Issues in Multiprocessors

Similar documents
Issues in Multiprocessors

Computer and Information Sciences College / Computer Science Department CS 207 D. Computer Architecture. Lecture 9: Multiprocessors

Computer parallelism Flynn s categories

Dr. Joe Zhang PDC-3: Parallel Platforms

Lecture 30: Multiprocessors Flynn Categories, Large vs. Small Scale, Cache Coherency Professor Randy H. Katz Computer Science 252 Spring 1996

Multiprocessing and Scalability. A.R. Hurson Computer Science and Engineering The Pennsylvania State University

Parallel Computing Platforms

Parallel Computing Platforms. Jinkyu Jeong Computer Systems Laboratory Sungkyunkwan University

Number of processing elements (PEs). Computing power of each element. Amount of physical memory used. Data access, Communication and Synchronization

Parallel Architectures

Chap. 4 Multiprocessors and Thread-Level Parallelism

COSC 6385 Computer Architecture - Thread Level Parallelism (I)

Processor Architecture and Interconnect

Multiple Issue and Static Scheduling. Multiple Issue. MSc Informatics Eng. Beyond Instruction-Level Parallelism

COSC 6385 Computer Architecture - Multi Processor Systems

Multiprocessors and Thread-Level Parallelism. Department of Electrical & Electronics Engineering, Amrita School of Engineering

Computer and Information Sciences College / Computer Science Department CS 207 D. Computer Architecture. Lecture 9: Multiprocessors

Introduction to Parallel Computing

INSTITUTO SUPERIOR TÉCNICO. Architectures for Embedded Computing

CMSC 611: Advanced. Parallel Systems

FLYNN S TAXONOMY OF COMPUTER ARCHITECTURE

Computing architectures Part 2 TMA4280 Introduction to Supercomputing

Parallel Architecture. Sathish Vadhiyar

CS 590: High Performance Computing. Parallel Computer Architectures. Lab 1 Starts Today. Already posted on Canvas (under Assignment) Let s look at it

Parallel Processors. The dream of computer architects since 1950s: replicate processors to add performance vs. design a faster processor

Introduction II. Overview

Chapter 9 Multiprocessors

Multiprocessors - Flynn s Taxonomy (1966)

Parallel Computers. CPE 631 Session 20: Multiprocessors. Flynn s Tahonomy (1972) Why Multiprocessors?

Computer Architecture

Computer Systems Architecture

Objective. We will study software systems that permit applications programs to exploit the power of modern high-performance computers.

Parallel Computer Architectures. Lectured by: Phạm Trần Vũ Prepared by: Thoại Nam

MULTIPROCESSORS AND THREAD-LEVEL. B649 Parallel Architectures and Programming

MULTIPROCESSORS AND THREAD-LEVEL PARALLELISM. B649 Parallel Architectures and Programming

WHY PARALLEL PROCESSING? (CE-401)

Multiprocessors. Flynn Taxonomy. Classifying Multiprocessors. why would you want a multiprocessor? more is better? Cache Cache Cache.

Computer Systems Architecture

Multi-core Programming - Introduction

Convergence of Parallel Architecture

Flynn s Classification

Multiprocessors and Thread Level Parallelism Chapter 4, Appendix H CS448. The Greed for Speed

CS 770G - Parallel Algorithms in Scientific Computing Parallel Architectures. May 7, 2001 Lecture 2

Outline. Distributed Shared Memory. Shared Memory. ECE574 Cluster Computing. Dichotomy of Parallel Computing Platforms (Continued)

Computer Architecture: Parallel Processing Basics. Prof. Onur Mutlu Carnegie Mellon University

18-447: Computer Architecture Lecture 30B: Multiprocessors. Prof. Onur Mutlu Carnegie Mellon University Spring 2013, 4/22/2013

Lecture 2 Parallel Programming Platforms

3/24/2014 BIT 325 PARALLEL PROCESSING ASSESSMENT. Lecture Notes:

CS Parallel Algorithms in Scientific Computing

Parallel Architecture. Hwansoo Han

Overview. Processor organizations Types of parallel machines. Real machines

Computer Organization. Chapter 16

Organisasi Sistem Komputer

Computer Architecture Lecture 27: Multiprocessors. Prof. Onur Mutlu Carnegie Mellon University Spring 2015, 4/6/2015

Module 5 Introduction to Parallel Processing Systems

CS4230 Parallel Programming. Lecture 3: Introduction to Parallel Architectures 8/28/12. Homework 1: Parallel Programming Basics

THREAD LEVEL PARALLELISM

Chapter 5. Thread-Level Parallelism

Online Course Evaluation. What we will do in the last week?

Chapter 4 Data-Level Parallelism

Introduction to parallel computers and parallel programming. Introduction to parallel computersand parallel programming p. 1

Parallel Computer Architecture Spring Shared Memory Multiprocessors Memory Coherence

Aleksandar Milenkovich 1

MIMD Overview. Intel Paragon XP/S Overview. XP/S Usage. XP/S Nodes and Interconnection. ! Distributed-memory MIMD multicomputer

Introduction to Multiprocessors (Part I) Prof. Cristina Silvano Politecnico di Milano

Three basic multiprocessing issues

Chapter 11. Introduction to Multiprocessors

Objectives of the Course

Why Multiprocessors?

A taxonomy of computer architectures

Aleksandar Milenkovic, Electrical and Computer Engineering University of Alabama in Huntsville

Spring 2011 Parallel Computer Architecture Lecture 4: Multi-core. Prof. Onur Mutlu Carnegie Mellon University

Lecture 24: Virtual Memory, Multiprocessors

CSCI 4717 Computer Architecture

Lect. 2: Types of Parallelism

Chapter Seven. Idea: create powerful computers by connecting many smaller ones

Advanced Parallel Architecture. Annalisa Massini /2017

Advanced Computer Architecture. The Architecture of Parallel Computers

Chapter 8. Multiprocessors. In-Cheol Park Dept. of EE, KAIST

COSC4201 Multiprocessors

Introduction. EE 4504 Computer Organization

Multiprocessors & Thread Level Parallelism

Part IV. Chapter 15 - Introduction to MIMD Architectures

Computer Science 146. Computer Architecture

CS650 Computer Architecture. Lecture 10 Introduction to Multiprocessors and PC Clustering

Lecture 24: Memory, VM, Multiproc

Serial. Parallel. CIT 668: System Architecture 2/14/2011. Topics. Serial and Parallel Computation. Parallel Computing

Embedded Systems Architecture. Computer Architectures

CS252 Graduate Computer Architecture Lecture 14. Multiprocessor Networks March 9 th, 2011

Computer Architecture Spring 2016

SMD149 - Operating Systems - Multiprocessing

Overview. SMD149 - Operating Systems - Multiprocessing. Multiprocessing architecture. Introduction SISD. Flynn s taxonomy

Computer Science 246. Computer Architecture

Dheeraj Bhardwaj May 12, 2003

Parallel Computing Why & How?

INSTITUTO SUPERIOR TÉCNICO. Architectures for Embedded Computing

CSE Introduction to Parallel Processing. Chapter 4. Models of Parallel Processing

Motivation for Parallelism. Motivation for Parallelism. ILP Example: Loop Unrolling. Types of Parallelism

Handout 3 Multiprocessor and thread level parallelism

Intro to Multiprocessors

Transcription:

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 passing explicit sends & receives TMC CM-5, Intel Paragon, IBM SP-2 Which execution model control parallel identify & synchronize different asynchronous threads data parallel same operation on different parts of the shared data space Spring 2008 CSE 471 - Multiprocessors 1

Issues in Multiprocessors How to express parallelism language support HPF, ZPL runtime library constructs coarse-grain, explicitly parallel C programs automatic (compiler) detection implicitly parallel C & Fortran programs, e.g., SUIF & PTRANS compilers Application development embarrassingly parallel programs could be easily parallelized development of different algorithms for same problem Spring 2008 CSE 471 - Multiprocessors 2

Issues in Multiprocessors How to get good parallel performance recognize parallelism transform programs to increase parallelism without decreasing processor locality decrease sharing costs Spring 2008 CSE 471 - Multiprocessors 3

Flynn Classification SISD: single instruction stream, single data stream single-context uniprocessors SIMD: single instruction stream, multiple data streams exploits data parallelism example: Thinking Machines CM MISD: multiple instruction streams, single data stream systolic arrays example: Intel iwarp, today s streaming processors MIMD: multiple instruction streams, multiple data streams multiprocessors multithreaded processors parallel programming & multiprogramming relies on control parallelism: execute & synchronize different asynchronous threads of control example: most processor companies have CMP configurations Spring 2008 CSE 471 - Multiprocessors 4

CM-1 Spring 2008 CSE 471 - Multiprocessors 5

Systolic Array Spring 2008 CSE 471 - Multiprocessors 6

MIMD Low-end bus-based simple, but a bottleneck simple cache coherency protocol physically centralized memory uniform memory access (UMA machine) Sequent Symmetry, SPARCCenter, Alpha-, PowerPC- or SPARCbased servers, most of today s CMPs Spring 2008 CSE 471 - Multiprocessors 7

Low-end MP Spring 2008 CSE 471 - Multiprocessors 8

MIMD High-end higher bandwidth, multiple-path interconnect more scalable more complex cache coherency protocol (if shared memory) longer latencies physically distributed memory non-uniform memory access (NUMA machine) could have processor clusters SGI Challenge, Convex Examplar, Cray T3D, IBM SP-2, Intel Paragon, Sun T1 Spring 2008 CSE 471 - Multiprocessors 9

High-end MP Spring 2008 CSE 471 - Multiprocessors 10

Comparison of Issue Capabilities Spring 2008 CSE 471 - Multiprocessors 11

Shared Memory vs. Message Passing Shared memory + simple parallel programming model global shared address space not worry about data locality but get better performance when program for data placement lower latency when data is local but can do data placement if it is crucial, but don t have to hardware maintains data coherence synchronize to order processor s accesses to shared data like uniprocessor code so parallelizing by programmer or compiler is easier can focus on program semantics, not interprocessor communication Spring 2008 CSE 471 - Multiprocessors 12

Shared Memory vs. Message Passing Shared memory + low latency (no message passing software) but overlap of communication & computation latency-hiding techniques can be applied to message passing machines + higher bandwidth for small transfers but usually the only choice Spring 2008 CSE 471 - Multiprocessors 13

Shared Memory vs. Message Passing Message passing + abstraction in the programming model encapsulates the communication costs but more complex programming model additional language constructs need to program for nearest neighbor communication + no coherency hardware + good throughput on large transfers but what about small transfers? + more scalable (memory latency doesn t scale with the number of processors) but large-scale SM has distributed memory also hah! so you re going to adopt the message-passing model? Spring 2008 CSE 471 - Multiprocessors 14

Shared Memory vs. Message Passing Why there was a debate little experimental data not separate implementation from programming model can emulate one paradigm with the other Who won? MP on SM machine message buffers in local (to each processor) memory copy messages by ld/st between buffers SM on MP machine ld/st becomes a message copy sloooooooooow Spring 2008 CSE 471 - Multiprocessors 15