Parallelizing the Construction of Static Single Assignment Form

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app # methods (% change) mean length (% change) max length montecarlo 179 (+1%) 20.82 (+18%) 231 raytracer 69 (+6%) 40.31 (+32%) 406 search 32 (+10%) 89.87 (+4%) 484 Table 3: Method statistics for selected benchmarks with inlining applied even in cases where the program was originally written as a collection of small functions. 2. Another trend is the increasing amount of source code that is produced by automatic code generators. more source code is produced by code generators. Such programs do not necessarily have the same properties as human written code. Methods are often larger, and loops deeper. 3. There is an increasing need for interprocedural data flow analysis. In the context of this paper, the SSA construction operates at a single-procedure level. However SSA does extend naturally to the whole-program scope [19, 29]. Again, our parallel SSA construction techniques would be applicable to such large interprocedural control flow graphs. Exploiting all the available parallel hardware for the analysis of such large SSA-based program representations is therefore of great importance in reducing compile times. In overall terms, the largest single contributor to compilation time is parsing. Researchers are working on parallelizing the parsing process [24]. We anticipate that every significant phase of next-generation compilers should be engineered to take advantage of underlying hardware parallelism, to achieve scalability. This includes the data flow analysis phase, of which SSA construction is generally a key initial step. One might argue that it would be better to perform sequential (i.e. single-threaded) SSA-construction on multiple methods in parallel, rather than parallelizing SSA construction for a single method. However we note that in a JIT compiler or an interactive compilation server [16], requests for optimizing compilation of methods will occur sporadically, unpredictably and not necessarily concurrently. Where possible, it would be good to parallelize both individual method SSA construction, and handle multiple methods concurrently. We have only tackled the former problem in this paper. Also, in a JIT context, response time is often more important than total performance: so reducing the analysis time for the largest methods will improve worst case response time, even if it does not improve performance on the small methods (where performance is already adequate). 7. REFERENCES [1] Ashby, S., Eulisse, G., Schmid, S., Tuura, L.: Parallel compilation of CMS software. In: Proc. Computing in High Energy and Nuclear Physics Conference (CHEP) (2004) [2] Baalbergen, E.: Design and implementation of parallel make. Computing Systems 1(2), 135–158 (1988) [3] Bilardi, G., Pingali, K.: Algorithms for computing the static single assignment form. 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