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Chapter 7 Dataflow Architecture

Chapter 7 Dataflow Architecture. 7.1 Dataflow Models. A dataflow program: the sequence of operations is not specified, but depends upon the need and availability of data. data driven Dataflow concepts: the finest grain level (instruction level parallelism) DFG (dataflow graph): Figure 7.2.

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Chapter 7 Dataflow Architecture

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  1. Chapter 7 Dataflow Architecture

  2. 7.1 Dataflow Models • A dataflow program: the sequence of operations is not specified, but depends upon the need and availability of data. • data driven • Dataflow concepts: the finest grain level (instruction level parallelism) • DFG (dataflow graph): Figure 7.2

  3. 7.1 Dataflow Models (continued) • Two dataflow models: based on firing rule • Static dataflow model: Figure 7.3 • A node fires only when each of its input arcs has a token and its output arcs are empty. • Dynamic dataflow model: Figure 7.5 • A node fires only when all its input have tokens and the absence of tokens on its outputs is not necessary.

  4. 7.2 Dataflow Graphs • DFG operators: Figure 7.6 • DFG control operators: Figure 7.7 • Race condition: To eliminate the problem labels are attached to the data

  5. 7.3 Dataflow Languages • Id (Irvine dataflow language) • VAL (Value-oriented Algorithm Language) • HASAL • Lapse • SISAL (Streams and Iteration in Single Assignment Language)

  6. 7.3 Dataflow Languages (continued) • The essential features of dataflow language • The language should be functional. • The language should allow a nonsequential specifications • The language should obey the single assignment rule. • The language should be no side effects.

  7. 7.3 Dataflow Languages (continued) • Differences of dataflow languages from conventional languages • The concepts of variables: all variables are values not memory locations • Applicative • Locality of effect • Go to constructs are not required • The iteration structures are somewhat unusual.

  8. 7.4 Example Systems • Static architectures • MIT static architecture • TI’s DDP system • LAU • Dynamic architecture • Manchester dataflow machine • Irvine dataflow machine • Demand-driven machine (DDM) • Epsilon dataflow processor • EDDY • MIT/Motorola Monsoon system

  9. 7.5 Performance • Figure 7.19 • Figure 7.21 • Figure 7.22 • Table 7.1 • Table 7.2 • Table 7.3

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