incremental run-time application mapping for heterogeneous network on chip 2012 ieee 14th...
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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 3TRANSCRIPT
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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 !