Titlebook: Languages and Compilers for Parallel Computing; 33rd International W Barbara Chapman,José Moreira Conference proceedings 2022 Springer Natu

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An Affine Scheduling Framework for Integrating Data Layout and Loop Transformations memory layouts of variables. There is a mutually dependent relationship between the two, i.e., the best statement execution order can depend on the underlying data layout and vice versa. Existing approaches have typically addressed this inter-dependency by picking a specific phase order, and can th

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Guiding Code Optimizations with Deep Learning-Based Code Matchingrmance modeling of nontrivial computations typically requires significant expertise and human effort. Moreover, even when performed by experts, it is necessarily limited in scope, accuracy, or both. In this paper, we are building the Meliora framework for machine learning-based performance model gen

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Expanding Opportunities for Array Privatization in Sparse Computationsompiler-based data dependence analysis of sparse computations needed for automatic parallelization is difficult due to usage of indirect memory accesses through index arrays, e.g. . in .[.[.]], in these computations. One use of such data dependence analysis is to find opportunities for array privati

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Using Hardware Transactional Memory to Implement Speculative Privatization in OpenMPver, the reuse of shared scalar or array variables introduces constraints (false dependences or false sharing) that obstruct efficient speculative parallelization. Speculative privatization relieves these constraints by creating speculatively private data copies for each transaction thus enabling sc

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Improving Fortran Performance Portabilityslate standard Fortran code to various parallel programming languages in Fortran and C/C++ to enable running on a wide variety of GPUs and CPUs. The translation is performed using the full syntax parsing capabilities of the LFortran compiler, a research compiler currently in development. Using the A

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