Research On Benefit Evaluation Techniques In Automatic Vectorization

With the rapid development of multimedia industry including audio/video and image etc recently, the general micro-processor architecture widely supports the multimedia extension technology. The multimedia extension mainly takes advantage of the widely existence of SIMD parallelism in program to achieve performance acceleration. The acceleration is implemented by SIMD instruction sets support for small data type operations and classical complex operations. In recent years, SIMD mechanism is being largely introduced in High Performance Computing and embedded DSP processors, accelerating and promoting the performance. So with these development of computing technology and applications, research on automatic vectorization in compilation technology has been facilitated and developed. However, in realistic research and technique implementation, code vectorization is not always prior to the original scalar code, by bringing in some overhead and is worth doing only when vectorization benefits is greater than costs. In which case can vectorization be profitable? The key content of the thesis encloses this key problem, analyses and explores benefit analysis model in automatic vectorization as well as the methods and implementation technique in order to support compiler optimization accurately and quantitatively.In the thesis, firstly, in order to resolve the performance loss in SIMD auto-vectorization, we define the SIMD profitability evaluation, present the WHIRL intermediate representation which involve in profitability evaluation, establish the profit-cost evaluation model and then find the best pack generation solution.Secondly, he thesis propose the SLP vectorization basic profitability evaluation model based on traditional vectorization as well as the cost evaluation method in the contiguous and misalignment mode, influence vectorization performance including continuous memory access, alignment memory access, splice instruction and register spill etc.Thirdly, loop transformation can have impact on vectorization performance. The thesis discusses different polyhedral representation of loop transformation, research vectorization performance metrics model under polyhedral representation. Based on the current SIMD compiler optimization framework, the thesis brings out the novel optimization procedure to guide loop transformation, present optimal-vectorization-alternatives-search(OVAS) algorithm, clarifies profit-cost model in non-unit stride access, proposes cost evaluation method under misalignment access based on memory access model and establish the SIMD vectorization cost model based on polyhedron representation.At last, we implement the technique mentioned above, make tests on targeted machine, analyze the results and make a further expectation on relating work.