José M. Andión

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The widespread use of multicore processors is not a consequence of significant advances in parallel programming. In contrast, multicore processors arise due to the complexity of building power-efficient, high-clock-rate, single-core chips. Automatic parallelization of sequential applications is the ideal solution for making parallel programming as easy as(More)
Complete comprehension of loop codes is desirable for a variety of program optimizations. Compilers perform static code analyses and transformations, such as loop tiling or memory partitioning, by constructing and manipulating formal representations of the source code. Runtime systems observe and characterize application behavior to drive resource(More)
The use of GPUs for general purpose computation has increased dramatically in the past years due to the rising demands of computing power and their tremendous computing capacity at low cost. Hence, new programming models have been developed to integrate these accelerators with high-level programming languages, giving place to heterogeneous computing(More)
This manuscript summarizes the main ideas introduced in [1]. We propose a compiler that automatically transforms a sequential application into a parallel counterpart for multicore processors. It is based on an intermediate representation, named KIR, which exposes multiple levels of parallelism and hides the complexity of the implementation details thanks to(More)
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