Applying Edge Partitioning to SPFD's 1 Applying Edge Partitioning to SPFD’s 219B Project Presentation Trevor Meyerowitz Mentor: Subarna Sinha Professor: Robert Brayton 5/16/2000

Applying Edge Partitioning to SPFD's 2 Outline of Presentation A Refresher on SPFD’s Motivation of the Program Example Partitioning Explanation of Different Algorithms Results Future Work Conclusion

Applying Edge Partitioning to SPFD's 3 SPFD’s Sets of Pairs of Functions to be Distinguished Can be represented as graph –Each node represents a care value –Missing nodes are don’t cares –Nodes with different values have an edge between them –For Binary functions this results in a bi-partite graph ABC F(A,B,C) * *

Applying Edge Partitioning to SPFD's 4 SPFD’s When Applied to a multivalued function with k values, a k-partite graph results Sample example –Make 0’s red, 1’s green, 2’s yellow –Add edges between different colors ABC F(A,B,C) *

Applying Edge Partitioning to SPFD's 5 Motivation SPFD’s provide great flexibility but… –This is a non-polynomial problem –Current implementations (bdd, sat) are slow and memory intensive More Heuristics are needed So let’s try formulating this as a graph partitioning and coloring problem –both have many heuristics, but they haven’t really been studied together

Applying Edge Partitioning to SPFD's 6 SPFD Partitioning Initial SPFD’s are propagated back to the local inputs to form their SPFD’s –This can also be viewed as taking the edges from the main and placing them into the graphs of the local inputs Each edge must be propagated back to a subgraph that can distinguish between its vertices

Applying Edge Partitioning to SPFD's 7 SPFD Partitioning The number of colors in each sub-graph represents complexity We want the minimal coloring over all of the sub-graphs Can’t assume k-partite topology of a graph because this is only true for the output nodes

Applying Edge Partitioning to SPFD's 8 SPFD Partitioning Example ABC F(A,B,C) * * * * F(a,b,c) G(a,b)H(b,c) ab c Primary Output F(a,b,c) has two local inputs G(a,b) and H(b,c) Truth Table for F(A,B,C) SPFD Graph Observation: If a local function doesn’t contain any distinguishing primary inputs between the vertices for the given edge, then the edge can’t be placed in the SPFD graph of that function.

Applying Edge Partitioning to SPFD's 9 SPFD Partitioning Example ABC F(A,B,C) * * * * Truth Table for F(A,B,C) SPFD Graph SPFD Graph for G(a,b) SPFD Graph for H(b,c)

Applying Edge Partitioning to SPFD's 10 SPFD Partitioning Example ABC F(A,B,C) * * * * Truth Table for F(A,B,C) SPFD Graph SPFD Graph for G(a,b) SPFD Graph for H(b,c) Edge From 000 to 001. Only distinguished by A so it must be placed in G. Color graph appropiately.

Applying Edge Partitioning to SPFD's 11 SPFD Partitioning Example ABC F(A,B,C) * * * * Truth Table for F(A,B,C) SPFD Graph SPFD Graph for G(a,b) SPFD Graph for H(b,c) Edge From 100 to 101. Only distinguished by C so it must be placed in H. Color graph appropriately.

Applying Edge Partitioning to SPFD's 12 SPFD Partitioning Example ABC F(A,B,C) * * * * Truth Table for F(A,B,C) SPFD Graph SPFD Graph for G(a,b) SPFD Graph for H(b,c) Choose Edge From 000 to 010. It can go into either graph without increasing coloring. So add it to H.

Applying Edge Partitioning to SPFD's 13 SPFD Partitioning Example ABC F(A,B,C) * * * * Truth Table for F(A,B,C) SPFD Graph SPFD Graph for G(a,b) SPFD Graph for H(b,c) Choose Edge From 100 to 010. It can be placed in either w/o increasing coloring. So add it to G.

Applying Edge Partitioning to SPFD's 14 SPFD Partitioning Example ABC F(A,B,C) * * * * Truth Table for F(A,B,C) SPFD Graph SPFD Graph for G(a,b) SPFD Graph for H(b,c) Final Edge From 100 to 010. It can be placed in either w/o increasing coloring. So add it to H.

Applying Edge Partitioning to SPFD's 15 SPFD Partitioning Example (Lessons Learned) With no restrictions the minimally colored partition is obtained by placing all edges into 1 sub-graph Edge Ordering is key to the result –Choose forced edges first –Next choose edges that don’t increase coloring –Finally place the edges that do increase coloring

Applying Edge Partitioning to SPFD's 16 Outline of Presentation A Refresher on SPFD’s Motivation of the Program Example Partitioning Explanation of Different Algorithms Results Future Work Conclusion

Applying Edge Partitioning to SPFD's 17 The Algorithm The Family of Algorithms outlined here can be broken down into these groups. –Pre-processing –Edge Ordering –Edge Placement –Post-processing Currently varied To be varied in the future

Applying Edge Partitioning to SPFD's 18 The Algorithm: Pre-processing Read in initial graph data + build SPFD graph Color the initial SPFD graph using favorite graph coloring heuristic (DSATUR) –This gives information about graph topology that can be used in future steps Read in edge restrictions for each sub-graph and store using hash tables

Applying Edge Partitioning to SPFD's 19 The Algorithm: Edge Ordering One of the most important steps Different ordering techniques explored –Random (the control) –Vertices based on their number of edges and their frequencies –Vertices based on their edges + colors and their frequencies –Edge based on the colors of the 2 vertices + their frequencies

Applying Edge Partitioning to SPFD's 20 The Algorithm: Edge Placement This is the same for all algorithms The steps are: –Edges are traversed based on order obtained –Place all edges that have up no options –Place all edges that don’t add to the coloring of any of the sub-graphs –Place edge(s) that increase colorings by minimal amount. –Repeat steps until all edges are placed

Applying Edge Partitioning to SPFD's 21 The Algorithm: Post- Processing Given greediness of coloring in edge placement, recolor the sub-graphs Then try edge swapping among different subgraphs using below techniques –breaking up maximal cliques –removing edges from minimally used colored vertices –random annealing for ripup and recoloring

Applying Edge Partitioning to SPFD's 22 Results Implementation (2 separate programs) –A program integrated into MVSIS that derives SPFD graphs and sub-graph constraints for a given output on a network and outputs them to 2 output files –Another program that reads in the SPFD graph and constraints and performs the partitioning to get results using the techniques previously described

Applying Edge Partitioning to SPFD's 23 Results (output numbers) Due to Memory required by large graphs only trivial examples have been presented here Graphs with >3000 nodes can’t be generated due to O(n 2 ) space requirements where n is the number of vertices. Bottom 2 examples should be executed before the week is up Ord 0 - no ordering Ord 1 – vertex ordering Ord 2 – edge-color ordering

Applying Edge Partitioning to SPFD's 24 Conclusions This is a good proof of concept Does edge ordering matter? This code uses way too much memory –can only handle small examples –has some memory leaks –Is edge level partitioning too fine a grain? Needs to be compared with other SPFD techniques Probably map-able to other problems

Applying Edge Partitioning to SPFD's 25 Future Work Optimization of current code to handle larger examples Consideration of global SPFD’s + global topology Benchmark vs. current SPFD code Explore more heuristics Get a better idea of runtimes/memory usage