Perf: optimize the stream strategy in module_gint

[dzzz2001]

Background

While testing the LCAO GPU version of abacus on an A800 GPU, I noticed a significant difference in performance when running different commands on a machine with only 16 cores. Specifically, the efficiency of the command OMP_NUM_THREADS=4 mpirun -n 4 differs greatly from that of the command OMP_NUM_THREADS=1 mpirun -n 16. The cal_gint efficiency of the latter can be approximately 8 times slower than the former. Below are the runtime statistics I collected(the test case is si256)：

command	cal_gint_vl	cal_gint_rho	cal_gint_force
omp 4 mpirun 4	15.49	14.39	2.30
omp 1 mpirun 16	114.35	113.6	19.25

After reviewing the code, I discovered that the significant difference in performance might be due to the OpenMP thread setting strategy in the GPU code of module_gint：

From the code, it is evident that the grid integration code sets num_stream parallel threads (where num_stream is typically 4) regardless of whether the system has enough cores. This likely results in the number of threads exceeding the available system cores, leading to a loss in efficiency. Therefore, I modified the thread settings here to address this issue.
Additionally, the stream synchronization strategy in module_gint was previously rather coarse. I have now reset the stream synchronization strategy using CUDA events, which has resulted in some performance gains. After completing all modifications, I re-measured the runtime for the same test cases, with the following results:

command	cal_gint_vl	cal_gint_rho	cal_gint_force
omp 4 mpirun 4	10.99	9.97	3.99
omp 1 mpirun 16	28.60	28.60	9.14