Loop selection for multilevel nested loops is a very difficult problem, for which solutions through the underlying hardware-based loop selection techniques and the traditional software-based static compilation techniques are ineffective. A genetic algorithm- (GA-) based method is proposed in this study to solve this problem. First, the formal specification and mathematical model of the loop selection problem are presented; then, the overall framework for the GA to solve the problem is designed based on the mathematical model; finally, we provide the chromosome representation method and fitness function calculation method, the initial population generation algorithm and chromosome improvement methods, the specific implementation methods of genetic operators (crossover, mutation, and selection), the offspring population generation method, and the GA stopping criterion during the GA operation process. Experimental tests with the SPEC2006 and NPB3.3.1 standard test sets were performed on the Sunway TaihuLight supercomputer. The test results indicated that the proposed method can achieve a speedup improvement that is superior to that by the current mainstream methods, which confirm the effectiveness of the proposed method. Solving the loop selection problem of multilevel nested loops is of great practical significance for exploiting the parallelism of general scientific computing programs and for giving full play to the performance of multicore processors.

MRI (magnetic resonance imaging) technology aims to map the internal structure image of organisms. It is an important application scenario of Non-Uniform Fast Fourier Transform (NUFFT), which can help doctors quickly locate the lesion site of patients. However, in practical application, it has disadvantages such as large computation and difficulty in parallel. Under the architecture of multi-core shared memory, using block pretreatment, color block scheduling NUFFT convolution interpolation offers a parallel solution, and then using a static linked list solves the problem of large memory requirements after the parallel solution on the basis of multithreading to cycle through more source code versions. Then, manual vectorization, such as processing, using short vector components, further accelerates the process. Through a series of optimizations, the final Random, Radial, and Spiral dataset obtained an acceleration effect of 273.8×, 291.8× and 251.7×, respectively.

To address the issues of coarse-grained thread allocation and difficult load balancing in compiler automatic parallelization for processors, this paper proposes a loop-block-level automatic parallelization method for compilers based on an iterative compilation mode, using the SWGCC compiler on the Sunway platform. An automatic parallelization method that independently sets the number of threads for each loop block is designed within the SWGCC, which allocates threads to each parallelizable loop block in the program at a finer granularity. Meanwhile, iterative compilation is combined with a genetic algorithm to iteratively optimize the optimal thread group. Through operations such as the chromosome encoding of thread allocation schemes, weighted mutation operations based on loop execution proportions, and fitness function-guided population evolution, the optimal thread combination is efficiently searched for the loop block thread allocation algorithm. Experiments are validated on the Sunway processor using the SPEC2006 test suite. The results reveal that the loop-block-level compiler automatic parallelization algorithm combined with the evolutionary algorithm achieves a maximum performance score improvement of 19% and an average performance score improvement of 4% compared to the baseline automatic parallelization algorithm in the tests.