👉 Races are a parallel computing paradigm designed to efficiently distribute and synchronize computations across multiple processing units, such as GPUs or CPUs, to solve large-scale problems. In a race, each processing unit independently computes a portion of the overall task and then compares the results to determine the correct solution. This approach is particularly effective for tasks that can be decomposed into independent subtasks, like matrix multiplications or solving linear systems. By using a race-based framework, such as CUDA for NVIDIA GPUs or OpenMP for multi-core CPUs, developers can harness the power of many cores simultaneously, significantly speeding up computations and enabling the solution of complex problems that would be infeasible with sequential processing. This method not only optimizes performance but also simplifies parallel programming by abstracting away some of the complexities involved in managing concurrent computations.
