H 501 501 201 grid nodes. CPU Xeon 3.1 GHz (Seconds) RT-LBM 3632.14 Tesla GPU V100 (Seconds) 30.26 GPU Speed Up Issue (CPU/GPU) 120.The 12-Oxo phytodienoic acid Bacterial single-thread CPU computation using a FORTRAN version in the code, that is slightly quicker than the code in C, is used for the computation speed comparison. The speed of your RT-LBM model and MC model within a similar CPU are compared for the initial case only to demonstrate that the MC model is substantially slower than the RT-LBM. RT-LBM inside the CPU is about ten.36 times more rapidly than the MC model in the first domain setup applying the CPU. A NVidia Tesla V100 (5120 cores, 32 GB memory) was run to observe the speed-up things for the GPU more than the CPU. The CPU utilized for the RT-LBM model computation is definitely an Intel CPU (Intel Xeon CPU at 2.3 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 can be worthwhile noting the speed-up factor of RT-LBM (GPU) over the MC model (CPU) was 406.53 (370/0.91) instances if RT-LBM was run on a Tesla V100 GPU. For the much larger domain size, 501 501 201 grid nodes (Table two), the RT-LBM inside the Tesla V100 GPU had a 120.03 instances speed-up compared with all the Intel Xeon CPU at 2.3 GHz. These final results indicated the GPU is even more powerful in speeding up RT-LBM computations when the computational domain is a lot bigger, which is consistent with what we found together with the LBM fluid flow modeling [30]. We’re in the process of extending our RT-LBM implementation to a number of GPUs which will be required in an effort to deal with even bigger computational domains. The computational speed-up of RT-LBM using the single GPU over CPU just isn’t as wonderful as inside 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 Bucindolol medchemexpress Aspect (CPU/GPU) Element (CPU/GPU) 406.53 406.53 39.24 39.24 12 ofTable 2. Computation time to get a domain with 501 501 201 grid nodes. Table two. Computation time for any domain with 501 501 201 grid nodes.CPU Xeon three.1 GHz Tesla GPU V100 GPU Speed Up up utilizing older NVidiaCPU Xeon 3.1 GHz GPU cards. The purpose is turbulent flow modeling uses a timeTesla GPU V100 GPU Speed Up (Seconds) (Seconds) Aspect (CPU/GPU) marching transient model, while RT-LBM is usually a steady-state model, which requires a lot of (Seconds) (Seconds) Factor (CPU/GPU) extra iterations to achieve a 3632.14 steady-state answer. Nevertheless, the GPU speed-up of RT-LBM 3632.14 30.26 120.03 RT-LBM 30.26 120.03 120 times in RT-LBM is considerable for implementing radiative transfer modeling which is computationallycode can also be tested for the grid dependency by computing the radiation The model pricey. The model code is also tested for the grid dependency by computing the radiation field in a modeldomain employing three various grid densities. Figure 9 shows the radiation inside a very same code is also three different grid densities. by computing the radiation field Precisely the same domain usingtested for the grid dependencyFigure 9 shows the radiation field in a very same domain usinggrid densities (10133,, 20133, and 30133 computation grids). The intensities in 3 various grid densities (101 densities. 301 computation grids). The intensities in 3 various 3 different grid 201 , and Figure 9 shows the radiation three 3 3 intensities in criteria were setto be 10-5 for the error norm.
