Of course, the computational speed on an extremely large mesh would be very low. The Nvidia Tesla K20 GPU has 5 GB, and therefore renders the use of up to 105 million simulation nodes possible.Īt the same time, Intel processors used in the test can exploit up to 32 GB, which corresponds to 690 million nodes (assuming that other processes consume 1 GB in total). The amount of RAM on the Nvidia GTX 560 Ti and the Nvidia GTX 660 Ti graphics cards is 2 GB, which makes them suitable for modeling heat transfer problems with a number of units up to 42 million. The result was influenced by a higher frequency of CUDA cores in the Nvidia GTX 560 Ti, as well as solver optimization for this graphics card. The Nvidia GTX 560 Ti appeared to be ahead of the Nvidia GTX 660 Ti in 4 out of 5 tests, despite the fact that it has less CUDA cores. The graph below demonstrates the absolute time in computational minutes for temperature over 1 year in a cubic area of 20x20x20 meters.
The histogram shows the accelaration of test computations on different multicore processors for models with different dimensions relative to the Core i7. The computation assignment was the three-dimensional non-stationary two-phase Stefan problem (nonlinear heat transfer).įor efficient parallelization, the code of the computational mechanism was written in two versions: in the C ++ language with the Open MP parallelization directives support for Intel CPUs, and in the CUDA C ++ language for Nvidia GPUs. In order to carry out a detailed comparison of the computational speeds on systems with different configurations, Simmakers prepared special tests that included the following model dimensions: The results have exceeded all expectations - the computation for an ice wall around the Fukushima Nuclear Power Plant was completed in under 6 minutes.
Simmakers Ltd, together with Nvidia and Forsite have tested the speed of a GPU version of the Frost 3D Universal software package for solving heat transfer problems on the Nvidia Tesla K20 system.