PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
Run-time methods for parallelizing partially parallel loops
ICS '95 Proceedings of the 9th international conference on Supercomputing
On the Automatic Parallelization of the Perfect Benchmarks®
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
The R-LRPD Test: Speculative Parallelization of Partially Parallel Loops
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Principles of Speculative Run-Time Parallelization
LCPC '98 Proceedings of the 11th International Workshop on Languages and Compilers for Parallel Computing
SmartApps: An Application Centric Approach to High Performance Computing
LCPC '00 Proceedings of the 13th International Workshop on Languages and Compilers for Parallel Computing-Revised Papers
Techniques for Reducing the Overhead of Run-Time Parallelization
CC '00 Proceedings of the 9th International Conference on Compiler Construction
SmartApps: middle-ware for adaptive applications on reconfigurable platforms
ACM SIGOPS Operating Systems Review
Optimistic parallelism requires abstractions
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Rewriting procedures for batched bindings
Proceedings of the VLDB Endowment
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Current parallelizing compilers treat while loops and do loops with conditional exits as sequential constructs because their iteration space is unknown. Because these types of loops arise frequently in practice, we have developed techniques that can automatically transform them for parallel execution. We succeed in parallelizing loops involving linked lists traversals-something that has not been done before. This is an important problem since linked list traversals arise frequently in loops with irregular access patterns, such as sparse matrix computations. The methods can even be applied to loops whose data dependence relations cannot be analyzed at compile-time. Experimental results on loops from the PERFECT Benchmarks and sparse matrix packages show that these techniques can yield significant speedups.