Parallelizing irregular C codes assisted by interprocedural shape analysis

In the new multicore architecture arena, the problem of improving the performance of a code is more in the soft-ware side than in the hardware one. However, optimizing irregular dynamic data structure based codes for such ar-chitectures is not easy, either by hand or compiler assisted. Regarding this last approach, shape analysis is a static tech-nique that achieves abstraction of dynamic memory and can help to disambiguate, quite accurately, memory references in programs that create and traverse recursive data struc-tures. This kind of analysis has promising applicability for accurate data dependence tests in loops or recursive func-tions that traverse dynamic data structures. However, sup-port for interprocedural programs in shape analysis is still a challenge, especially in the presence of recursive func-tions. In this work we present a novel fully context-sensitive interprocedural shape analysis algorithm that supports re-cursion and can be used to uncover parallelism. Our ap-proach is based on three key ideas: i) intraprocedural sup-port based on “Coexistent Links Sets ” to precisely describe the memory configurations during the abstract interpreta-tion of the C code; ii) interprocedural support based on “Recursive Flow Links ” to trace the state of pointers in previous calls; and iii) annotations of the read/written heap locations during the program analysis. We present prelim-inary experiments that reveal that our technique compares favorably with related work, and obtains precise memory abstractions in a variety of recursive programs that create and manipulate dynamic data structures. We have also im-plemented a data dependence test over our interprocedural shape analysis. With this test we have obtained promis-ing results, automatically detecting parallelism in three C codes, which have been successfully parallelized