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Hi,
recently I do some CUDA acceleration of the schur complement in ceres, which compute Hsc matrix from Jacobian matrix.
and I encounter some problem about bad performance of CUDA global memory random access in Hsc matrix.
I read the procedure in this project which perform sparse-sparse matrix multiply part, (something like H_lp'Hpl in this code).
It seems that you pre-calculate some [i, j, k] triplets, which is the addresses of matrix mutiply operations, sort them. and perform small matrix multiplication via cuda kernel function.
I was wondering, is this method also suffer from the bad performance of Hsc matrix memory access? and How to tackle it?
The text was updated successfully, but these errors were encountered:
Hi,
recently I do some CUDA acceleration of the schur complement in ceres, which compute Hsc matrix from Jacobian matrix.
and I encounter some problem about bad performance of CUDA global memory random access in Hsc matrix.
I read the procedure in this project which perform
sparse-sparse matrix multiply
part, (something like H_lp'Hpl in this code).It seems that you pre-calculate some [i, j, k] triplets, which is the addresses of matrix mutiply operations, sort them. and perform small matrix multiplication via cuda kernel function.
I was wondering, is this method also suffer from the bad performance of Hsc matrix memory access? and How to tackle it?
The text was updated successfully, but these errors were encountered: