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Rinard, Martin C.
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Bayesian synthesis of probabilistic programs for automatic data modeling
Saad F., Cusumano-Towner M., Schaechtle U., Rinard M., Mansinghka V. Proceedings of the ACM on Programming Languages 3(POPL): 1-32, 2019. Type: Article
Somewhat overshadowed by neural networks (NN) is another thread in machine learning: the Bayesian-based approach. Less data hungry, it also has the promise of being closer to explainable artificial intelligence (XAI), although it hasn&...
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Jun 3 2020
Eliminating synchronization bottlenecks using adaptive replication: a code and design conventions preprocessor for Java
Rinard M., Diniz P. ACM Transactions on Programming Languages and Systems 25(3): 316-359, 2003. Type: Article
Long and dense, this paper contains five important contributions, each of which could be the basis of an interesting paper at a technical conference. As it is, it can be recommended to people seriously studying current problems in the ...
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Jul 16 2003
Effective fine-grain synchronization for automatically parallelized programs using optimistic synchronization primitives
Rinard M. ACM Transactions on Computer Systems 17(4): 337-371, 1999. Type: Article
Rinard proposes a synchronization mechanism to be applied by compilers that automatically parallelize irregular computations. Storage and runtime needed for mutual exclusion locks can be saved by optimistic synchronization. When a proc...
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Mar 1 2000
Eliminating synchronization overhead in automatically parallelized programs using dynamic feedback
Diniz P., Rinard M. ACM Transactions on Computer Systems 17(2): 89-132, 1999. Type: Article
When optimizations are chosen at runtime, information about execution behavior can be exploited to select the best optimization. This paper presents dynamic feedback, a technique for delaying optimization choices until runtime....
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Dec 1 1999
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