By Ralf Karrenberg

Ralf Karrenberg offers Whole-Function Vectorization (WFV), an procedure that enables a compiler to immediately create code that exploits data-parallelism utilizing SIMD directions. Data-parallel purposes equivalent to particle simulations, inventory choice fee estimation or video deciphering require an identical computations to be played on large quantities of knowledge. with out WFV, one processor center executes a unmarried example of a data-parallel functionality. WFV transforms the functionality to execute a number of situations straight away utilizing SIMD directions. the writer describes a complicated WFV set of rules that features a number of analyses and code new release innovations. He indicates that this method improves the functionality of the generated code in numerous use cases.

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The prototype offers, together with the Intel driver, the currently best performance of any commonly used OpenCL CPU driver. 5 SIMD Property Analyses In this chapter, we describe the heart of the WFV algorithm: a set of analyses that determine properties of a function for a SIMD execution model. The analyses presented in this chapter determine a variety of properties for the instructions, basic blocks, and loops of a scalar source function. 2. They describe the behavior of the function in data-parallel execution.

Various research programs were aimed at parallelizing scientific (Fortran) programs. Especially the analysis and automatic transformation of loop nests has been studied thoroughly [Allen & Kennedy 1987, Darte et al. 2000]. Allen et al. [1983] pioneered control-flow to data-flow conversion to help the dependence analyses to cope with more complex control structures. In our setting, we do not have to perform dependence analysis or find any parallelism; it is implicit in the programming model we consider.

In addition to our approach, they can also analyze affine constraints, yielding more precise results in some situations. Since the divergence analysis also marks branches 6 We have no information about the AMD driver but suspect that no Whole-Function Vectorization is used due to the inferior performance. org/wiki/GalliumCompute/ 36 4 Related Work as uniform or varying, Coutinho et al. uni. 3, the approach does not produce correct results for unstructured control flow. Lee et al. [2013] introduced a scalarizing compiler for GPU kernels that identifies code regions where no threads are inactive.

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