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Incremental Run-time Application Mapping for Heterogeneous Network

on Chip2012 IEEE 14th International Conference on High Performance Computing and

CommunicationsJingcheng Shao, Chen Tian-zhou, Li Liu

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Outline

Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion

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Outline

Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion

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Introduction

Propose an incremental run-time application mapping algorithm for heterogeneous NoC

Apply the idea of near convex region to heterogeneous NoC

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Outline

Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion

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Near Convex Region Algorithm

Two steps Select a near convex region whose area is close to its convex hull Assign nodes to the selected region

Optimizing the mapping results of not only the currently incoming application but also the additional applications in the future

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Near Convex Region Algorithm (cont.)

Convex region?

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Near Convex Region Algorithm (cont.)

Convex region?

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Near Convex Region Algorithm (cont.)

Convex hull

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Near Convex Region Algorithm (cont.)

Convex hull

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Near Convex Region Algorithm (cont.)

Convex hull

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Near Convex Region Algorithm (cont.)

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Near Convex Region Algorithm (cont.)

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Near Convex Region Algorithm (cont.)

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Outline

Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion

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Mapping Problem and Evaluation Metrics

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Mapping Problem and Evaluation Metrics Application Communication Graph

ACG = G(V, E) W(ei,j) : communication volume T(vk) : the type of a vertex (Tcpu, Txpu) Wcpu(vk) : computing volume using CPU Wxpu(vk) : computing volume using XPU

Application mapping map(vk) -> PEi,j

MAP(ACG) -> R

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Mapping Problem and Evaluation Metrics Energy model

Ecomp : computing energy consumption Ecomm : communication energy consumption

Computing energy Vk is assigned to CPU, then Xk = 1 Vk is assigned to XPU, then Xk = 0

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Mapping Problem and Evaluation Metrics Communication energy

Total energy

computing communication

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Outline

Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion

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HNCR-Region Selection

Find a proper number of XPU

K = 5*(4/36) Contiguous convex region selection

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HNCR-Region Selection D(PE) : the number of

available neighbors of the PE

C(PE) : the distance from the geometric center of the selected region to the PE

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HNCR-Region Selection D(PE) : the number of

available neighbors of the PE

C(PE) : the distance from the geometric center of the selected region to the PE

R’

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HNCR-Region Selection D(PE) : the number of

available neighbors of the PE

C(PE) : the distance from the geometric center of the selected region to the PE

R’

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HNCR-Region Selection D(PE) : the number of

available neighbors of the PE

C(PE) : the distance from the geometric center of the selected region to the PE

R’

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HNCR-Region Selection D(PE) : the number of

available neighbors of the PE

C(PE) : the distance from the geometric center of the selected region to the PE

R’

S

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HNCR-Region Selection D(PE) : the number of

available neighbors of the PE

C(PE) : the distance from the geometric center of the selected region to the PE

R’

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HNCR-Region Selection D(PE) : the number of

available neighbors of the PE

C(PE) : the distance from the geometric center of the selected region to the PE

R’

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HNCR-Region Selection D(PE) : the number of

available neighbors of the PE

C(PE) : the distance from the geometric center of the selected region to the PE

R’

SS

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HNCR-Region Selection D(PE) : the number of

available neighbors of the PE

C(PE) : the distance from the geometric center of the selected region to the PE

R’

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HNCR-Node Allocation Sort the node of application

Step 1 : select all Txpu, sort their computing volume differences in decreasing order

V5, V4 Keep the first K nodes (assume k =1)

Step 2 : sort the remaining nodes by their communication volume with adjacent nodes in decreasing order

V1, V4, V2, V3

Step 3 : append the second list to the tail of the first one V5, V1, V4, V2, V3

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HNCR-Node Allocation DISCOVER : Select possible temporary

locations for a node

FINISH : Select an accurate location for a node such that the distance between this node and its “discovered” or “finished” neighbors is minimized

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HNCR-Node Allocation DISCOVER : Select possible temporary

locations for a node

FINISH : Select an accurate location for a node such that the distance between this node and its “discovered” or “finished” neighbors is minimized

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HNCR-Node Allocation DISCOVER : Select possible temporary

locations for a node

FINISH : Select an accurate location for a node such that the distance between this node and its “discovered” or “finished” neighbors is minimized

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HNCR-Node Allocation DISCOVER : Select possible temporary

locations for a node

FINISH : Select an accurate location for a node such that the distance between this node and its “discovered” or “finished” neighbors is minimized

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HNCR-Node Allocation DISCOVER : Select possible temporary

locations for a node

FINISH : Select an accurate location for a node such that the distance between this node and its “discovered” or “finished” neighbors is minimized

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HNCR-Node Allocation DISCOVER : Select possible temporary

locations for a node

FINISH : Select an accurate location for a node such that the distance between this node and its “discovered” or “finished” neighbors is minimized

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HNCR-Node Allocation DISCOVER : Select possible temporary

locations for a node

FINISH : Select an accurate location for a node such that the distance between this node and its “discovered” or “finished” neighbors is minimized

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Outline

Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion

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Experiment Setup

Target NoC 6 X 6 mesh

ACG Generation TGFF Vertex : 5-8 Degree of vertex : 1-4

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Experiment Setup (cont.)

Comparison algorithm Random Greedy

Simulator Booksim Orion : calculate energy consumption

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Experiments and Results

Two performance metrics Average latency Average energy consumption

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Injection Rate

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Traffic Distributionapplication

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Traffic Distribution

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Mapping Process

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Mapping Process (cont.)

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Outline

Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion

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Conclusion

Proposed an incremental run-time application mapping algorithm for heterogeneous NoC

Extend the algorithm to heterogeneous NoC which more types of PEs The algorithm needs to be adjusted when system is much

complicated

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Thank you !