This shows you the differences between two versions of the page.
Both sides previous revision Previous revision Next revision | Previous revision | ||
fdtd:near-field_to_far_field [2018/01/24 08:14] 127.0.0.1 external edit |
fdtd:near-field_to_far_field [2018/08/30 08:30] pklapetek |
||
---|---|---|---|
Line 8: | Line 8: | ||
Note that on graphics card (GPU), for NFFF computations it is not possible to establish one-to-one correspondence between GPU threads and computational space points as this could lead to access conflicts in the accumulators for far field data (being in the global memory in the present implementation). In GSvit, one thread is responsible for one far field point computation (this approach is effective namely for large numbers of far field points, as e.g. for scattering distribution computations). For small number of far field points, multiple threads are used for accumulation of single far field point. | Note that on graphics card (GPU), for NFFF computations it is not possible to establish one-to-one correspondence between GPU threads and computational space points as this could lead to access conflicts in the accumulators for far field data (being in the global memory in the present implementation). In GSvit, one thread is responsible for one far field point computation (this approach is effective namely for large numbers of far field points, as e.g. for scattering distribution computations). For small number of far field points, multiple threads are used for accumulation of single far field point. | ||
+ | |||
+ | A typical example of using the NFFF algorithm is the [[app:diffraction_grating|Diffraction grating]]. | ||
+ | |||
+ | === Reference === | ||
+ | |||
+ | [1] O. M. Ramahi, Near- and far-field calculations in FDTD simulations using Kirchhoff surface integral representation, IEEE Trans. Antennas and Propagation 45, 753-759 (1997) | ||
+ |