hipblas, cublas algorithm

The HipBLASLt tuning process and algorithm selection is based on these factors in your data:

```

dev_cap,m,n,k,trans_a,trans_b,type_a,type_b,type_d,bias_type,lda,ldb,ldd,epi,comp,scale,ws_min,ws_max,algo_id,aidx

```

Key parameters:

1. Matrix Dimensions:

- `m,n,k`: Matrix dimensions for GEMM operations

- Example: `904,8192,2048,8192` = matrix sizes

2. Data Types:

- `type_a,type_b`: Input types (float8e4m3, bfloat16)

- `type_d`: Output type (bfloat16)

- `comp`: Computation type (f32)

3. Memory Layout:

- `trans_a,trans_b`: Matrix transposition (T=transposed, N=not)

- `lda,ldb,ldd`: Leading dimensions

4. Algorithm Selection:

- `algo_id`: Specific algorithm identifier

- `aidx`: Algorithm variant index

- workspace limits: `ws_min,ws_max`

The tuning process (`TE_HIPBLASLT_TUNING_RUN_COUNT=30` and `TE_HIPBLASLT_TUNING_ALGO_COUNT=100`) tests different combinations and selects the best based on:

1. Performance (speed)

2. Numerical stability

3. Memory usage

4. Hardware compatibility (dev_cap=904)

This tuning happens in the Tensor Engine (TE) library during the GEMM operations.