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CoNA : Dynamic Application Mapping forCongestion Reduction in Many-Core

Systems

2012 IEEE 30th International Conference on Computer Design (ICCD)

M. Fattah, M. Ramirez, M. Daneshtalab, P. Liljeberg, J. Plosila

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Outline

Introduction

Mapping Problem and Evaluation Metrics

Contiguous Neighborhood Allocation Mapping

Experimental Setup

Results and Analysis

Conclusion

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Outline

Introduction

Mapping Problem and Evaluation Metrics

Contiguous Neighborhood Allocation Mapping

Experimental Setup

Results and Analysis

Conclusion

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Introduction

An efficient algorithm for run-time application mapping problem

Three novel contributions

First node selection

First task selection

Map the rest of tasks onto nearest neighborhood

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Outline

Introduction

Mapping Problem and Evaluation Metrics

Contiguous Neighborhood Allocation Mapping

Experimental Setup

Results and Analysis

Conclusion

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Mapping Problem and Evaluation Metrics

Applications

Ap =TG(T, E) ti ϵ T ei,j ϵ E

Communication platform

AG(Ñ, L)

ñi,j={(ri,j, pei,j)| ñi,jϵ Ñ, 0≤ i<M, 0≤ j<N}

Manhattan Distance : MD(ñi,j, ñm,n ) = (|i - m| + |j - n|)

Mapping function

map: T→ Ñ, s.t. map(ti ) = ñm,n; ∀ti∈T, ∃nm,n∈ Ñ

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Evaluation Metrics

Packet latency

Average Manhattan Distance

Average Weighted Manhattan Distance

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Evaluation Metrics (cont.)

Mapped Region Dispersion

Internal Congestion Ratio (ICR)

The number of edges using the same channel with respect to its total number of edges

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Outline

Introduction

Mapping Problem and Evaluation Metrics

Contiguous Neighborhood Allocation Mapping

Experimental Setup

Results and Analysis

Conclusion

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Contiguous Neighborhood Allocation Mapping(CoNA)

Three steps

First node selection

Choosing the first task of the application

Contiguous neighborhood allocation

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CoNA (cont.)

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CoNA (cont.)

First node selection

The nearest node to the central manager among the nodes with the largest number of available neighbors

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CoNA (cont.)

Choosing the first task of the application

Selects the task with the largest number of edges

The most intensive communication

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CoNA (cont.)

Contiguous neighborhood allocation

Task graph is traversed in the breadth-first order, paired with their predecessors is: {(t1, t4), (t2, t4), (t5, t4), (t0, t1), (t3, t2)}

Select the one which fits in the smallest square with the first node

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CoNA (cont.)

Contiguous neighborhood allocation

Task graph is traversed in the breadth-first order, paired with their predecessors is: {(t1, t4), (t2, t4), (t5, t4), (t0, t1), (t3, t2)}

Select the one which fits in the smallest square with the first node

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CoNA (cont.)

Contiguous neighborhood allocation

Task graph is traversed in the breadth-first order, paired with their predecessors is: {(t1, t4), (t2, t4), (t5, t4), (t0, t1), (t3, t2)}

Select the one which fits in the smallest square with the first node

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CoNA (cont.)

Contiguous neighborhood allocation

Task graph is traversed in the breadth-first order, paired with their predecessors is: {(t1, t4), (t2, t4), (t5, t4), (t0, t1), (t3, t2)}

Select the one which fits in the smallest square with the first node

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CoNA (cont.)

Contiguous neighborhood allocation

Task graph is traversed in the breadth-first order, paired with their predecessors is: {(t1, t4), (t2, t4), (t5, t4), (t0, t1), (t3, t2)}

Select the one which fits in the smallest square with the first node

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CoNA (cont.)

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Outline

Introduction

Mapping Problem and Evaluation Metrics

Contiguous Neighborhood Allocation Mapping

Experimental Setup

Results and Analysis

Conclusion

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Experimental Setup

NoC platform

Plasma processor

Local memory

DMA controller

Tra-NI interface

Central manager (CM)

The maximum number of applications that could be injected per second into the system is denoted as λfull

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Experimental Setup (cont.)

Simulation

To extract packet latency

FPGA

To investigate CoNA time complexity

Xilinx ML605

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Experimental Setup (cont.)

Application set

Task graphs are randomly generated (set1) using the Task graph generator

Number of nodes : 4 – 11

Weight of edges : 4 – 16 flits

The weights of applications edges are equally multiplied by 16 (set16)

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Outline

Introduction

Mapping Problem and Evaluation Metrics

Contiguous Neighborhood Allocation Mapping

Experimental Setup

Results and Analysis

Conclusion

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Results and Analysis

Packet latency evaluation

Time complexity evaluation

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Packet latency evaluation

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Packet latency evaluation (cont.)

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Packet latency evaluation (cont.)

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Packet latency evaluation (cont.)

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Time complexity evaluation

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Time complexity evaluation (cont.)

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Outline

Introduction

Mapping Problem and Evaluation Metrics

Contiguous Neighborhood Allocation Mapping

Experimental Setup

Results and Analysis

Conclusion

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Conclusion

An efficient run-time task allocation is proposed

Reduce internal and external congestions

Three novel contributions

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Thank you !