Making context-sensitive points-to analysis with heap cloning practical for the real world
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Dynamic performance tuning of word-based software transactional memory
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Profiling Java programs for parallelism
IWMSE '09 Proceedings of the 2009 ICSE Workshop on Multicore Software Engineering
Sambamba: runtime adaptive parallel execution
Proceedings of the 3rd International Workshop on Adaptive Self-Tuning Computing Systems
Tightfit: adaptive parallelization with foresight
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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How can we exploit a microprocessor as efficiently as possible? The "classic" approach is static optimization at compile-time, optimizing a program for all possible uses. Further optimization can only be achieved by anticipating the actual usage profile: If we know, for instance, that two computations will be independent, we can run them in parallel. In the Sambamba project, we replace anticipation by adaptation. Our runtime system provides the infrastructure for implementing runtime adaptive and speculative transformations. We demonstrate our framework in the context of adaptive parallelization. We show the fully automatic parallelization of a small irregular C program in combination with our adaptive runtime system. The result is a parallel execution which adapts to the availability of idle system resources. In our example, this enables a 1.92 fold speedup on two cores while still preventing oversubscription of the system.