
You will implement a block scaled dual matrix-matrix multiplication kernel with silu activation optimized for NVIDIA B200.
To be explicit, you will be given a tuple of tensors:
```
(a, b1, b2, sfa, sfb1, sfb2, c)
```
where:
* `a` is M x K x L in K-major order in nvfp4(e2m1)
* `b1` is N x K x L in K-major order in nvfp4(e2m1)
* `b2` is N x K x L in K-major order in nvfp4(e2m1)
* `sfa` is M x (K // 16) x L in K-major order in fp8(e4m3fnuz)
* `sfb1` is N x (K // 16) x L in K-major order in fp8(e4m3fnuz)
* `sfb2` is N x (K // 16) x L in K-major order in fp8(e4m3fnuz)
* `c` is M x N x L in fp16

Matrix sizes `M` is divisible by mma_tiler_mn[0], `N` is divisible by mma_tiler_mn[1], `K` is divisible by 256.
The ranking criteria is the geometric mean of the benchmark results.
For the grand price, your kernel will be evaluated against the speed of light analysis
and the solution closest to the speed of light will be awarded the grand price.
```
The speed of light analysis based on the max(FP4 Tensor Core math throughput, DRAM memory throughput) of B200 and tested under 1.5Ghz clock:
  M   N   K   L time[us] 
256 4096 7168 1 4.708
512 4096 7168 1 8.714
256 3072 4096 1 2.125
512 3072 7168 1 6.535
```
