A new algorithm for partial redundancy elimination based on SSA form
Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
Register promotion by sparse partial redundancy elimination of loads and stores
PLDI '98 Proceedings of the ACM SIGPLAN 1998 conference on Programming language design and implementation
Partial redundancy elimination in SSA form
ACM Transactions on Programming Languages and Systems (TOPLAS)
Efficient static single assignment form for predication
Proceedings of the 34th annual ACM/IEEE international symposium on Microarchitecture
Complete inlining of recursive calls: beyond tail-recursion elimination
Proceedings of the 44th annual Southeast regional conference
Induction variable analysis with delayed abstractions
HiPEAC'05 Proceedings of the First international conference on High Performance Embedded Architectures and Compilers
Proceedings of the 10th Workshop on Optimizations for DSP and Embedded Systems
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Loop induction variable canonicalization transforms a loop to use a single primary induction variable that is incremented by one at the end of each iteration. In this process, other induction variables in the loop, called secondary induction variables, are removed from the loop, with their original references expressed in terms of the primary induction variable, and their loop exit values assigned to them after the loop exit. This paper presents a simple and powerful approach to loop induction variable canonicalization in which the problem is broken up into a sequence of steps. The whole canonicalization process is integrated into a global optimizer that builds an SSA representation of the program, performs global optimizations and hands the optimized code on to other components in the compiler that perform loop and instruction level parallelization. By taking advantage of optimizations that will be performed later, each step in the canonicalization process can be made small and efficient. By letting generic parts of the optimizer share in the work, the approach infuses generality into the solution and provides automatic coverage of a wide variety of forms of induction variable occurrences. A number of examples are given to show the effectiveness of the approach. We also provide a set of measurements that shows that the additional compile-time cost incurred due to implementing our approach is small.