Computer Architecture
|
|
- Willa Wiggins
- 6 years ago
- Views:
Transcription
1 Computer Architecture Chapter 7 Parallel Processing 1
2 Parallelism Instruction-level parallelism (Ch.6) pipeline superscalar latency issues hazards Processor-level parallelism (Ch.7) array/vector of processors (1 control unit, limited application) multiprocessor (multiple CPUs, common memory) multicomputer (multiple CPUs, each with own memory) 2
3 Processor-Level Parallelism Instruction-level parallelism helps a little, but pipelining and superscalar operation rarely wins more than a factor of five to ten. How to get even more speedup: An array processor which consists of a large number of identical processors that perform the same sequence of instructions on different sets of data. 3
4 4
5 Problems 1- Array processors work only well on problems requiring the same computation to be performed on many data sets simultaneously. 2- they require much more hardware and are difficult to program. 3- The processing elements are not independent CPU s since there is only one control unit! 5
6 Solution: Multiprocessors A multiprocessor is a system with more than one CPU sharing a common memory. Problems: Conflicts will result when processors access the common bus! Solution: Use a multicomputer 6
7 7
8 DESIGN ISSUE FOR PARALLEL COMPUTERS What are the nature, size, and number of the processing elements? What are the nature, size and number of memory modules? How are the processing and memory elements interconnected? 8
9 CLASSIFICATION OF PARALLEL STRUCTURES 1) A single processor system is called: Single Instruction stream, Single Data stream (SISD System). 2) A single stream of instructions is broadcast to a number of processors, each processor operates on its own data. Single Instruction stream, Multiple Data stream (SIMD system) 9
10 3)A number of independent processors executing a different program and having their own sequence of data: Multiple Instruction stream, Multiple Data stream: (MIMD system) 4) A common data structure is manipulated by separate processors each executing a different program. Multiple Instruction stream, Single Data stream ( MISD system) This form does not occur often in practice! 10
11 11
12 12
13 SIMD COMPUTERS Array Processing Idea: single Control Unit for many processing units. Examples: ILLIAC IV, CM-2, Maspar MP-2. 13
14 14
15 SIMD COMPUTERS Vector Processing It has been much more successful commercially. Developed by Seymour Cray for Cray Research. The machine takes two n-element vectors as input, and operates on the corresponding elements in parallel using a vector ALU that can operate on all n elements simultaneously. It produces a vector result. Examples: Cray-1 Vector Supercomputer, 15
16 16
17 CRAY-1 17
18 MIMD SYSTEMS These systems can be divided into 2 categories: Multiprocessors: also called shared memory system Multicomputers: also called distributed memory system 18
19 Multiprocessors All processes working together on a multiprocessor can share a single virtual address space mapped onto the common memory. The ability for two (or more) processes to communicate by just reading and writing memory is the reason multiprocessors are popular. It s an easy model for programmers to understand and is applicable on a very wide range of problems. 19
20 The system runs one copy of the operating system. When every CPU has equal access to all the memory modules and all the I/O devices, the system is called an SMP (Symmetric MultiProcessor) architecture 20
21 Multiprocessors 21
22 Example: UMA Bus-Based Architecture UMA: Uniform Memory Access Architecture based on a single bus bus contention. Solution: add cache to each CPU Also add private memory which can be accessed over a dedicated (private) bus. Results: much less traffic system supports more CPU s. 22
23 UMA Bus-Based Architecture 23
24 NUMA Multiprocessors NUMA: Non-Uniform Memory Access To get to more than 100 CPU s, the UMA fails due to hardware complexity, and to the fact that all memory modules have the same access time. Like UMA, they provide a single address space across all the CPU s, but access to local memory modules is faster than access to remote ones. 24
25 NUMA 25
26 Characteristics of NUMA machines 1) There is a single address space visible to all CPUs Access to remote memory is done using LOAD and STORE instructions. Access to remote memory is slower than access to local memory. 26
27 COMA Multiprocessors COMA: Cache Only Memory Access NUMA machines have the disadvantage that access to remote memory are much slower than local one even though excellent performance, but limited in size and quite expensive. Solution: Using each CPU s main memory as a cache greatly increases the hit rate, hence the performance. 27
28 MESSAGE-PASSING MULTICOMPUTERS In MIMD architectures: Multiprocessors: appear to the OS as having a shared memory that can be accessed by LOAD and STORE instructions. Multicomputers have one address space per CPU Distributed Memory System Instead of reading and writing the common memory, Multicomputers use another communication mechanism: Pass messages back and forth using the interconnection network Software primitives: send and receive 28
29 29
30 Interconnection networks 30
31 31
32 Multiprocessors are easy to program, so why do we have to build multicomputers? Answer: large multicomputers are much simpler and cheaper to build than multiprocessors with the same number of CPUs 32
33 MPP: Massively Parallel Processors MPP: Huge multimillion dollars supercomputers used in science, engineering and industry for very large complex calculations, for handling very large numbers of instructions per second. Or for data warehousing (managing immense databases). Most of these machines use standard CPUs as their processors. ( Pentium, Sun ultrasparc, IBM RS/6000, DEC Alpha..) 33
34 Classic (old) Example: Intel/Sandia Option Red machine 4608 CPUs arranged in 3D mesh (32 x 38 x 2): 4536 compute nodes, 32 service nodes, 32 disk nodes, 6 network nodes, 2 boot nodes. I/O nodes manage 640 disks with 1 TB of data. Speed: up to 100 teraflops FL operations per second. 34
35 35
36 COW : Cluster Of Workstations Also called NOW ( Network Of Workstations). Consists of a few hundreds of PCs or workstations connected by a commercially available network board. The difference between MPPs and COWs is analogous to the difference between a mainframe and a PC! 36
37 Parallel computing performance depends on Hardware CPU speed of individual processors I/O speed of individual processors Interconnection network Scalability Software Parallelizability of algorithms Application programming languages Operating systems Parallel system libraries 37
38 Hardware CPU and I/O speed: Same factors as for single-processor machines Interconnection network Latency (wait time): Distance Collisions / collision resolution Bandwidth (bps) Bus limitations CPU and I/O limitations Scalability Adding more processors affects latency and bandwidth 38
39 Hardware Reducing latency Reducing collisions Resolving collisions Increasing bandwidth 39
40 40
41 41
42 Software Parallelizability of algorithms Number of processors Sequential/parallel parts Amdahl's Law: n = number of processors f = fraction of code that is sequential T = time to process entire algorithm sequentially speedup n 1 (n 1) f Note: total execution time is: ft (1 f n ) T 42
43 Example: Software An algorithm takes 15 seconds to execute on a single 1.8G processor. 30% of the algorithm is sequential. Assuming zero latency and perfect parallelism in the remaining code, how long should the algorithm take on a 20 x 1.8G processor parallel machine? 43
44 Example: Software An algorithm takes 15 seconds to execute on a single 1.8G processor. 30% of the algorithm is sequential. Assuming zero latency and perfect parallelism in the remaining code, how long should the algorithm take on a 20 x 1.8G processor parallel machine? speedup 1 n (n 1)f x Therefore the expected time is T / speedup 15 / (20 / 6.7) = seconds Another way: (.3 x 15) + (.7 x 15) / 20 Seq. + Parallel 44
45 Software speedup n 1 (n 1) Assuming perfect scalability, what are the implications of Amdahl s Law as n? f 45
46 Software speedup n 1 (n 1) f Assuming perfect scalability, what are the implications of Amdahl s Law when n? speedup 1/f (assuming f 0) So if f =.4, parallelism can never make the program run more than 2.5 times as fast. 46
47 Software Parallel system libraries Precompiled functions designed for multiprocessing (e.g., matrix transformations) Functions for control of communication (e.g., background printing) Application programming languages Built-in functions for creating child processes, threads, parallel looping, etc. 47
48 Software issues: In order to really take advantage of hardware parallelism 1. Control models Single instruction thread Multiple instruction threads Single data set Multiple data sets SISD, SIMD, MISD, MIMD Software (including OS, compilers, etc.) must be designed to use the features 48
49 Software issues: In order to really take advantage of hardware parallelism 2. Granularity of parallelism At what levels is parallelism implemented? 3. Computational paradigms Pipelining Divide and conquer Phased computation Replicated worker 49
50 50
51 Software issues: In order to really take advantage of hardware parallelism 4. Communication methods Shared variable Message passing 5. Synchonization Semaphores, locks, etc. 51
Serial. Parallel. CIT 668: System Architecture 2/14/2011. Topics. Serial and Parallel Computation. Parallel Computing
CIT 668: System Architecture Parallel Computing Topics 1. What is Parallel Computing? 2. Why use Parallel Computing? 3. Types of Parallelism 4. Amdahl s Law 5. Flynn s Taxonomy of Parallel Computers 6.
More informationLecture 7: Parallel Processing
Lecture 7: Parallel Processing Introduction and motivation Architecture classification Performance evaluation Interconnection network Zebo Peng, IDA, LiTH 1 Performance Improvement Reduction of instruction
More informationComputer and Information Sciences College / Computer Science Department CS 207 D. Computer Architecture. Lecture 9: Multiprocessors
Computer and Information Sciences College / Computer Science Department CS 207 D Computer Architecture Lecture 9: Multiprocessors Challenges of Parallel Processing First challenge is % of program inherently
More informationComputer and Information Sciences College / Computer Science Department CS 207 D. Computer Architecture. Lecture 9: Multiprocessors
Computer and Information Sciences College / Computer Science Department CS 207 D Computer Architecture Lecture 9: Multiprocessors Challenges of Parallel Processing First challenge is % of program inherently
More informationParallel Processors. The dream of computer architects since 1950s: replicate processors to add performance vs. design a faster processor
Multiprocessing Parallel Computers Definition: A parallel computer is a collection of processing elements that cooperate and communicate to solve large problems fast. Almasi and Gottlieb, Highly Parallel
More informationMultiprocessors - Flynn s Taxonomy (1966)
Multiprocessors - Flynn s Taxonomy (1966) Single Instruction stream, Single Data stream (SISD) Conventional uniprocessor Although ILP is exploited Single Program Counter -> Single Instruction stream The
More informationCOSC 6385 Computer Architecture - Multi Processor Systems
COSC 6385 Computer Architecture - Multi Processor Systems Fall 2006 Classification of Parallel Architectures Flynn s Taxonomy SISD: Single instruction single data Classical von Neumann architecture SIMD:
More informationComputing architectures Part 2 TMA4280 Introduction to Supercomputing
Computing architectures Part 2 TMA4280 Introduction to Supercomputing NTNU, IMF January 16. 2017 1 Supercomputing What is the motivation for Supercomputing? Solve complex problems fast and accurately:
More informationINSTITUTO SUPERIOR TÉCNICO. Architectures for Embedded Computing
UNIVERSIDADE TÉCNICA DE LISBOA INSTITUTO SUPERIOR TÉCNICO Departamento de Engenharia Informática Architectures for Embedded Computing MEIC-A, MEIC-T, MERC Lecture Slides Version 3.0 - English Lecture 11
More informationChap. 4 Multiprocessors and Thread-Level Parallelism
Chap. 4 Multiprocessors and Thread-Level Parallelism Uniprocessor performance Performance (vs. VAX-11/780) 10000 1000 100 10 From Hennessy and Patterson, Computer Architecture: A Quantitative Approach,
More informationDr. Joe Zhang PDC-3: Parallel Platforms
CSC630/CSC730: arallel & Distributed Computing arallel Computing latforms Chapter 2 (2.3) 1 Content Communication models of Logical organization (a programmer s view) Control structure Communication model
More informationComputer parallelism Flynn s categories
04 Multi-processors 04.01-04.02 Taxonomy and communication Parallelism Taxonomy Communication alessandro bogliolo isti information science and technology institute 1/9 Computer parallelism Flynn s categories
More information10 Parallel Organizations: Multiprocessor / Multicore / Multicomputer Systems
1 License: http://creativecommons.org/licenses/by-nc-nd/3.0/ 10 Parallel Organizations: Multiprocessor / Multicore / Multicomputer Systems To enhance system performance and, in some cases, to increase
More informationParallel Architectures
Parallel Architectures Part 1: The rise of parallel machines Intel Core i7 4 CPU cores 2 hardware thread per core (8 cores ) Lab Cluster Intel Xeon 4/10/16/18 CPU cores 2 hardware thread per core (8/20/32/36
More informationComputer Systems Architecture
Computer Systems Architecture Lecture 23 Mahadevan Gomathisankaran April 27, 2010 04/27/2010 Lecture 23 CSCE 4610/5610 1 Reminder ABET Feedback: http://www.cse.unt.edu/exitsurvey.cgi?csce+4610+001 Student
More informationPARALLEL COMPUTER ARCHITECTURES
8 ARALLEL COMUTER ARCHITECTURES 1 CU Shared memory (a) (b) Figure 8-1. (a) A multiprocessor with 16 CUs sharing a common memory. (b) An image partitioned into 16 sections, each being analyzed by a different
More informationLecture 7: Parallel Processing
Lecture 7: Parallel Processing Introduction and motivation Architecture classification Performance evaluation Interconnection network Zebo Peng, IDA, LiTH 1 Performance Improvement Reduction of instruction
More informationLecture 9: MIMD Architectures
Lecture 9: MIMD Architectures Introduction and classification Symmetric multiprocessors NUMA architecture Clusters Zebo Peng, IDA, LiTH 1 Introduction A set of general purpose processors is connected together.
More informationTop500 Supercomputer list
Top500 Supercomputer list Tends to represent parallel computers, so distributed systems such as SETI@Home are neglected. Does not consider storage or I/O issues Both custom designed machines and commodity
More informationBlueGene/L (No. 4 in the Latest Top500 List)
BlueGene/L (No. 4 in the Latest Top500 List) first supercomputer in the Blue Gene project architecture. Individual PowerPC 440 processors at 700Mhz Two processors reside in a single chip. Two chips reside
More informationComputer Systems Architecture
Computer Systems Architecture Lecture 24 Mahadevan Gomathisankaran April 29, 2010 04/29/2010 Lecture 24 CSCE 4610/5610 1 Reminder ABET Feedback: http://www.cse.unt.edu/exitsurvey.cgi?csce+4610+001 Student
More informationParallel Computer Architectures. Lectured by: Phạm Trần Vũ Prepared by: Thoại Nam
Parallel Computer Architectures Lectured by: Phạm Trần Vũ Prepared by: Thoại Nam Outline Flynn s Taxonomy Classification of Parallel Computers Based on Architectures Flynn s Taxonomy Based on notions of
More informationModule 5 Introduction to Parallel Processing Systems
Module 5 Introduction to Parallel Processing Systems 1. What is the difference between pipelining and parallelism? In general, parallelism is simply multiple operations being done at the same time.this
More informationParallel Computing Introduction
Parallel Computing Introduction Bedřich Beneš, Ph.D. Associate Professor Department of Computer Graphics Purdue University von Neumann computer architecture CPU Hard disk Network Bus Memory GPU I/O devices
More informationMotivation for Parallelism. Motivation for Parallelism. ILP Example: Loop Unrolling. Types of Parallelism
Motivation for Parallelism Motivation for Parallelism The speed of an application is determined by more than just processor speed. speed Disk speed Network speed... Multiprocessors typically improve the
More informationLecture 9: MIMD Architectures
Lecture 9: MIMD Architectures Introduction and classification Symmetric multiprocessors NUMA architecture Clusters Zebo Peng, IDA, LiTH 1 Introduction MIMD: a set of general purpose processors is connected
More informationLecture 9: MIMD Architecture
Lecture 9: MIMD Architecture Introduction and classification Symmetric multiprocessors NUMA architecture Cluster machines Zebo Peng, IDA, LiTH 1 Introduction MIMD: a set of general purpose processors is
More informationChapter 18 Parallel Processing
Chapter 18 Parallel Processing Multiple Processor Organization Single instruction, single data stream - SISD Single instruction, multiple data stream - SIMD Multiple instruction, single data stream - MISD
More informationIntroduction to Parallel Computing
Portland State University ECE 588/688 Introduction to Parallel Computing Reference: Lawrence Livermore National Lab Tutorial https://computing.llnl.gov/tutorials/parallel_comp/ Copyright by Alaa Alameldeen
More informationParallel Computer Architecture Spring Shared Memory Multiprocessors Memory Coherence
Parallel Computer Architecture Spring 2018 Shared Memory Multiprocessors Memory Coherence Nikos Bellas Computer and Communications Engineering Department University of Thessaly Parallel Computer Architecture
More informationIntroduction to parallel computers and parallel programming. Introduction to parallel computersand parallel programming p. 1
Introduction to parallel computers and parallel programming Introduction to parallel computersand parallel programming p. 1 Content A quick overview of morden parallel hardware Parallelism within a chip
More informationMULTIPROCESSORS AND THREAD-LEVEL. B649 Parallel Architectures and Programming
MULTIPROCESSORS AND THREAD-LEVEL PARALLELISM B649 Parallel Architectures and Programming Motivation behind Multiprocessors Limitations of ILP (as already discussed) Growing interest in servers and server-performance
More informationRAID 0 (non-redundant) RAID Types 4/25/2011
Exam 3 Review COMP375 Topics I/O controllers chapter 7 Disk performance section 6.3-6.4 RAID section 6.2 Pipelining section 12.4 Superscalar chapter 14 RISC chapter 13 Parallel Processors chapter 18 Security
More informationMULTIPROCESSORS AND THREAD-LEVEL PARALLELISM. B649 Parallel Architectures and Programming
MULTIPROCESSORS AND THREAD-LEVEL PARALLELISM B649 Parallel Architectures and Programming Motivation behind Multiprocessors Limitations of ILP (as already discussed) Growing interest in servers and server-performance
More informationFLYNN S TAXONOMY OF COMPUTER ARCHITECTURE
FLYNN S TAXONOMY OF COMPUTER ARCHITECTURE The most popular taxonomy of computer architecture was defined by Flynn in 1966. Flynn s classification scheme is based on the notion of a stream of information.
More informationLecture 24: Virtual Memory, Multiprocessors
Lecture 24: Virtual Memory, Multiprocessors Today s topics: Virtual memory Multiprocessors, cache coherence 1 Virtual Memory Processes deal with virtual memory they have the illusion that a very large
More informationSMD149 - Operating Systems - Multiprocessing
SMD149 - Operating Systems - Multiprocessing Roland Parviainen December 1, 2005 1 / 55 Overview Introduction Multiprocessor systems Multiprocessor, operating system and memory organizations 2 / 55 Introduction
More informationOverview. SMD149 - Operating Systems - Multiprocessing. Multiprocessing architecture. Introduction SISD. Flynn s taxonomy
Overview SMD149 - Operating Systems - Multiprocessing Roland Parviainen Multiprocessor systems Multiprocessor, operating system and memory organizations December 1, 2005 1/55 2/55 Multiprocessor system
More informationMultiprocessors and Thread-Level Parallelism. Department of Electrical & Electronics Engineering, Amrita School of Engineering
Multiprocessors and Thread-Level Parallelism Multithreading Increasing performance by ILP has the great advantage that it is reasonable transparent to the programmer, ILP can be quite limited or hard to
More informationIssues in Multiprocessors
Issues in Multiprocessors Which programming model for interprocessor communication shared memory regular loads & stores message passing explicit sends & receives Which execution model control parallel
More informationParallel Computing Platforms. Jinkyu Jeong Computer Systems Laboratory Sungkyunkwan University
Parallel Computing Platforms Jinkyu Jeong (jinkyu@skku.edu) Computer Systems Laboratory Sungkyunkwan University http://csl.skku.edu Elements of a Parallel Computer Hardware Multiple processors Multiple
More information3/24/2014 BIT 325 PARALLEL PROCESSING ASSESSMENT. Lecture Notes:
BIT 325 PARALLEL PROCESSING ASSESSMENT CA 40% TESTS 30% PRESENTATIONS 10% EXAM 60% CLASS TIME TABLE SYLLUBUS & RECOMMENDED BOOKS Parallel processing Overview Clarification of parallel machines Some General
More informationComputer Organization. Chapter 16
William Stallings Computer Organization and Architecture t Chapter 16 Parallel Processing Multiple Processor Organization Single instruction, single data stream - SISD Single instruction, multiple data
More informationHandout 3 Multiprocessor and thread level parallelism
Handout 3 Multiprocessor and thread level parallelism Outline Review MP Motivation SISD v SIMD (SIMT) v MIMD Centralized vs Distributed Memory MESI and Directory Cache Coherency Synchronization and Relaxed
More informationNon-uniform memory access machine or (NUMA) is a system where the memory access time to any region of memory is not the same for all processors.
CS 320 Ch. 17 Parallel Processing Multiple Processor Organization The author makes the statement: "Processors execute programs by executing machine instructions in a sequence one at a time." He also says
More informationShared Memory and Distributed Multiprocessing. Bhanu Kapoor, Ph.D. The Saylor Foundation
Shared Memory and Distributed Multiprocessing Bhanu Kapoor, Ph.D. The Saylor Foundation 1 Issue with Parallelism Parallel software is the problem Need to get significant performance improvement Otherwise,
More information18-447: Computer Architecture Lecture 30B: Multiprocessors. Prof. Onur Mutlu Carnegie Mellon University Spring 2013, 4/22/2013
18-447: Computer Architecture Lecture 30B: Multiprocessors Prof. Onur Mutlu Carnegie Mellon University Spring 2013, 4/22/2013 Readings: Multiprocessing Required Amdahl, Validity of the single processor
More informationProcessor Architecture and Interconnect
Processor Architecture and Interconnect What is Parallelism? Parallel processing is a term used to denote simultaneous computation in CPU for the purpose of measuring its computation speeds. Parallel Processing
More informationCS 590: High Performance Computing. Parallel Computer Architectures. Lab 1 Starts Today. Already posted on Canvas (under Assignment) Let s look at it
Lab 1 Starts Today Already posted on Canvas (under Assignment) Let s look at it CS 590: High Performance Computing Parallel Computer Architectures Fengguang Song Department of Computer Science IUPUI 1
More informationChapter 11. Introduction to Multiprocessors
Chapter 11 Introduction to Multiprocessors 11.1 Introduction A multiple processor system consists of two or more processors that are connected in a manner that allows them to share the simultaneous (parallel)
More informationOrganisasi Sistem Komputer
LOGO Organisasi Sistem Komputer OSK 14 Parallel Processing Pendidikan Teknik Elektronika FT UNY Multiple Processor Organization Single instruction, single data stream - SISD Single instruction, multiple
More informationParallel Computing Platforms
Parallel Computing Platforms Jinkyu Jeong (jinkyu@skku.edu) Computer Systems Laboratory Sungkyunkwan University http://csl.skku.edu SSE3054: Multicore Systems, Spring 2017, Jinkyu Jeong (jinkyu@skku.edu)
More informationParallel Computers. c R. Leduc
Parallel Computers Material based on B. Wilkinson et al., PARALLEL PROGRAMMING. Techniques and Applications Using Networked Workstations and Parallel Computers c 2002-2004 R. Leduc Why Parallel Computing?
More informationChapter Seven. Idea: create powerful computers by connecting many smaller ones
Chapter Seven Multiprocessors Idea: create powerful computers by connecting many smaller ones good news: works for timesharing (better than supercomputer) vector processing may be coming back bad news:
More informationCOSC 6385 Computer Architecture - Thread Level Parallelism (I)
COSC 6385 Computer Architecture - Thread Level Parallelism (I) Edgar Gabriel Spring 2014 Long-term trend on the number of transistor per integrated circuit Number of transistors double every ~18 month
More informationLecture 2 Parallel Programming Platforms
Lecture 2 Parallel Programming Platforms Flynn s Taxonomy In 1966, Michael Flynn classified systems according to numbers of instruction streams and the number of data stream. Data stream Single Multiple
More informationIssues in Multiprocessors
Issues in Multiprocessors Which programming model for interprocessor communication shared memory regular loads & stores SPARCCenter, SGI Challenge, Cray T3D, Convex Exemplar, KSR-1&2, today s CMPs message
More informationCSCI 4717 Computer Architecture
CSCI 4717/5717 Computer Architecture Topic: Symmetric Multiprocessors & Clusters Reading: Stallings, Sections 18.1 through 18.4 Classifications of Parallel Processing M. Flynn classified types of parallel
More informationParallel Computers. CPE 631 Session 20: Multiprocessors. Flynn s Tahonomy (1972) Why Multiprocessors?
Parallel Computers CPE 63 Session 20: Multiprocessors Department of Electrical and Computer Engineering University of Alabama in Huntsville Definition: A parallel computer is a collection of processing
More informationOnline Course Evaluation. What we will do in the last week?
Online Course Evaluation Please fill in the online form The link will expire on April 30 (next Monday) So far 10 students have filled in the online form Thank you if you completed it. 1 What we will do
More informationNumber of processing elements (PEs). Computing power of each element. Amount of physical memory used. Data access, Communication and Synchronization
Parallel Computer Architecture A parallel computer is a collection of processing elements that cooperate to solve large problems fast Broad issues involved: Resource Allocation: Number of processing elements
More informationCS650 Computer Architecture. Lecture 10 Introduction to Multiprocessors and PC Clustering
CS650 Computer Architecture Lecture 10 Introduction to Multiprocessors and PC Clustering Andrew Sohn Computer Science Department New Jersey Institute of Technology Lecture 10: Intro to Multiprocessors/Clustering
More informationMulti-core Programming - Introduction
Multi-core Programming - Introduction Based on slides from Intel Software College and Multi-Core Programming increasing performance through software multi-threading by Shameem Akhter and Jason Roberts,
More informationIntro to Multiprocessors
The Big Picture: Where are We Now? Intro to Multiprocessors Output Output Datapath Input Input Datapath [dapted from Computer Organization and Design, Patterson & Hennessy, 2005] Multiprocessor multiple
More informationObjectives of the Course
Objectives of the Course Parallel Systems: Understanding the current state-of-the-art in parallel programming technology Getting familiar with existing algorithms for number of application areas Distributed
More informationMultiple Issue and Static Scheduling. Multiple Issue. MSc Informatics Eng. Beyond Instruction-Level Parallelism
Computing Systems & Performance Beyond Instruction-Level Parallelism MSc Informatics Eng. 2012/13 A.J.Proença From ILP to Multithreading and Shared Cache (most slides are borrowed) When exploiting ILP,
More informationIssues in Parallel Processing. Lecture for CPSC 5155 Edward Bosworth, Ph.D. Computer Science Department Columbus State University
Issues in Parallel Processing Lecture for CPSC 5155 Edward Bosworth, Ph.D. Computer Science Department Columbus State University Introduction Goal: connecting multiple computers to get higher performance
More informationTypes of Parallel Computers
slides1-22 Two principal types: Types of Parallel Computers Shared memory multiprocessor Distributed memory multicomputer slides1-23 Shared Memory Multiprocessor Conventional Computer slides1-24 Consists
More informationChapter-4 Multiprocessors and Thread-Level Parallelism
Chapter-4 Multiprocessors and Thread-Level Parallelism We have seen the renewed interest in developing multiprocessors in early 2000: - The slowdown in uniprocessor performance due to the diminishing returns
More informationComputer Architecture Spring 2016
Computer Architecture Spring 2016 Lecture 19: Multiprocessing Shuai Wang Department of Computer Science and Technology Nanjing University [Slides adapted from CSE 502 Stony Brook University] Getting More
More informationLecture 24: Memory, VM, Multiproc
Lecture 24: Memory, VM, Multiproc Today s topics: Security wrap-up Off-chip Memory Virtual memory Multiprocessors, cache coherence 1 Spectre: Variant 1 x is controlled by attacker Thanks to bpred, x can
More informationMultiprocessors & Thread Level Parallelism
Multiprocessors & Thread Level Parallelism COE 403 Computer Architecture Prof. Muhamed Mudawar Computer Engineering Department King Fahd University of Petroleum and Minerals Presentation Outline Introduction
More informationComputer Architecture Lecture 27: Multiprocessors. Prof. Onur Mutlu Carnegie Mellon University Spring 2015, 4/6/2015
18-447 Computer Architecture Lecture 27: Multiprocessors Prof. Onur Mutlu Carnegie Mellon University Spring 2015, 4/6/2015 Assignments Lab 7 out Due April 17 HW 6 Due Friday (April 10) Midterm II April
More informationMulticores, Multiprocessors, and Clusters
1 / 12 Multicores, Multiprocessors, and Clusters P. A. Wilsey Univ of Cincinnati 2 / 12 Classification of Parallelism Classification from Textbook Software Sequential Concurrent Serial Some problem written
More informationParallel Architecture. Hwansoo Han
Parallel Architecture Hwansoo Han Performance Curve 2 Unicore Limitations Performance scaling stopped due to: Power Wire delay DRAM latency Limitation in ILP 3 Power Consumption (watts) 4 Wire Delay Range
More informationParallel Architecture. Sathish Vadhiyar
Parallel Architecture Sathish Vadhiyar Motivations of Parallel Computing Faster execution times From days or months to hours or seconds E.g., climate modelling, bioinformatics Large amount of data dictate
More informationNon-Uniform Memory Access (NUMA) Architecture and Multicomputers
Non-Uniform Memory Access (NUMA) Architecture and Multicomputers Parallel and Distributed Computing Department of Computer Science and Engineering (DEI) Instituto Superior Técnico February 29, 2016 CPD
More informationWHY PARALLEL PROCESSING? (CE-401)
PARALLEL PROCESSING (CE-401) COURSE INFORMATION 2 + 1 credits (60 marks theory, 40 marks lab) Labs introduced for second time in PP history of SSUET Theory marks breakup: Midterm Exam: 15 marks Assignment:
More informationParallel Architectures
Parallel Architectures CPS343 Parallel and High Performance Computing Spring 2018 CPS343 (Parallel and HPC) Parallel Architectures Spring 2018 1 / 36 Outline 1 Parallel Computer Classification Flynn s
More informationParallel Processing. Computer Architecture. Computer Architecture. Outline. Multiple Processor Organization
Computer Architecture Computer Architecture Prof. Dr. Nizamettin AYDIN naydin@yildiz.edu.tr nizamettinaydin@gmail.com Parallel Processing http://www.yildiz.edu.tr/~naydin 1 2 Outline Multiple Processor
More informationNon-Uniform Memory Access (NUMA) Architecture and Multicomputers
Non-Uniform Memory Access (NUMA) Architecture and Multicomputers Parallel and Distributed Computing Department of Computer Science and Engineering (DEI) Instituto Superior Técnico September 26, 2011 CPD
More informationChapter 5. Thread-Level Parallelism
Chapter 5 Thread-Level Parallelism Instructor: Josep Torrellas CS433 Copyright Josep Torrellas 1999, 2001, 2002, 2013 1 Progress Towards Multiprocessors + Rate of speed growth in uniprocessors saturated
More informationMIMD Overview. Intel Paragon XP/S Overview. XP/S Usage. XP/S Nodes and Interconnection. ! Distributed-memory MIMD multicomputer
MIMD Overview Intel Paragon XP/S Overview! MIMDs in the 1980s and 1990s! Distributed-memory multicomputers! Intel Paragon XP/S! Thinking Machines CM-5! IBM SP2! Distributed-memory multicomputers with hardware
More informationWhy Multiprocessors?
Why Multiprocessors? Motivation: Go beyond the performance offered by a single processor Without requiring specialized processors Without the complexity of too much multiple issue Opportunity: Software
More informationIntroduction to Parallel Programming
Introduction to Parallel Programming January 14, 2015 www.cac.cornell.edu What is Parallel Programming? Theoretically a very simple concept Use more than one processor to complete a task Operationally
More informationARCHITECTURES FOR PARALLEL COMPUTATION
Datorarkitektur Fö 11/12-1 Datorarkitektur Fö 11/12-2 Why Parallel Computation? ARCHITECTURES FOR PARALLEL COMTATION 1. Why Parallel Computation 2. Parallel Programs 3. A Classification of Computer Architectures
More informationIntroduction II. Overview
Introduction II Overview Today we will introduce multicore hardware (we will introduce many-core hardware prior to learning OpenCL) We will also consider the relationship between computer hardware and
More informationMultiprocessors and Thread Level Parallelism Chapter 4, Appendix H CS448. The Greed for Speed
Multiprocessors and Thread Level Parallelism Chapter 4, Appendix H CS448 1 The Greed for Speed Two general approaches to making computers faster Faster uniprocessor All the techniques we ve been looking
More informationCDA3101 Recitation Section 13
CDA3101 Recitation Section 13 Storage + Bus + Multicore and some exam tips Hard Disks Traditional disk performance is limited by the moving parts. Some disk terms Disk Performance Platters - the surfaces
More informationNon-Uniform Memory Access (NUMA) Architecture and Multicomputers
Non-Uniform Memory Access (NUMA) Architecture and Multicomputers Parallel and Distributed Computing MSc in Information Systems and Computer Engineering DEA in Computational Engineering Department of Computer
More informationARCHITECTURAL CLASSIFICATION. Mariam A. Salih
ARCHITECTURAL CLASSIFICATION Mariam A. Salih Basic types of architectural classification FLYNN S TAXONOMY OF COMPUTER ARCHITECTURE FENG S CLASSIFICATION Handler Classification Other types of architectural
More informationIntroduction to Parallel Programming
Introduction to Parallel Programming David Lifka lifka@cac.cornell.edu May 23, 2011 5/23/2011 www.cac.cornell.edu 1 y What is Parallel Programming? Using more than one processor or computer to complete
More informationA taxonomy of computer architectures
A taxonomy of computer architectures 53 We have considered different types of architectures, and it is worth considering some way to classify them. Indeed, there exists a famous taxonomy of the various
More informationChapter 17 - Parallel Processing
Chapter 17 - Parallel Processing Luis Tarrataca luis.tarrataca@gmail.com CEFET-RJ Luis Tarrataca Chapter 17 - Parallel Processing 1 / 71 Table of Contents I 1 Motivation 2 Parallel Processing Categories
More informationHigh Performance Computing in C and C++
High Performance Computing in C and C++ Rita Borgo Computer Science Department, Swansea University Announcement No change in lecture schedule: Timetable remains the same: Monday 1 to 2 Glyndwr C Friday
More informationIntroduction to parallel computing
Introduction to parallel computing 2. Parallel Hardware Zhiao Shi (modifications by Will French) Advanced Computing Center for Education & Research Vanderbilt University Motherboard Processor https://sites.google.com/
More informationMultiprocessing and Scalability. A.R. Hurson Computer Science and Engineering The Pennsylvania State University
A.R. Hurson Computer Science and Engineering The Pennsylvania State University 1 Large-scale multiprocessor systems have long held the promise of substantially higher performance than traditional uniprocessor
More informationArchitecture of parallel processing in computer organization
American Journal of Computer Science and Engineering 2014; 1(2): 12-17 Published online August 20, 2014 (http://www.openscienceonline.com/journal/ajcse) Architecture of parallel processing in computer
More informationDheeraj Bhardwaj May 12, 2003
HPC Systems and Models Dheeraj Bhardwaj Department of Computer Science & Engineering Indian Institute of Technology, Delhi 110 016 India http://www.cse.iitd.ac.in/~dheerajb 1 Sequential Computers Traditional
More informationCOMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. 5 th. Edition. Chapter 6. Parallel Processors from Client to Cloud
COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface 5 th Edition Chapter 6 Parallel Processors from Client to Cloud Introduction Goal: connecting multiple computers to get higher performance
More information