Program package MPGOS: challenges and solutions during the integration of a large number of independent ODE systems using GPUs. Challenges and efficient solution techniques during the integration of a large number of independent ordinary differential equations (ODEs) using the massively parallel architecture of graphics processing units (GPUs) are presented. One of the main difficulties is the minimisation of the memory transactions through the PCI-E bus between the host (CPU) and the device (GPU) required frequently, for instance, during the calculation of the Lyapunov exponent, winding number or maximum response diagram. The second difficulty is the minimisation of the slow global memory transactions and memory usage by exploiting the memory hierarchy of the GPU architecture. Finally, a good GPU solver has to treat the possible asynchronous features of the ODE systems efficiently; for instance, event detection occurring at distinct time instances or handling the orders of magnitude difference in the required number of time steps of the different ODE systems. The program package MPGOS (written in C++ and CUDA C software environments) can address the aforementioned issues easily via the addition of user-defined functions that must be implemented similarly to the right-hand side of the system; via the possibility of the definition of shared parameters common to all instances of the independent ODE systems; via user-programmable parameters to store only the desired properties of the trajectories; and via an easy was to overlap GPU and CPU computations. This paper focuses on the detailed description of the implementation strategies of the program package.
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References in zbMATH (referenced in 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Nagy, Dániel; Plavecz, Lambert; Hegedűs, Ferenc: The art of solving a large number of non-stiff, low-dimensional ordinary differential equation systems on GPUs and CPUs (2022)
- Hegedűs, Ferenc: Program package MPGOS: challenges and solutions during the integration of a large number of independent ODE systems using GPUs (2021)
- Hegedűs, F.; Krähling, P.; Aron, M.; Lauterborn, W.; Mettin, R.; Parlitz, U.: Feedforward attractor targeting for non-linear oscillators using a dual-frequency driving technique (2020)