H 501 501 201 grid nodes. CPU Xeon 3.1 GHz (Pipamperone medchemexpress Seconds) RT-LBM 3632.14

H 501 501 201 grid nodes. CPU Xeon 3.1 GHz (Pipamperone medchemexpress Seconds) RT-LBM 3632.14 Tesla GPU V100 (Seconds) 30.26 GPU Speed Up Element (CPU/GPU) 120.The single-thread CPU computation employing a FORTRAN version with the code, that is slightly faster than the code in C, is employed for the computation speed comparison. The speed in the RT-LBM model and MC model in a identical CPU are compared for the initial case only to demonstrate that the MC model is a great deal slower than the RT-LBM. RT-LBM in the CPU is about ten.36 instances more rapidly than the MC model from the initial domain setup making use of the CPU. A NVidia Tesla V100 (5120 cores, 32 GB memory) was run to observe the speed-up elements for the GPU more than the CPU. The CPU made use of for the RT-LBM model computation is definitely an Intel CPU (Intel Xeon CPU at two.3 GHz). For the domain size of 101 101 101, the Tesla V100 GPU showed a 39.24 times speed-up compared with single CPU processing (Table 1). It truly is worthwhile noting the speed-up factor of RT-LBM (GPU) over the MC model (CPU) was 406.53 (370/0.91) occasions if RT-LBM was run on a Tesla V100 GPU. For the significantly larger domain size, 501 501 201 grid nodes (Table 2), the RT-LBM inside the Tesla V100 GPU had a 120.03 instances speed-up compared using the Intel Xeon CPU at two.three GHz. These outcomes indicated the GPU is a lot more successful in speeding up RT-LBM computations when the computational domain is substantially larger, that is consistent with what we discovered using the LBM fluid flow modeling [30]. We’re in the approach of extending our RT-LBM implementation to BMY-14802 References several GPUs that will be important in an effort to manage even larger computational domains. The computational speed-up of RT-LBM making use of the single GPU more than CPU is just not as terrific as in the case of turbulent flow modeling [30], which showed a 200 to 500 speed-Atmosphere 2021, 12,RT-MC RT-MC RT-LBM RT-LBMCPU Xeon 3.1 GHz CPU Xeon three.1 GHz (Seconds) (Seconds) 370 370 35.71 35.Tesla GPU V100 Tesla GPU V100 (Seconds) (Seconds) 0.91 0.GPU Speed Up GPU Speed Up Issue (CPU/GPU) Aspect (CPU/GPU) 406.53 406.53 39.24 39.24 12 ofTable two. Computation time for any domain with 501 501 201 grid nodes. Table 2. Computation time for any domain with 501 501 201 grid nodes.CPU Xeon 3.1 GHz Tesla GPU V100 GPU Speed Up up making use of older NVidiaCPU Xeon 3.1 GHz GPU cards. The purpose is turbulent flow modeling utilizes a timeTesla GPU V100 GPU Speed Up (Seconds) (Seconds) Factor (CPU/GPU) marching transient model, whilst RT-LBM is actually a steady-state model, which calls for numerous (Seconds) (Seconds) Aspect (CPU/GPU) far more iterations to achieve a 3632.14 steady-state remedy. Nevertheless, the GPU speed-up of RT-LBM 3632.14 30.26 120.03 RT-LBM 30.26 120.03 120 instances in RT-LBM is substantial for implementing radiative transfer modeling which can be computationallycode can also be tested for the grid dependency by computing the radiation The model high-priced. The model code is also tested for the grid dependency by computing the radiation field within a modeldomain employing three diverse grid densities. Figure 9 shows the radiation within a identical code is also three various grid densities. by computing the radiation field Precisely the same domain usingtested for the grid dependencyFigure 9 shows the radiation field within a same domain usinggrid densities (10133,, 20133, and 30133 computation grids). The intensities in 3 different grid densities (101 densities. 301 computation grids). The intensities in 3 different three unique grid 201 , and Figure 9 shows the radiation 3 3 3 intensities in criteria had been setto be 10-5 for the error norm.