Parallelization and vectorization of ROOT fitting classes

We introduce parallelization and vectorization in ROOT mathematical and statistical libraries in order to take full advantage of new computer architectures and to maximize the CPU usage with an increasing amount of data to analyze. As part of this effort, we define in ROOT new generic classes supporting a task-based parallelization mode, which can be used for a wide range of computational tasks in the field of High Energy Physics. We also include support for different SIMD libraries, by including in ROOT a new generic library for vectorization. These different parallelization tools are applied together when parallelizing the minimization process for solving fitting problems. We report on the improvements obtained by adding the support for SIMD vectorization and multithreaded parallelization when fitting ROOT histograms and datasets represented by ROOT trees.