Transcript 2014-01-23.pptx
Incremental Run-time Application Mapping for Heterogeneous Network on Chip
2012 IEEE 14th International Conference on High Performance Computing and Communications Jingcheng Shao, Chen Tian-zhou, Li Liu 1
Outline Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion 2
Outline Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion 3
Introduction Propose an incremental run-time application mapping algorithm for heterogeneous NoC Apply the idea of near convex region to heterogeneous NoC 4
Outline Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion 5
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 6
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 9
Near Convex Region Algorithm (cont.) Convex hull 10
Near Convex Region Algorithm (cont.) Convex hull 11
Near Convex Region Algorithm (cont.) 12
Near Convex Region Algorithm (cont.) 13
Near Convex Region Algorithm (cont.) 14
Outline Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion 15
Mapping Problem and Evaluation Metrics 16
Mapping Problem and Evaluation Metrics Application Communication Graph ACG = G(V, E) W(e i,j ) : communication volume T(v k ) : the type of a vertex (T cpu , T xpu ) W cpu (v k ) : computing volume using CPU W xpu (v k ) : computing volume using XPU Application mapping map(v k ) -> PE i,j MAP(ACG) -> R 17
Mapping Problem and Evaluation Metrics Energy model E comp : computing energy consumption E comm : communication energy consumption Computing energy Vk is assigned to CPU, then Xk = 1 Vk is assigned to XPU, then Xk = 0 18
Mapping Problem and Evaluation Metrics Communication energy Total energy computing 19 communication
Outline Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion 20
HNCR-Region Selection Find a proper number of XPU k = num app task ∗ num(available XPU) num(available PEs) K = 5*(4/36) Contiguous convex region selection 21
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 22
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
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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
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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
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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-Node Allocation Sort the node of application Step 1 : select all T xpu, in decreasing order sort their computing volume differences 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 31
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 32
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 33
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 34
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 35
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 36
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 37
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 38
Outline Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion 39
Experiment Setup Target NoC 6 X 6 mesh ACG Generation TGFF Vertex : 5-8 Degree of vertex : 1-4 40
Experiment Setup (cont.) Comparison algorithm Random Greedy Simulator Booksim Orion : calculate energy consumption 41
Experiments and Results Two performance metrics Average latency Average energy consumption 42
Injection Rate 43
Traffic Distribution application 44
Traffic Distribution 45
Mapping Process 46
Mapping Process (cont.) 47
Outline Introduction Near Convex Region Algorithm Mapping Problem and Evaluation Metrics Heterogeneous Near Convex Region Algorithm (HNCR) Experiments and Results Conclusion 48
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 49
Thank you !
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