feat: add app and docs (#6)

* feat: add app and docs

* readme modified

* pipeline for efficiency measurement

* strategy search

---------

Co-authored-by: Huang Huang(AI) <huang.huang@mthreads.com>

Author: sherry-huang-997

README.md

@@ -1,11 +1,16 @@
 # SimuMax: a static analytical model for LLM distributed training
-
+* [Latest News](#latest-news)
 * [Introduction](#introduction)
 * [Getting Started](#Installation)
 * [Unitest](#Unitest)
 * [TODO](#todo)
 * [Acknowledgements](#acknowledgements)
 * [Community](#Community)
+## Latest News
+- 📣 NEW! [App](./develop/app/README.md) - Add a user application to the SimuMax.
+- 📣 NEW! [Strategy Search](./examples/search/llm_search.py) - Support strategy search.
+- 📣 NEW! [System Config Pipeline](./simu_tools/efficency_test/README.md) - Add a Pipeline to generate system file include computing and communication efficiency.
+
 
 ## Introduction
 SimuMax is a distributed training simulator designed for large-scale language model (LLM) workloads. It leverages a static analytical model to simulate and analyze both performance and memory usage, providing detailed insights into training efficiency without running the actual training process. Based on these analyses, SimuMax helps users explore potential ways to maximize computational efficiency.
@@ -35,15 +40,17 @@ It's appropriate to address various use-cases:
 - [x] Megatron Compatibility: Simplified model migration pipeline
 - [x] Finer-grained selective recompute
 - [x] Efficiency measurement across shapes/layouts
+- [x] Efficiency measurement pipeline for both computing and commucation
+- [x] Strategy Search
 
 
 ### Benchmarks
 Performance of some models on a single node. Llama3-70B was trimmed to 12 layers and DeepSeek-236B was trimmed to 4 layers.
 Details can be found in  [FULL_RESULTS](docs/FULL_RESULTS.md) 
 
 
-
 #### A100-Pcie
+Please refer to [./simu_tools/megatron_scripts/README.md](./simu_tools/megatron_scripts/README.md) for the benchmark script.
 ![alt text](assets/A100-Pcie.png)
 
 
@@ -66,29 +73,53 @@ pip install -v -e .
 
 
 ## Example
+We recommend using our app program to get started with SimuMax with zero cost. Please refer to the [README](./app/README.md) for more details. 
+
 Please refer to the [tutorial](./docs/tutorial.md) for more details.
 
 ```bash
 cd ./examples
 python perf_llama3_8b_tp1_pp2.py
 # The results are stored in the llama3_8b_a100_pcie_bf16 directory
 ```
-The output is as follows:
+The output is as follows, the output details are saved in the ./llama3_8b_a100_pcie_bf16 directory:
 ```
--------------SIMUMAX SUMMARY TP=1,EP=1,PP=2 -------------
-- parallelism = seq4096.mbs1.mbc8.gbs32 tp1.ep1.pp2.dp4.etp1.edp4, world_size:8
+-------------SIMUMAX SUMMARY  llama3_8b(8.03B) TP=1,EP=1,PP=2 -------------
+- parallelism = layer32.dense0.seq4096.mbs1.mbc8.gbs32 tp1.ep1.pp2.dp4.etp1.edp4, world_size:8
 - recompute = No Recompute
 - dtype = bf16, grad_reduce = fp32
 - system = a100_pcie_bf16
-- model = dense
-- mfu = 0.49
-- TFLOPS = 151.59 (tflops=843.3426 T, duration=5.5632 s)
-- TGS_per_gpu = 2945.052828675417
-- peak_alloc_mem = {'first_stage': '50.7760 GB', 'last_stage': '45.1637 GB'}
+- model_type = dense
+· mfu = 0.48
+· TFLOPS = 151.01T (tflops=843.3426 T, duration=5.5848 s)
+· TFLOPS_PER_TOKEN = 0.01T, duration=5.5848 s
+· peak_alloc_mem = {'first_stage': '50.8854 GB', 'last_stage': '45.1637 GB'}
+- peak_alloc_mem_with_reserved = {'first_stage': '54.1334 GB', 'last_stage': '48.0465 GB'}
+- TGS_per_gpu = 2933.6554333899194
+- net = pp_net=intra_node_pcie_8x, tp_net=intra_node_pcie_2x, dp_net=intra_node_pcie_4x, ep_net=intra_node_pcie_2x, etp_net=intra_node_pcie_2x 
+------------------------------------------
 ```
 
-# Unitest
-after clone the repo, plz use "git config core.hooksPath git_hooks" to set commit hook, which will check the perf result in some config is still the same before commit automatically.
+## Strategy search example
+We provide a set of strategy search scripts that run on the [llm_search.py](./examples/search/llm_search.py) model, which can be used to search for the optimal strategy for your model.
+```
+cd examples/llm_search
+python llm_search.py
+```
+
+The example strategy search output results are saved in the directory ./search_deepseek_v3_a100_pcie_bf16 and search_deepseek_v2_a100_pcie_bf16.
+
+
+## Generate system file
+
+If you want to use SimuMax on new equipment, please refer to [./simu_tools/efficency_test/README.md](./simu_tools/efficency_test/README.md) to generate system files.
+
+## Benchmark
+We provide a set of benchmark testing scripts that run on NVIDIA GPUs, which can be used for SimuMax simulation calibration. These scripts enable actual measurements for llama and deepseek models. The benchmark results will be stored in a designated directory. For detailed benchmark procedures and interpretation of results, please refer to [./simu_tools/megatron_scripts/README.md](./simu_tools/megatron_scripts/README.md).
+
+## Notes
+- Currently, all Linear models are forced to perform gradient accumulation fusion.
+
 
 # TODO
 SimuMax is in active development, may contain bugs or incomplete features. Contributions are welcome!
@@ -97,7 +128,6 @@ There are features to be added. Several significants are:
 - More pipeline scheduler
 - Overlap between computation and communication
 - Offloading
-- Strategy search
 - More accurate memory-bound operator simulation
 
 
@@ -120,9 +150,4 @@ If you're passionate about:
 Large-scale models for MoE, Reinforcement Learning, Multi-Modal
 GPU/GPU-Cluster Training/Inference performance optimization
 
-Feel free to reach out to xuerong.huang@mthreads.com.
-
-## Star History
-## Star History
-
-[![Star History Chart](https://api.star-history.com/svg?repos=MooreThreads/SimuMax&type=date&legend=top-left)](https://www.star-history.com/#MooreThreads/SimuMax&type=date&legend=top-left)  
+Feel free to reach out to 1354789084@qq.com.

app/README.md

@@ -0,0 +1,7 @@
+# Get started
+you can run the app with the following command:
+```shell
+cd app
+bash install.sh
+streamlit run streamlit_app.py
+```

app/install.sh

@@ -0,0 +1,7 @@
+# pip install PyQt5 -i https://pypi.tuna.tsinghua.edu.cn/simple
+# sudo apt-get update
+# sudo apt-get install libxcb-xinerama0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-shape0 libxcb-sync1 libxcb-xfixes0 libxcb-xkb1 libxkbcommon-x11-0
+
+
+# pip install flask -i https://pypi.tuna.tsinghua.edu.cn/simple
+pip install streamlit -i https://pypi.tuna.tsinghua.edu.cn/simple