Automatic Parallelization for Heterogeneous Embedded Systems

Recent years have seen an increase of heterogeneous architectures combining multi-core CPUs with accelerators such as GPU, FPGA, and Intel Xeon Phi. GPU can achieve significant performance for certain categories of application. Nevertheless, achieving this performance with low-level APIs (e.g. CUDA, OpenCL) requires to rewrite the sequential code, to have a good knowledge of GPU architecture, and to apply complex optimizations that are sometimes not portable. On the other hand, directive-based programming models (e.g. OpenACC, OpenMP) offer a high-level abstraction of the underlying hardware, thus simplifying the code maintenance and improving productivity. They allow users to accelerate their sequential codes on GPU by simply inserting directives. OpenACC/OpenMP compilers have the daunting task of applying the necessary optimizations from the user-provided directives and generating efficient codes that take advantage of the GPU architecture. Although the OpenACC / OpenMP compilers are mature and able to apply some optimizations automatically, the generated code may not achieve the expected speedup as the compilers do not have a full view of the whole application. Thus, there is generally a significant performance gap between the codes accelerated with OpenACC/OpenMP and those hand-optimized with CUDA/OpenCL. To help programmers for speeding up efficiently their legacy sequential codes on GPU with directive-based models and broaden OpenMP/OpenACC impact in both academia and industry, several research issues are discussed in this dissertation. We investigated OpenACC and OpenMP programming models and proposed an effective application parallelization methodology with directive-based programming approaches. Our application porting experience revealed that it is insufficient to simply insert OpenMP/OpenACC offloading directives to inform the compiler that a particular code region must be compiled for GPU execution. It is highly essential to combine offloading directives with loop parallelization constructs. Although current compilers are mature and perform several optimizations, the user may provide them more information through loop parallelization constructs clauses in order to get an optimized code. We have also revealed the challenge of choosing good loop schedules. The default loop schedule chosen by the compiler may not produce the best performance, so the user has to manually try different loop schedules to improve the performance. We demonstrate that OpenMP and OpenACC programming models can achieve best performance with lesser programming effort, but OpenMP/OpenACC compilers quickly reach their limit when the offloaded region code is computed/memory bound and contain several nested loops. In such cases, low-level languages may be used. We also discuss pointers aliasing problem in GPU codes and propose two static analysis tools that perform automatically at source level type qualifier insertion and scalar promotion to solve aliasing issues.