Improving Data-flow Analysis with Path Profiles ● Glenn Ammons & James R. Larus ● University of Wisconsin-Madison ● 1998 ● Presented by Jessica Friis

Problem and Approach ● Some paths in a CFG are not run ● A small number of paths often make up a large portion of the runtime ● By duplicating some frequently used paths, we can improve analysis on them

Outline ● Identify “hot paths,” frequently run paths in a CFG by doing a training run on the program ● Duplicate “hot paths” to form a new CFG, the Hot Paths Graph, or HPG ● The extra paths allow more precise analysis. Constant propagation is looked at here ● Reduce the graph to preserve only the valuable solutions for the rest of compilation. ● Implementation and Results

Example CFG Hot paths taken during sample executions ● A, B, C, E, F, H, I, X ● A, B, D, E, F, H ● B, D, E, G, H ● B, D, E, F, H, I, X Use Ball-Larus path profiles Chose paths so that they cover 97% of execution time

Finite Automaton for the path profile ● The retrieval tree is used as a simple representation of the automaton. ● CFG edges label the transitions ● At 13, 15, or 16, a B takes you back to 0 ● Anything else not labeled goes to an error state ● These extra states/edges are left out for readability

Trace CFG and FA to get HPG ● Uses Holley and Rosen's data flow tracing algorithm ● The HPG will have nodes made up of a tuple {v,q} where v is a vertex from the original CFG and q is a state in the automaton ● A worklist algorithm starts with {r,q Ɛ } where r is the starting node in the CFG and q Ɛ is the starting node in the automaton. It follows the edges in the CFG and automaton to create a new HPG

New HPG ● The diagonally striped nodes (A0 and B0) represent the beginnings of the forward paths ● The shaded nodes represent the error states in the automaton (paths that are not 'hot.') ● Haven't lost any information from orginal CFG

New Knowledge with HPG ● At H14, a+b=6 ● At H12, H15, a+b=5 ● At H13, a+b=4 ● At H14&H15, i++ = 1 ● At I17, n is 1 These are new constant results

Eliminate unneeded vertices ● Heuristic algorithm for identifying the most valuable duplicated vertices. ● Others can be reduced/combined to form rHPG for further compilation.

Implementation ● Implemented as two new paths in SUIF compiler, PP and PW ● Compile into intermediate form ● PP pass instrumented intermediate form for path profiling ● Running the result gives us our path profile ● Next the intermediate code and the path profile is run through PW ● PW generates the HPG, discovers new constants, and generates the rHPG ● The output is compiled into an executable

Speedup of SPEC95 benchmarks ● The benchmarks with the most new constants found sped up, the other slowed ● The increase in program size causes the slowdown

Program Costs ● Cost of duplication in size of CFG → HPG – Go increased 184% – All others increasaed an average of 32% ● Cost of duplication in size of CFG → rHPG – Go increased 77% – All others increased less than 10% ● Analysis time – Go took 6 times longer – All others took an average of 61% longer

Contributions ● Shows improvements in the precision of data flow analysis through guided duplication ● Describes how to reduce hot path graphs ● Preserves path profiling information through transformation from CFG to HPG to rHPG ● Applies to constant propagation to show performance increase

Issues ● The cost of analysis is significant – Only should be used just before release ● The increased graph size slows down the running time – Specific reasons are not known, but larger program size is a possibility ● They only tested with a single optimization – Adding in other optimizations may give more speedup gains

Questions?