems.
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.
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.
This document discusses different types of multiple processor systems including multiprocessors, multicomputers, and distributed systems. It covers topics such as multiprocessor hardware architectures, operating systems, scheduling, communication software, remote procedure calls, distributed shared memory, and middleware for coordination between distributed systems.
The document provides an introduction to parallel programming concepts. It discusses Flynn's taxonomy of parallel systems including SISD, SIMD, MIMD, and MISD models. It then gives examples of adding two vectors in parallel using different models like SISD, SIMD, SIMD-SIMT, MIMD-MPMD, and MIMD-SPMD. It also covers parallel programming concepts like synchronization, computational patterns like map, reduce, and pipeline, and data usage patterns like gather, scatter, subdivide and pack. Finally, it provides an overview of CUDA hardware including its memory model, execution model using kernels, blocks, threads and warps. It highlights some caveats of CUDA like data
A Look Back | A Look Ahead Seattle Foundation ServicesSeattle Foundation
The document outlines various philanthropic services provided by Seattle Foundation, including lifetime philanthropy through donor advised funds and family foundations, legacy philanthropy through bequests and planned giving, organizational philanthropy through corporate foundations and agency endowments, and specialized services like global giving and impact investing. It provides details on each type of service and how donors can engage to accomplish their personal, financial, and philanthropic goals through principled oversight of charitable assets.
Computers when invented by Charles Babbage only viewed it as a computing machines. However it is only recently that computer has evolved more rapidly. Through its complex systems and processing capabilities computers can be used to manipulate databases.
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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.
Interconnection Network
in this presentation there are some explain to Interconnection Network , and espically in computer architecture and parallel processing.
Vector processing involves executing the same operation on multiple data elements simultaneously using a single instruction. Early implementations like the CDC Cyber 100 had limitations. The Cray-1 was the first successful vector processing supercomputer, using vector registers to perform calculations faster than requiring memory access. Seymour Cray led the development of vector processing machines that dominated the field for many years. While vector processing is no longer a focus, its principles are still used today in multimedia SIMD instructions.
Ccn unit-2- data link layer by prof.suresha vSURESHA V
The document discusses the data link layer of the OSI model. It covers topics such as:
- Framing methods including character-oriented, bit-oriented, and byte stuffing
- Flow control and error control functions of the data link layer
- Common data link layer protocols including simplex and stop-and-wait
Interstage buffer B1 feeds the Decode stage with a newly-fetched instruction.
Interstage buffer B2 feeds the Compute stage with the two operands
Interstage buffer B3 holds the result of the ALU operation
Interstage buffer B4 feeds the Write stage with a value to be written into the register file
This document discusses instruction-level parallelism (ILP), which refers to executing multiple instructions simultaneously in a program. It describes different types of parallel instructions that do not depend on each other, such as at the bit, instruction, loop, and thread levels. The document provides an example to illustrate ILP and explains that compilers and processors aim to maximize ILP. It outlines several ILP techniques used in microarchitecture, including instruction pipelining, superscalar, out-of-order execution, register renaming, speculative execution, and branch prediction. Pipelining and superscalar processing are explained in more detail.
The document discusses parallel algorithms and their analysis. It introduces a simple parallel algorithm for adding n numbers using log n steps. Parallel algorithms are analyzed based on their time complexity, processor complexity, and work complexity. For adding n numbers in parallel, the time complexity is O(log n), processor complexity is O(n), and work complexity is O(n log n). The document also discusses models of parallel computation like PRAM and designs of parallel architectures like meshes and hypercubes.
The document discusses parallel algorithms and parallel computing. It begins by defining parallelism in computers as performing more than one task at the same time. Examples of parallelism include I/O chips and pipelining of instructions. Common terms for parallelism are defined, including concurrent processing, distributed processing, and parallel processing. Issues in parallel programming such as task decomposition and synchronization are outlined. Performance issues like scalability and load balancing are also discussed. Different types of parallel machines and their classification are described.
This document discusses instruction level parallelism (ILP) and how it can be used to improve performance by overlapping the execution of instructions through pipelining. ILP refers to the potential overlap among instructions within a basic block. Factors like dynamic branch prediction and compiler dependence analysis can impact the ideal pipeline CPI and number of data hazard stalls. Loop level parallelism refers to the parallelism available across iterations of a loop. Data dependencies between instructions, if not properly handled, can limit parallelism and require instructions to execute in order. The three types of data dependencies are data, name, and control dependencies.
This document discusses instruction pipelining as a technique to improve computer performance. It explains that pipelining allows multiple instructions to be processed simultaneously by splitting instruction execution into stages like fetch, decode, execute, and write. While pipelining does not reduce the time to complete individual instructions, it improves throughput by allowing new instructions to begin processing before previous instructions have finished. The document outlines some challenges to achieving peak performance from pipelining, such as pipeline stalls from hazards like data dependencies between instructions. It provides examples of how data hazards can occur if the results of one instruction are needed by a subsequent instruction before they are available.
Pipelining is an speed up technique where multiple instructions are overlapped in execution on a processor. It is an important topic in Computer Architecture.
This slide try to relate the problem with real life scenario for easily understanding the concept and show the major inner mechanism.
Parallel algorithms can increase throughput by using multiple processing units to perform independent tasks simultaneously. However, parallelization also introduces limitations. Amdahl's law dictates that speedup from parallelization is limited by the fraction of the algorithm that must execute sequentially. Complexity in designing, implementing, and maintaining parallel programs can outweigh performance benefits for some problems. Other challenges include data dependencies, portability across systems, scalability to larger problem and system sizes, and potential for parallel slowdown rather than speedup.
Parallel algorithms can be specifically written to execute on computers with multiple processors. They are often modeled using parallel random-access machines (PRAM) which allow for an unlimited number of processors that can access shared memory uniformly. Common parallel algorithms include matrix multiplication, merge sort, and shortest path algorithms like Floyd's algorithm.
Flynn's taxonomy classifies computer architectures based on the number of instruction and data streams. The main categories are:
1) SISD - Single instruction, single data stream (von Neumann architecture)
2) SIMD - Single instruction, multiple data streams (vector/MMX processors)
3) MIMD - Multiple instruction, multiple data streams (most multiprocessors including multi-core)
Multiprocessor architectures can be organized as shared memory (SMP/UMA) or distributed memory (message passing/DSM). Shared memory allows automatic sharing but can have memory contention issues, while distributed memory requires explicit communication but scales better. Achieving high parallel performance depends on minimizing sequential
This document summarizes a seminar on parallel computing. It defines parallel computing as performing multiple calculations simultaneously rather than consecutively. A parallel computer is described as a large collection of processing elements that can communicate and cooperate to solve problems fast. The document then discusses parallel architectures like shared memory, distributed memory, and shared distributed memory. It compares parallel computing to distributed computing and cluster computing. Finally, it discusses challenges in parallel computing like power constraints and programmability and provides examples of parallel applications like GPU processing and remote sensing.
1) Early parallel architectures included the mainframe approach using a crossbar interconnect and the minicomputer approach using a shared bus. (2) Modern architectures have converged on a distributed memory model connected by a general-purpose network. (3) Programming models have also converged but hardware organizations remain flexible to support different approaches like message passing, shared memory, data parallel and systolic.
BIL406-Chapter-2-Classifications of Parallel Systems.pptKadri20
This document discusses various classifications of parallel computer systems. It describes:
1. Flynn's taxonomy which divides systems into SISD, SIMD, MISD and MIMD based on their processing structure and instruction streams. SISD refers to traditional CPUs while MIMD allows for multiple independent instruction streams.
2. Examples of parallel architectures like the Cray-1 supercomputer, Connection Machine, and Transputers. The Cray-1 used vector processing to perform operations in parallel while Connection Machine had thousands of simple processors.
3. Different levels of parallelism from bit-level to instruction-level to job-level, with varying granularity of computation. Finer grain allows more
Unit IV discusses parallelism and parallel processing architectures. It introduces Flynn's classifications of parallel systems as SISD, MIMD, SIMD, and SPMD. Hardware approaches to parallelism include multicore processors, shared memory multiprocessors, and message-passing systems like clusters, GPUs, and warehouse-scale computers. The goals of parallelism are to increase computational speed and throughput by processing data concurrently across multiple processors.
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.
Computer system Architecture. This PPT is based on computer systemmohantysikun0
This document discusses thread and process-level parallelism. It begins by introducing how improvements to computer performance initially came from manufacturing techniques and exploitation of instruction-level parallelism (ILP), but that ILP is now fully exploited. It states that the way to achieve higher performance now is through exploiting parallelism across multiple processes or threads. It provides examples of how individual transactions in a banking application could be executed in parallel.
An operating system provides an interface between users and computer hardware. It implements a virtual machine that is easier and safer to program than raw hardware. The key functions of an operating system include resource management, coordination of applications and users, and providing protection between processes. Operating systems abstract away hardware complexity and provide standard services to applications through virtualization layers. They juggle resources to provide the illusion of dedicated hardware and protect users from each other through mechanisms like address translation and dual mode operation.
This document discusses multiprocessor systems. It begins by explaining the reasons for using multiprocessors, including improving performance by using multiple CPUs. It then describes different types of multiprocessor symmetry and architectures, such as symmetric multiprocessing (SMP) and non-uniform memory access (NUMA). The document also discusses instruction and data streams, processor coupling in tightly-coupled and loosely-coupled systems, and communication architectures like message passing and shared memory. Finally, examples of multiprocessor systems like the HP Superdome are provided.
This document provides an introduction to high performance computer architecture and multiprocessors. It discusses how initial improvements in computer performance came from innovative manufacturing techniques and exploitation of instruction level parallelism (ILP). More recently, exploiting thread and process level parallelism across multiple processors has become a focus. The key types of multiprocessor architectures discussed are symmetric multiprocessors (SMPs) and distributed memory computers which use message passing. SMPs connect multiple processors to a shared memory using a bus, while distributed memory computers require explicit message passing between separate processor memories.
This document provides an overview of distributed systems and distributed computing. It defines a distributed system as a collection of independent computers that appears as a single coherent system. It discusses the advantages and goals of distributed systems, including connecting users and resources, transparency, openness and scalability. It also covers hardware concepts like multi-processor systems with shared or non-shared memory, and multi-computer systems that can be homogeneous or heterogeneous.
This document provides an introduction and agenda for a course on parallelism and code optimization in C/C++ and Fortran for large data analysis using MPI. The course covers topics like processor architectures, vectorization, OpenMP programming, and data exchange rules and principles. Parallel programming approaches covered include shared memory, distributed memory, and hybrid memory models. Shared memory is the most common parallelization approach, where multiple processors can access and exchange data through a common memory. Distributed memory uses message passing between processors with separate memory. The course aims to help with the growing use of HPC, big data, and deep learning by exploring how to leverage HPC technologies to improve bottlenecks in big data processing and deep learning middleware.
This document discusses parallel architecture and parallel programming. It begins with an introduction to von Neumann architecture and serial computation. Then it defines parallel architecture, outlines its benefits, and describes classifications of parallel processors including multiprocessor architectures. It also discusses parallel programming models, how to design parallel programs, and examples of parallel algorithms. Specific topics covered include shared memory and distributed memory architectures, message passing and data parallel programming models, domain and functional decomposition techniques, and a case study on developing parallel web applications using Java threads and mobile agents.
Parallel computing involves using multiple processing units simultaneously to solve computational problems. It can save time by solving large problems or providing concurrency. The basic design involves memory storing program instructions and data, and a CPU fetching instructions from memory and sequentially performing them. Flynn's taxonomy classifies computer systems based on their instruction and data streams as SISD, SIMD, MISD, or MIMD. Parallel architectures can also be classified based on their memory arrangement as shared memory or distributed memory systems.
The document provides an introduction and overview of parallel computing. It discusses parallel computing systems and parallel programming models like MPI and OpenMP. It covers theoretical concepts like Amdahl's law and practical limits of parallel computing related to load balancing and non-computational sections. Examples of parallel programming using MPI and OpenMP are also presented.
This document discusses parallel architecture and parallel programming. It begins by introducing the traditional von Neumann architecture and serial computation model. It then defines parallel architecture, noting its use of multiple processors to solve problems concurrently by breaking work into discrete parts that can execute simultaneously. Key concepts in parallel programming models are also introduced, including shared memory, message passing, and data parallelism. The document outlines approaches for designing parallel programs, such as automatic and manual parallelization, as well as domain and functional decomposition. It concludes by mentioning examples of parallel algorithms and case studies in parallel application development using Java mobile agents and threads.
Flynn's classification categorizes computer architectures based on the number of instruction and data streams. There are four categories: SISD, SIMD, MISD, and MIMD. SISD refers to a single instruction single data architecture, like a typical CPU. SIMD uses a single instruction on multiple data streams, like GPUs. MISD uses multiple instructions on a single data stream. MIMD uses multiple instructions and data streams, like modern multiprocessor systems.
This document discusses the architecture of parallel computers. It covers hardware issues like the number and type of processors, memory hierarchy, and I/O devices. It also discusses operating system issues in managing resources and supporting hardware features. Programming issues are discussed, like difficulty in programming parallel computers. Flynn's classification of computer architectures is presented, including SISD, SIMD, MIMD, and MISD models. Different types of parallel computers are described such as multi-processors, multi-computers, vector computers, and SIMD computers.
Implementing ELDs or Electronic Logging Devices is slowly but surely becoming the norm in fleet management. Why? Well, integrating ELDs and associated connected vehicle solutions like fleet tracking devices lets businesses and their in-house fleet managers reap several benefits. Check out the post below to learn more.
Dahua provides a comprehensive guide on how to install their security camera systems. Learn about the different types of cameras and system components, as well as the installation process.
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Charging Fueling & Infrastructure (CFI) Program by Kevin MillerForth
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Welcome to ASP Cranes, your trusted partner for crane solutions in Raipur, Chhattisgarh! With years of experience and a commitment to excellence, we offer a comprehensive range of crane services tailored to meet your lifting and material handling needs.
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Understanding Catalytic Converter Theft:
What is a Catalytic Converter?: Learn about the function of catalytic converters in vehicles and why they are targeted by thieves.
Why are They Stolen?: Discover the valuable metals inside catalytic converters (such as platinum, palladium, and rhodium) that make them attractive to criminals.
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Parking Strategies: Tips on where and how to park your vehicle to reduce the risk of theft, such as parking in well-lit areas or secure garages.
Protective Devices: Overview of various anti-theft devices available, including catalytic converter locks, shields, and alarms.
Etching and Marking: The benefits of etching your vehicle’s VIN on the catalytic converter or using a catalytic converter marking kit to make it traceable and less appealing to thieves.
Surveillance and Monitoring: Recommendations for using security cameras and motion-sensor lights to deter thieves.
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Theft Rates by Borough: Analysis of data to determine which borough in NYC experiences the highest rate of catalytic converter thefts.
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Charging Fueling & Infrastructure (CFI) Program Resources by Cat PleinForth
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2. Overview
• Flynn’s taxonomy
• Classification based on the memory arrangement
• Classification based on communication
• Classification based on the kind of parallelism
– Data-parallel
– Function-parallel
2
3. Flynn’s Taxonomy
– The most universally excepted method of classifying computer
systems
– Published in the Proceedings of the IEEE in 1966
– Any computer can be placed in one of 4 broad categories
» SISD: Single instruction stream, single data stream
» SIMD: Single instruction stream, multiple data streams
» MIMD: Multiple instruction streams, multiple data streams
» MISD: Multiple instruction streams, single data stream
3
4. SISD
Instructions
Processing Main memory
element (PE) (M)
Data
IS
IS DS
Control Unit PE Memory
4
6. SIMD Architectures
• Fine-grained
– Image processing application
– Large number of PEs
– Minimum complexity PEs
– Programming language is a simple extension of a sequential
language
• Coarse-grained
– Each PE is of higher complexity and it is usually built with
commercial devices
– Each PE has local memory
6
9. Flynn taxonomy
– Advantages of Flynn
» Universally accepted
» Compact Notation
» Easy to classify a system (?)
– Disadvantages of Flynn
» Very coarse-grain differentiation among machine
systems
» Comparison of different systems is limited
» Interconnections, I/O, memory not considered in the
scheme
9
11. Shared-memory multiprocessors
• Uniform Memory Access (UMA)
• Non-Uniform Memory Access (NUMA)
• Cache-only Memory Architecture (COMA)
• Memory is common to all the processors.
• Processors easily communicate by means of shared
variables.
11
12. The UMA Model
• Tightly-coupled systems (high degree of resource
sharing)
• Suitable for general-purpose and time-sharing
applications by multiple users.
P1 Pn
$ $
Interconnection network
Mem Mem
12
13. Symmetric and asymmetric multiprocessors
• Symmetric:
- all processors have equal access to all peripheral
devices.
- all processors are identical.
• Asymmetric:
- one processor (master) executes the operating system
- other processors may be of different types and may be
dedicated to special tasks.
13
14. The NUMA Model
• The access time varies with the location of the memory
word.
• Shared memory is distributed to local memories.
• All local memories form a global address space
accessible by all processors
Access time: Cache, Local memory, Remote memory
COMA - Cache-only Memory Architecture
P1 Pn
$ $
Mem Mem
Interconnection network
Distributed Memory (NUMA)
14
15. Distributed memory multicomputers
• Multiple computers- nodes
• Message-passing network
• Local memories are private with its
own program and data M M M
• No memory contention so that the PE PE PE
number of processors is very large
• The processors are connected by
Interconnection
communication lines, and the precise network
way in which the lines are connected
is called the topology of the
multicomputer. PE PE PE
• A typical program consists of M M M
subtasks residing in all the
memories.
15
17. Interconnection Network [1]
• Mode of Operation (Synchronous vs. Asynchronous)
• Control Strategy (Centralized vs. Decentralized)
• Switching Techniques (Packet switching vs. Circuit
switching)
• Topology (Static Vs. Dynamic)
17
18. Classification based on the kind of
parallelism[3]
Parallel
architectures
PAs
Data-parallel architectures Function-parallel architectures
Instruction-level Thread-level Process-level
PAs
PAs PAs
DPs
ILPS MIMDs
Vector Associative SIMDs Systolic Pipelined VLIWs Superscalar Ditributed Shared
and neural architecture processors processors memory memory
architecture architecture MIMD (multi-
(multi-computer) Processors)
18
19. References
• Advanced Computer Architecture and Parallel
Processing, by Hesham El-Rewini and Mostafa Abd-El-
Barr, John Wiley and Sons, 2005.
• Advanced Computer Architecture Parallelism,
Scalability, Programmability, by K. Hwang, McGraw-Hill
1993.
• Advanced Computer Architectures – A Design Space
Approach by Desco Sima, Terence Fountain and Peter
Kascuk, Pearson, 1997.
19
20. Speedup
• S = Speed(new) / Speed(old)
• S = Work/time(new) / Work/time(old)
• S = time(old) / time(new)
• S = time(before improvement) /
time(after improvement)
20
21. Speedup
• Time (one CPU): T(1)
• Time (n CPUs): T(n)
• Speedup: S
• S = T(1)/T(n)
21
22. Amdahl’s Law
The performance improvement to be gained from using
some faster mode of execution is limited by the fraction
of the time the faster mode can be used
22
23. Example
20 hours
A B
must walk 200 miles
Walk 4 miles /hour
Bike 10 miles / hour
Car-1 50 miles / hour
Car-2 120 miles / hour
Car-3 600 miles /hour
23
24. Example
20 hours
A B
must walk 200 miles
Walk 4 miles /hour 50 + 20 = 70 hours S=1
Bike 10 miles / hour 20 + 20 = 40 hours S = 1.8
Car-1 50 miles / hour 4 + 20 = 24 hours S = 2.9
Car-2 120 miles / hour 1.67 + 20 = 21.67 hours S = 3.2
Car-3 600 miles /hour 0.33 + 20 = 20.33 hours S = 3.4
24
25. Amdahl’s Law (1967)
∀ β : The fraction of the program that is naturally serial
• (1- β): The fraction of the program that is naturally
parallel
25
26. S = T(1)/T(N)
T(1)(1- β )
T(N) = T(1)β +
N
1 N
S=
β+ (1- β ) =
βN + (1- β )
N
26
Two types of information flow into a processor: instructions and data. The instruction stream is defined as the sequence of instructions performed by the processing unit. The data stream is defined as the data traffic exchanged between the memory and the processing unit. According to Flynn’s classification, either of the instruction or data streams can be single or multiple. Comparisson with car assembly: SISD – one person is doing all the tasks one at the time MISD – one worker continues the work of the previous worker SIMD – several workers perform the same task concurrently; after all the workers are finished, another taks is given ot them MIMD – each worker constructs a car independently following his own set of instructions
A processing elements is capable to process a instruction passed by another entity, where a memory can be used to hold computational values. The first figure demonstrate the interaction between a processing element and its memory module. A single instruction, single data architecture is represented in the second figure. The Control Unit will provide a instruction to the processing element and the memory module will serve as mention above. Another function here of the memory module is that its capable store information provided by the processing element and provide a instruction to the Control Unit.
This architecture is capable to run with a boost of speedup compared to a sequential architectures. Since all processors are running at the same time, there a existence of certain processors waiting for others processors to finish running a specific instructions. The following example shows the same instruction running on two different processors. ----------------- | ----------------- PROCESSOR 1 | PROCESSOR 2 ----------------- |----------------- INST 1 |INST 1 INST 2 |INST 2 IF (A > B) |IF (A > B) //this processor will not validate this condition and will jump to INST 4 INST 3 | INST 3 INST 4 |INST 4 When processor 1 validates the condition instruction, it will have to do more computation compared to processor 2 which jumps to INST 4 since the condition is false. The SIMD model of parallel computing consists of two parts: a front-end computer of the usual von Neumann style, and a processor array. The processor array is a set of identical synchronized processing elements capable of simultaneously performing the same operation on different data. Each processor in the array has a small amount of local memory where the distributed data resides while it is being processed in parallel. A program can be developed and executed on the front end using a traditional serial programming language. The application program is executed by the front end in the usual serial way, but issues commands to the processor array to carry out SIMD operations in parallel. The similarity between serial and data parallel programming is one of the strong points of data parallelism. Synchronization is made irrelevant by the lock–step synchronization of the processors: Processors either do nothing or exactly the same operations at the same time. Fine-grained architectures: each processor processes few data elements Processor complexity
Shared memory: bulletin board Message passing: letters Using the shared memory model for multiprocessor could induce a bottleneck to the architecture. Multiple processor could be writing at a occasion. And at some instance, more then one processor could be accessing the same memory location which could greatly ones computation output. Using a local memory for each processing element and using the message passing model improves the previously issue.
Each processor may have registers, buffers, caches, and local memory banks as additional memory resources. These include access control - determines which process accesses are possible to which resources. Access control models make the required check for every access request issued by the processors to the shared memory, against the contents of the access control table. Synchronization constraints limit the time of accesses from sharing processes to shared resources. Protection is a system feature that prevents processes from making arbitrary access to resources belonging to other processes
The UMA model is when any processor are reading a memory location through the cache, they will all have the same delay.
The NUMA model : - Each processor have their own local memory - And memories are all part of a big address space where the processor holds that space with exclusivity Ex: Processor 1 -> 0 – 1GB Processor 2 -> 1GB – 2GB
Processor have each their own local memory. These memory modules are not shared from a big address space as seen in the NUMA model
In static networks, direct fixed links are established among nodes to form a fixed network in dynamic networks, connections are established as needed. Shared memory systems can be designed using bus-based or switch-based INs. Message passing INs can be divided into static and dynamic.
In synchronous mode of operation, a single global clock is used by all components in the system such that the whole system is operating in a lock–step manner. Asynchronous mode of operation, on the other hand, does not require a global clock. Handshaking signals are used instead in order to coordinate the operation of asynchronous systems. While synchronous systems tend to be slower compared to asynchronous systems, they are race and hazard-free. Packet switching is the procedure for which the packet are responsible to find a path to the desire source Circuit switching is when a path has been designed for a packet to reach from the initial source to its destination