Many program optimizations require exact knowledge of the sets of array elements that are referenced in or that ow b e t ween statements or procedures. Some examples are array privatization, generation of communications in distributed memory machines, or compile-time optimization of cache behavior in hierarchical memory machines. Exact array region analysis is introduced in this article. These regions exactly represent the e ects of statements and procedures upon array variables. To represent the ow of these data, we also introduce two n e w types of array region analyses: IN and OUT regions. The intraprocedural propagation is presented, as well as a general linear framework for interprocedural analyses, which handles array reshapes. The intra-and inter-procedural propagation of array regions is implemented in pips, the interprocedural parallelizer of fortran programs developed at Ecole des mines de Paris.

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Many program optimizations require exact knowledge of the sets of array elements that are referenced in or that ow b e t ween statements or procedures. Some examples are array privatization, generation of communications in distributed memory machines, or compile-time optimization of cache behavior in hierarchical memory machines. Exact array region analysis is introduced in this article. These regions exactly represent the e ects of statements and procedures upon array variables. To represent the ow of these data, we also introduce two n e w types of array region analyses: IN and OUT regions. The intraprocedural propagation is presented, as well as a general linear framework for interprocedural analyses, which handles array reshapes. The intra-and inter-procedural propagation of array regions is implemented in pips, the interprocedural parallelizer of fortran programs developed at Ecole des mines de Paris.

International audienceThe current microarchitecture trend leads toward heterogeneity. This evolution is driven by the end of Moore's law and the frequency wall due to the power wall. Moreover, with the spreading of smartphone, some constraints from the mobile world drive the design of most new architectures. An immediate consequence is that an application has to be executable on various targets. Porting and maintaining multiple versions of the code base requires different skills and the efforts required in the process as well as the increased complexity in debugging and testing are time consuming, thus expensive. Some solutions based on compilers emerge. They are based either on directives added to C like in openhmpp or openacc or on automatic solution like pocc, Pluto, ppcg, or Par4All. However compilers cannot retarget in an efficient way any program written in a low-level language such as unconstrained C. Programmers should follow good practices when writing code so that compilers have more room to perform the transformations required for efficient execution on heterogeneous targets. This chapter explores the impact of different patterns used by programmers, and defines a set of good practices allowing a compiler to generate efficient code