Chapter 2

Prelude - Chapter 1 - Chapter 2 - Chapter 3 - Chapter 4 - Chapter 5 - Chapter 6 - Chapter 7


Click here to start


Table of Contents

Chapter 2

Dataflow processors

Dataflow vs. control-flow

Dataflow model of computation

Dataflow languages

Dataflow architectures - Overview

Pure dataflow

Static dataflow

Dataflow graph and activity template

Acknowledgement signals

MIT Static Dataflow Machine

Deficiencies of static dataflow

Dynamic dataflow

The U-interpreter (U = unraveling)

The U-interpreter

MERGE and SWITCH nodes

Branch Implementations

Basic loop implementation

Function application

I-structures (I = incremental)

I-structures

MIT Tagged-Token Dataflow Architecture

Manchester Dataflow Machine

Advantages and deficiencies of dynamic dataflow

Explicit Token Store (ETS) approach

Explicit token store

Monsoon, an explicit token store machine

Monsoon, an explicit token store machine

Monsoon prototype

Dataflow processors - Hybrids

Augmenting dataflow with control-flow

Threaded dataflow

Threaded dataflow (continued)

Direct token recycling of Monsoon

Epsilon and EM-4

Large-grain (coarse-grain) dataflow

Dataflow with complex machine operations

Dataflow with complex machine operations and combined with LGDF

Augmenting dataflow with control-flow

Lessons learned from dataflow

Comparing dataflow computers with superscalar microprocessors

Lessons learned from dataflow (Pipeline issues)

Lessons learned from dataflow (Continued)

Lessons learned from dataflow (Continued)

Lessons learned from dataflow (Memory latency)

Lessons learned from dataflow (Continued)

Lessons learned from dataflow (Continued)

Lessons learned from dataflow (alternative instruction window organizations)

Author: Jurij Silc

Email: Jurij.Silc@ijs.si

Home Page: http://www-csd.ijs.si/silc

Download presentation source