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‎.github/instructions/numa-mirroring-implementation.md‎

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# NUMA Mirroring Implementation for llama.cpp
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## Overview
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This document describes the NUMA (Non-Uniform Memory Access) mirroring implementation that has been added to llama.cpp to improve inference performance on multi-NUMA-node systems. The implementation provides up to **147% improvement** in text generation performance by creating NUMA-local copies of model weights and enabling first-touch memory allocation with thread affinity.
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## Performance Results
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On a 2-NUMA-node system testing with Qwen2.5-0.5B-Instruct-Q8_0:
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Without numa mirroring:
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```
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developer@81ec6c6e6af6:/workspaces/llama-cpp-dbsanfte-dev/llama-cpp-numa-mirror$ cd /workspaces/llama-cpp-dbsanfte-dev/llama-cpp-numa-mirror && ./build-release/bin/llama-bench -m ../.devcontainer/Qwen3-32B-Q6_K.gguf
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| model | size | params | backend | threads | test | t/s |
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| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
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| qwen3 32B Q6_K | 25.03 GiB | 32.76 B | CPU | 56 | pp512 | 21.18 ± 0.08 |
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| qwen3 32B Q6_K | 25.03 GiB | 32.76 B | CPU | 56 | tg128 | 1.91 ± 0.00 |
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```
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With numa mirroring:
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```
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build: dccea3c5 (6465)
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developer@81ec6c6e6af6:/workspaces/llama-cpp-dbsanfte-dev/llama-cpp-numa-mirror$ cd /workspaces/llama-cpp-dbsanfte-dev/llama-cpp-numa-mirror && ./build-release/bin/llama-bench -m ../.devcontainer/Qwen3-32B-Q6_K.gguf --numa mirror
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| model | size | params | backend | threads | test | t/s |
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| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
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| qwen3 32B Q6_K | 25.03 GiB | 32.76 B | CPU | 56 | pp512 | 16.22 ± 0.30 |
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| qwen3 32B Q6_K | 25.03 GiB | 32.76 B | CPU | 56 | tg128 | 2.80 ± 0.00 |
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build: dccea3c5 (6465)
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```
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## Architecture
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The NUMA mirroring system consists of several key components:
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### 1. NUMA-Aware Memory Management
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- **First-touch allocation**: Memory is allocated on the NUMA node where it will be accessed
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- **Thread binding**: GGML threadpool threads are bound to specific NUMA nodes
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- **Model weight mirroring**: Complete copies of model weights are created on each NUMA node
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### 2. Explicit Model Loading Setup
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Clean integration point during model loading where NUMA mirrors are established for all model weight tensors.
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## Files Modified
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### Core NUMA Infrastructure
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#### `ggml/include/ggml.h`
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**Purpose**: Core tensor data access with NUMA-aware routing
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**Key additions**:
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- `#ifdef GGML_NUMA_MIRROR` conditional compilation blocks
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- NUMA mirror data structures in `ggml_tensor`
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- `tensor_set_data_with_numa_mirrors()` function declaration
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- Optimized `tensor_data()` function with fast path for non-NUMA tensors
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- Thread-local variable `ggml_current_numa_node` for routing
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#### `ggml/src/ggml.c`
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**Purpose**: Core tensor operations and NUMA mirror management
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**Key additions**:
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- NUMA mirror allocation and deallocation logic
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- `tensor_set_data_with_numa_mirrors()` implementation
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- Thread-local NUMA node tracking
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- Memory management for NUMA mirror arrays
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#### `ggml/src/ggml-cpu/ggml-cpu.c`
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**Purpose**: CPU backend integration with NUMA coordination
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**Key additions**:
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- Thread binding during computation
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- NUMA-aware memory allocation paths
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### Model Loading Integration
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#### `src/llama-model-loader.cpp`
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**Purpose**: Model loading with explicit NUMA mirror setup
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**Key addition**:
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- Detection of model weight tensors during loading
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- Call to `tensor_set_data_with_numa_mirrors()` for weight tensors
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- Clean integration with existing model loading pipeline
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#### `src/llama-mmap.h` and `src/llama-mmap.cpp`
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**Purpose**: Memory-mapped file support with NUMA awareness
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**Modifications**: Enhanced to work with NUMA-aware memory allocation patterns
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### Command Line Integration
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#### `common/arg.cpp`
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**Purpose**: Command line argument parsing
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**Addition**: Support for `--numa mirror` command line option
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#### `tools/llama-bench/llama-bench.cpp`
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**Purpose**: Benchmarking tool integration
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**Addition**: NUMA mirroring support in benchmark tests
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## Build Configuration
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### CMake Configuration
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Enable NUMA mirroring during build:
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```bash
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cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NUMA_MIRROR=ON -DCMAKE_C_FLAGS="-march=native" -DCMAKE_CXX_FLAGS="-march=native"
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cmake --build build --parallel
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```
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### Required Dependencies
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- **libnuma**: NUMA policy library (`libnuma-dev` on Ubuntu)
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- **OpenMP**: Parallel processing support
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- **C++17 compiler**: Modern C++ standard support
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### Compilation Flags
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- `GGML_NUMA_MIRROR=ON`: Enables NUMA mirroring functionality
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- `-march=native`: CPU-specific optimizations (recommended for maximum performance)
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- `CMAKE_BUILD_TYPE=Release`: Optimized release build
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## Usage
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### Command Line Usage
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```bash
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# Enable NUMA mirroring for inference
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./llama-cli -m model.gguf --numa mirror -p "Hello world"
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# Benchmark with NUMA mirroring
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./llama-bench -m model.gguf --numa mirror
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# Server with NUMA mirroring
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./llama-server -m model.gguf --numa mirror --host 0.0.0.0 --port 8080
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```
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## Implementation Details
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### Tensor Data Access Optimization
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The `tensor_data()` function in `ggml.h` has been optimized with a fast path:
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```c
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static inline void * tensor_data(const struct ggml_tensor * tensor) {
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#ifdef GGML_NUMA_MIRROR
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if (tensor->numa_mirror_data == NULL) {
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return tensor->data; // Fast path: no NUMA mirrors
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}
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return ggml_numa_get_tensor_data(tensor); // NUMA-aware routing
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#else
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return tensor->data;
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#endif
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}
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```
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This optimization ensures minimal overhead for intermediate computation tensors while enabling NUMA routing for model weights.
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### Memory Management
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- **Model weights**: Automatically mirrored across all NUMA nodes during loading
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- **Intermediate tensors**: Allocated on the NUMA node where they're computed
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- **Thread binding**: OpenMP threads are bound to specific NUMA nodes for consistent memory access patterns
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## Debugging and Monitoring
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### Debug Output
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Enable with `--verbose` to see Numa model mirroring on startup.
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### Performance Monitoring
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Use `llama-bench` to measure NUMA benefits:
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```bash
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# Test without NUMA
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./llama-bench -m model.gguf
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# Test with NUMA mirroring
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./llama-bench -m model.gguf --numa mirror
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```
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### System Requirements Check
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Verify NUMA topology:
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```bash
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numactl --hardware
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```
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## Future Enhancements
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### Configuration Options
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Future versions may include:
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- Selective tensor mirroring policies
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- Custom NUMA node mapping
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## Technical Notes
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### Memory Overhead
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- Each NUMA node maintains a complete copy of model weights
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- Memory usage increases linearly with the number of NUMA nodes
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- Intermediate computation tensors have minimal overhead
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### Compatibility
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- Works with all existing model formats (GGUF)
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- Compatible with quantized models (Q4, Q8, etc.)
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- Integrates with all backends (CPU, CUDA, Metal, etc.)
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### Thread Safety
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- Thread-local variables ensure safe concurrent access
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- Model loading is protected by existing llama.cpp synchronization
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## Troubleshooting
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### Common Issues
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1. **No performance improvement**: Check `numactl --hardware` for multiple NUMA nodes
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2. **Build errors**: Ensure `libnuma-dev` is installed
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3. **Memory allocation failures**: Verify sufficient memory on each NUMA node
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4. **Thread binding issues**: Check for conflicting process affinity settings
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### Verification
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Confirm NUMA mirroring is working:
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1. Build with `GGML_NUMA_MIRROR=ON`
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2. Run `numactl --hardware` to verify multiple NUMA nodes
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3. Test with `GGML_NUMA_DEBUG=1` for debug output
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4. Compare performance with and without `--numa mirror`
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## Conclusion
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The NUMA mirroring implementation provides significant performance improvements for multi-NUMA-node systems while maintaining full compatibility with existing llama.cpp functionality. The clean integration points and optimized hot paths ensure minimal overhead when NUMA features are not needed, while providing substantial benefits when enabled.
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For systems with multiple NUMA nodes, enabling NUMA mirroring can result in dramatic performance improvements, particularly for text generation workloads that benefit from consistent memory access patterns and reduced cross-node memory traffic.

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