Machine-learning-based compilation for thread-level parallelism

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Description

We consider using machine learning techniques for solving compiler challenging
problems arising in multi-threaded architectures, including:
o Automatic extraction of thread-level parallelism
o Efficient compilation of programming models that explicitly expose parallelism
o Use of feedback from the run-time environment for compiler optimization
taking advantage of thread-level parallelism