A performance- and energy-oriented extended tuning process for time-step-based scientific applications

The Journal of Supercomputing - Scientific application codes are often long-running time- and energy-consuming parallel codes, and the tuning of these methods towards the characteristics of a...     

Online auto-tuning for the time-step-based parallel solution of ODEs on shared-memory systems

This article considers automatic performance tuning of time-step-based parallel solution methods for initial value problems (IVPs) of systems of ordinary differential equations (ODEs). We apply auto-tuning to the parallel execution of a class of explicit predictor–corrector (PC) methods of Runge–Kutta (RK) type on shared-memory architectures. The performance of parallel multi-threaded implementation variants of these methods depends on various factors only known at runtime, for example, the coupling structure of the ODE system to be solved, the memory access pattern resulting from this coupling structure, and the number of threads executing the program.