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+# Introduction
+
+This markdown file is used to demonstrate the change i made compare to original prima.cpp
+
+## Configurable Network Port Options
+
+Enable separate configuration of local listening ports and remote node ports for distributed inference, supporting complex networking scenarios including port forwarding.
+
+- commits (1e7ae71)
+
+### New CLI Arguments
+
+**Local Listening Ports:**
+- `--data_port PORT`: Local port where this node listens for data communications
+- `--signal_port PORT`: Local port where this node listens for signal communications
+
+**Remote Node Ports (ports that other nodes are listening on):**
+- `--master_data_port PORT`: Port that master node is listening on for data
+- `--next_node_data_port PORT`: Port that next node is listening on for data  
+- `--next_node_signal_port PORT`: Port that next node is listening on for signals
+
+### Port Configuration Logic
+
+Prima.cpp separates local binding from remote addressing:
+- **Local ports** (`--data_port`, `--signal_port`): Where this node listens for incoming connections
+- **Remote ports** (`--master_*_port`, `--next_node_*_port`): Which ports other nodes are listening on
+
+This design enables port forwarding scenarios where nodes may listen on different ports locally but are accessed through forwarded ports.
+
+### Usage Examples
+
+```shell
+# On rank 0 (master server), run:
+./llama-server -m gguf/qwen2.5-7b-instruct-q4_k_m-00001-of-00002.gguf --host 0.0.0.0 --port 8080 --world 2 --rank 0 --data_port 9000 --signal_port 9001 --master 127.0.0.1 --master_data_port 9000 --next 192.168.4.10 --next_node_data_port 9000 --next_node_signal_port 9001
+
+# On rank 1 (worker node), run:
+./llama-cli -m gguf/qwen2.5-7b-instruct-q4_k_m-00001-of-00002.gguf --world 2 --rank 1 --data_port 9000 --signal_port 9001 --master 192.168.4.9 --master_data_port 9000 --next 192.168.4.9 --next_node_data_port 9000 --next_node_signal_port 9001
+```
+
+This provides complete control over both local binding and remote connectivity for distributed model inference.
+
+## Rank-Specific GGUF Split Loading
+
+Enable selective loading of GGUF split files based on layer assignment, reducing memory usage and download requirements for distributed inference.
+
+- commits (f1f7e37)
+
+### New CLI Argument
+
+- `--splits SPLIT_LIST`: Comma-separated list of split indices to load (e.g., "0,2,3")
+
+### Split Loading Logic
+
+When models are split using `gguf-split`:
+
+```shell
+./gguf-split --split-max-tensors 128 llama.gguf llama.gguf
+```
+
+You get multiple files:
+```
+llama.gguf-00001-of-00004.gguf
+llama.gguf-00002-of-00004.gguf  
+llama.gguf-00003-of-00004.gguf
+llama.gguf-00004-of-00004.gguf
+```
+
+Unlike `llama.cpp` which loads all splits, `prima.cpp` enables selective loading based on layer assignment:
+
+- Each rank only loads splits containing its assigned layers
+- Reduces memory footprint and network transfer
+- Eliminates need to download unused model segments
+
+### Layer Assignment Policy
+
+Prima.cpp enforces a specific layer distribution:
+
+- **Rank 0 (master)**: Always owns embedding layers + final transformer blocks
+- **Rank 1+ (workers)**: Own contiguous middle transformer blocks
+- **Rank 1**: Always starts from layer 0
+
+### Split Selection Strategy
+
+For `--n-layer-window "8,8"` with `--world 2`:
+
+- **Rank 0**: Owns embedding + layers 8-15 → loads splits 0,2,3  
+- **Rank 1**: Owns layers 0-7 → loads splits 0,1
+
+### Usage Examples
+
+```bash
+# Rank 0 (master) loads splits containing embedding + final layers
+./llama-cli -m llama.gguf-00001-of-00004.gguf --splits 0,2,3 --world 2 --rank 0 --n-layer-window "8,8"
+
+# Rank 1 (worker) loads splits containing initial layers  
+./llama-cli -m llama.gguf-00001-of-00004.gguf --splits 0,1 --world 2 --rank 1 --n-layer-window "8,8"
+```
+
+### Error Handling
+
+- Missing required tensors trigger immediate error with split suggestions
+- Explicitly set `--n-layer-window` to ensure predictable layer-to-split mapping
+- Validate split coverage before distributed execution
+
+This optimization significantly reduces resource requirements for large model inference across multiple nodes.
+
+## Communication and Compute Logging
+
+Enable detailed timestamped logging of inter-node communication and computation phases for performance analysis and debugging distributed inference.
+
+- commits (eb0cac1, adad23d, a399e49)
+
+### Logging Categories
+
+**Communication Logs:**
+- `[comm][start/end][send_tensors]`: Tensor transmission to next node/master
+- `[comm][start/end][recv_tensors]`: Tensor reception from other nodes
+
+**Compute Logs:**  
+- `[compute][start/end][transformer_blocks]`: Layer computation timing
+- `[compute][start/end][output_linear]`: Final output layer processing
+
+### Log Format
+
+Each log entry contains:
+```
+[rank][timestamp][category][phase][operation][batch_info, description]
+```
+
+- **rank**: Node rank identifier (0 for master, 1+ for workers)
+- **timestamp**: ISO 8601 timestamp with millisecond precision
+- **category**: `comm` for communication, `compute` for computation
+- **phase**: `start` or `end` of operation
+- **operation**: Specific function being logged
+- **batch_info**: `sbatch_tokens` and `ubatch_tokens` counts
+- **description**: Human-readable operation description
+
+### Use Cases
+
+- **Performance profiling**: Identify communication vs compute bottlenecks
+- **Gantt chart generation**: Visualize parallel execution timeline
+- **Debugging**: Track tensor flow and synchronization issues
+- **Load balancing**: Analyze per-node utilization patterns
+
+### Example Output
+
+```
+[0][2025-08-02T12:06:35.050Z][comm][start][send_tensors][sbatch_tokens: 0, ubatch_tokens: 512, send the result to the next node or the master]
+[0][2025-08-02T12:06:35.051Z][comm][end][send_tensors][sbatch_tokens: 0, ubatch_tokens: 512, send the result to the next node or the master]
+[0][2025-08-02T12:06:35.051Z][comm][start][recv_tensors][sbatch_tokens: 0, ubatch_tokens: 512, receive data from other nodes]
+[0][2025-08-02T12:06:35.160Z][comm][end][recv_tensors][sbatch_tokens: 0, ubatch_tokens: 512, receive data from other nodes]
+[0][2025-08-02T12:06:35.162Z][compute][start][transformer_blocks][sbatch_tokens: 0, ubatch_tokens: 512]
+[0][2025-08-02T12:06:35.165Z][compute][end][transformer_blocks][sbatch_tokens: 0, ubatch_tokens: 512]
+[0][2025-08-02T12:06:35.190Z][compute][start][output_linear][sbatch_tokens: 0, ubatch_tokens: 512]
+[0][2025-08-02T12:06:35.259Z][compute][end][output_linear][sbatch_tokens: 0, ubatch_tokens: 512]
+```
+
+This logging system provides comprehensive visibility into distributed inference execution patterns.
+
+## Communication Tensor Dumping
+
+Enable binary dumping of inter-node tensor communications with comprehensive metadata for detailed analysis of distributed inference data flow.
+
+- commits (e48e955)
+
+### New CLI Argument
+
+- `--dump-folder FOLDER`: Specify directory to dump network communication tensors (disabled if unset)
+
+### Binary Dump Format
+
+Each tensor file contains structured binary data with complete shape metadata:
+
+```
+[1 byte: element_type][8 bytes: n_embed][8 bytes: n_tokens][8 bytes: tensor_size][tensor_data]
+```
+
+**Header Fields:**
+- `element_type`: Data type identifier (FLOAT32 = 0, extensible for other types)
+- `n_embed`: Embedding dimension/width (uint64_t)  
+- `n_tokens`: Token count/height (uint64_t)
+- `tensor_size`: Total tensor data size in bytes (uint64_t)
+- `tensor_data`: Raw tensor bytes in native format
+
+### Dumping Operations
+
+**Send Path (`llama_send_tensors`):**
+- Files: `send_{counter}.bin`
+- Shape extracted from `tensors->sub_gf_out->ne[]`
+- Captures outbound tensor data before network transmission
+
+**Receive Path (`llama_recv_tensors`):**  
+- Files: `recv_{counter}.bin`
+- Shape extracted from `dims[]` parameter
+- Captures inbound tensor data after network reception
+
+### Usage Examples
+
+```bash
+# Enable tensor dumping for distributed inference
+./llama-cli --model model.gguf --dump-folder ./tensor_dumps --world 2 --rank 0 [other_args]
+```
+
+This feature provides comprehensive visibility into the tensor communication layer for distributed model analysis.
+
+## Multi-Node Perplexity Evaluation
+
+Enable distributed perplexity evaluation using `llama-perplexity` as master coordinator with `llama-cli` worker nodes for large model assessment.
+
+- commits (c949e53)
+
+### Distributed Perplexity Architecture
+
+**Master Node (Rank 0):**
+- Runs `llama-perplexity` binary
+- Coordinates text processing and perplexity calculations
+- Manages input text file distribution and result aggregation
+
+**Worker Nodes (Rank 1+):**
+- Run `llama-cli` binaries in distributed mode
+- Process assigned model layers during evaluation
+- Participate in tensor communication pipeline
+
+### Supported Evaluation Features
+
+- **Text file processing**: Load evaluation datasets via `-f` parameter
+- **Distributed inference**: Leverage multi-node processing for large models
+- **Standard perplexity metrics**: Calculate perplexity scores across distributed architecture
+
+### Usage Examples
+
+**2-Node Setup:**
+```shell
+# On rank 0 (master evaluator), run:
+./llama-perplexity -m download/qwq-32b-q4_k_m.gguf -f wikitext-2-raw/wiki.test.raw --world 2 --rank 0 --master 192.168.1.2 --next 192.168.1.3
+
+# On rank 1 (worker node), run:
+./llama-cli -m download/qwq-32b-q4_k_m.gguf --world 2 --rank 1 --master 192.168.1.2 --next 192.168.1.2
+``
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