Add reduce_scatter and All_gather benchmark

examples/22_rs_rmsnorm_fp8quant_ag/README.md

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+<!--
+SPDX-License-Identifier: MIT
+Copyright (c) 2025 Advanced Micro Devices, Inc. All rights reserved.
+-->
+
+# Reduce-Scatter → RMSNorm → FP8 Quantization → All-Gather benchmark using Iris
+This example implements a complete tensor processing pipeline across multiple GPUs:
+
+1. **Reduce-Scatter**: Sum tensors across all GPUs and distribute shards
+2. **RMSNorm**: Apply Root Mean Square normalization to each shard
+3. **FP8 Quantization**: Quantize to 8-bit floating point (optional)                                                                                                      4. **All-Gather**: Reconstruct the full tensor across all GPUs (optional)
+
+## Usage
+
+```terminal
+python benchmark.py --num_rows 8192 --num_cols 7168 --num_ranks 8 --benchmark --fp8_out --all_gather --BLOCK_M 16 --BLOCK_N 64 --num_warps 16 --num_stages 4 --waves_per_eu 4 --rmsnorm_block_size 1024 --rmsnorm_num_warps 8 --rmsnorm_num_prgms 1024 --rmsnorm_waves_per_eu 2 --fp8_block_m 64 --fp8_block_n 64 --fp8_num_warps 4 --fp8_num_stages 2 --fp8_waves_per_eu 2 --ag_block_m 64 --ag_block_n 64 --ag_num_warps 8 --ag_num_stages 3 --ag_waves_per_eu 2 --validate
+```
+
+The benchmark measures the bandwidth of each GPU receiving data from all other GPUs. Each GPU performs a load operation from every other GPU in the system, and the total bandwidth is calculated based on the total amount of data received and the time taken.