Parallelizing the Construction of Static Single Assignment Form

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).
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