[README] Add cuffpa-py library News🔥 (#214)

* Update README.md

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Author: DefTruth

README.md

@@ -12,18 +12,35 @@
   <img src=https://img.shields.io/badge/License-GPLv3.0-turquoise.svg >
  </div>   
 
-<div id="contents"></div>  
 
 📚 **Modern CUDA Learn Notes with PyTorch** for Beginners: It includes **Tensor/CUDA Cores, TF32/F16/BF16/F8**, [📖150+ CUDA Kernels🔥🔥(Easy -> Hard++)](#cuda-kernel) with PyTorch bindings, [📖100+ LLM/VLM/CV/CUDA/CuTe🔥](#my-blogs-part-1) blogs, [📖toy-hgemm⚡️⚡️](./kernels/hgemm) which can achieve `98%~100%` performance of **cuBLAS**, and [📖flash-attention-mma⚡️⚡️](./kernels/flash-attn) using Tensor Cores with pure MMA PTX. Welcome to 🌟👆🏻star this repo to support me, many thanks ~ 🎉🎉
 
-<div id="hgemm-sgemm"></div>  
+## 📖 News 🔥🔥
+<div id="news"></div>  
+
+- [2025-01-08]: [📚Fully QKV Fine-grained Tiling](#mma-tiling-qkv) has been refactored into 🤖[cuffpa-py](https://github.com/DefTruth/cuffpa-py): 📚FFPA - Yet another Faster Flash Prefill Attention with O(1)🎉SRAM complexity for headdim > 256, ~1.5x🎉faster vs SDPA EA.
+- [2024-12-02]: HGEMM MMA kernels has been refactored into 🤖[hgemm-tensorcores-mma](https://github.com/DefTruth/hgemm-tensorcores-mma): ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA PTX and CuTe API.
+
+## 📖 Contents👇👀
+
+<div id="contents"></div>  
+
+- [📖 HGEMM Benchmark](#hgemm-mma-bench)
+- [📖 FA2-MMA Benchmark](#fa-mma-bench)
+- [📖 150+ CUDA Kernels](#cuda-kernel)
+- [📖 100+ Blogs(LLM/CUDA)](#my-blogs-part-1)
+
+## 📖 HGEMM-MMA Benchmark 🎉🎉
+
+<div id="hgemm-mma-bench"></div>  
 
 <div align='center'>
   <img src='https://github.com/user-attachments/assets/71927ac9-72b3-4ce9-b0e2-788b5885bc99' height="170px" width="270px">
   <img src='https://github.com/user-attachments/assets/05ef4f5e-d999-48ea-b58e-782cffb24e85' height="170px" width="270px">
   <img src='https://github.com/user-attachments/assets/9472e970-c083-4b31-9252-3eeecc761078' height="170px" width="270px">
 </div> 
 
+
 Currently, on NVIDIA L20, RTX 4090 and RTX 3080 Laptop, compared with cuBLAS's default Tensor Cores algorithm, the `HGEMM (WMMA/MMA/CuTe)` in this repo (`blue`🔵) can achieve `98%~100%` of its (`orange`🟠) performance. Please check [toy-hgemm library⚡️⚡️](./kernels/hgemm) or [hgemm-tensorcores-mma⚡️⚡️](https://github.com/DefTruth/hgemm-tensorcores-mma) repo for more details.
 
 ![toy-hgemm-library](https://github.com/user-attachments/assets/962bda14-b494-4423-b8eb-775da9f5503d)
@@ -40,6 +57,9 @@ Currently, on NVIDIA L20, RTX 4090 and RTX 3080 Laptop, compared with cuBLAS's d
 |Collective Store (Shfl)|Row Major (NN)|Col Major (TN)| SGEMM FP32/TF32|
 |✔️|✔️|✔️|✔️|
 
+## 📖 FA2-MMA Benchmark 🎉🎉 
+
+<div id="fa-mma-bench"></div>  
 
 I have also implemented **FlashAttention-2** using pure MMA PTX instructions, which supports features such as Multi-Stages, Tile MMA, Tile Warp, Shared KV SMEM, **Fully Shared QKV SMEM**, **Prefetch Q s2r**, **Prefetch K/V g2s**, **QKV Fine-grained Tiling**, Collective Store, etc. Please refer to [flash-attention-mma⚡️⚡️](./kernels/flash-attn) for more details.
 
@@ -131,7 +151,7 @@ __global__ void // Q, K, V, O -> [B, H, N, D]
 flash_attn_mma_stages_split_q_tiling_qk_kernel(half* Q, half* K, half* V, half* O, ...);
 ```
 
-- 📚 Split Q + Fully QKV Fine-grained Tiling (**O(Brx16)~O(1) SRAM** vs FA2 **O(4xBrxd) SRAM**)
+- 📚 Split Q + Fully QKV Fine-grained Tiling (**O(2xBrx16)~O(1) SRAM** vs FA2 **O(4xBrxd) SRAM**)
 
 <div id="mma-tiling-qkv"></div>  
 
@@ -142,7 +162,6 @@ flash_attn_mma_stages_split_q_tiling_qk_kernel(half* Q, half* K, half* V, half*
 __global__ void // Q, K, V, O -> [B, H, N, D]
 flash_attn_mma_stages_split_q_tiling_qkv_kernel(half* Q, half* K, half* V, half* O, ...);
 ```
-
 ## ©️Citations🎉🎉
 
 ```BibTeX
@@ -538,7 +557,7 @@ GNU General Public License v3.0
 
 ## 🎉Contribute ([©️back👆🏻](#contents))
 
-<div id="Contribute"></div>  
+<div id="contribute"></div>  
 
 How to contribute? Star this repo or check [🌤🌤CONTRIBUTE🎉🎉](https://github.com/DefTruth/CUDA-Learn-Notes/issues/50). 
 
@@ -552,7 +571,9 @@ How to contribute? Star this repo or check [🌤🌤CONTRIBUTE🎉🎉](https://
 </a>
 </div>
 
-## 📖 References ([©️back👆🏻](#contents))
+## 📖 References ([©️back👆🏻](#contents)) 
+<div id="ref"></div>  
+
 - [flash-attention-minimal](https://github.com/tspeterkim/flash-attention-minimal)
 - [tiny-flash-attention](https://github.com/66RING/tiny-flash-attention)
 - [cute-gemm](https://github.com/reed-lau/cute-gemm)