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? 7

Near Convex Region Algorithm (cont.)  Convex region? 8

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 19 computingcommunication

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 21

HNCR-Region Selection 22  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

HNCR-Region Selection 23  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’

HNCR-Region Selection 24  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’

HNCR-Region Selection 25  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’

HNCR-Region Selection 26  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

HNCR-Region Selection 27  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’

HNCR-Region Selection 28  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’

HNCR-Region Selection 29  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 S

HNCR-Region Selection 30  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’

HNCR-Node Allocation  Sort the node of application  Step 1 : select all T xpu, 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 31

HNCR-Node Allocation 32  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

HNCR-Node Allocation 33  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

HNCR-Node Allocation 34  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

HNCR-Node Allocation 35  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

HNCR-Node Allocation 36  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

HNCR-Node Allocation 37  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

HNCR-Node Allocation 38  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

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 :

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 44 application

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 ! 50