P.: When polyhedral transformations meet SIMD code generation

Data locality and parallelism are critical optimization objectives for performance on modern multi-core machines. Both coarse-grain par-allelism (e.g., multi-core) and fine-grain parallelism (e.g., vector SIMD) must be effectively exploited, but despite decades of progress at both ends, current compiler optimization schemes that attempt to address data locality and both kinds of parallelism often fail at one of the three objectives. We address this problem by proposing a 3-step framework, which aims for integrated data locality, multi-core parallelism and SIMD exe-cution of programs. We define the concept of vectorizable codelets, with properties tailored to achieve effective SIMD code generation for the codelets. We leverage the power of a modern high-level transformation framework to restructure a program to expose good ISA-independent vectorizable codelets, exploiting multi-dimensional data reuse. Then