OpenMP Ninja-OpenMP code parallelizer

Example

#pragma omp parallel for\nfor(int i = 0; i < N; i++) {\n process(i);\n}

Scenario

When a user provides a block of sequential code, OpenMP Ninja analyzes it and identifies loops or sections that can be parallelized using OpenMP. For example, transforming a sequential loop into a parallel loop to distribute workload among multiple threads, enhancing the processing speed of computationally intensive tasks such as matrix multiplication or data sorting.

Example

#pragma omp parallel for reduction(+:sum)\nfor(int i = 0; i < N; i++) {\n sum += compute(i);\n}

Scenario

OpenMP Ninja helps detect potential race conditions in a user's code where multiple threads may attempt to write to the same variable concurrently. It then suggests mitigation strategies, such as the use of the `reduction` clause in OpenMP, to ensure that the operation is performed atomically and correctly, crucial in scenarios like parallel summation or updating shared counters.

Example

#pragma omp parallel sections\n{\n #pragma omp section\n {\n task1();\n }\n #pragma omp section\n {\n task2();\n }\n}

Scenario

This function provides strategies for optimizing parallel code, such as choosing between different types of parallel constructs (e.g., parallel for, sections, single). It helps in determining the best approach based on the workload and data dependency, essential in situations like concurrently executing independent tasks within a larger computation process.