Exploiting sparsity in automatic differentiation on multicore architectures We discuss the design, implementation and performance of algorithms suitable for the efficient computation of sparse Jacobian and Hessian matrices using automatic differentiation via operator overloading on multicore architectures. The procedure for exploiting sparsity (for runtime and memory efficiency) in serial computation involves a number of steps. Using nonlinear optimization problems as test cases, we show that the algorithms involved in the various steps can be adapted to multithreaded computations.