Voltrix-SpMM

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Revitalizing Sparse Matrix-Matrix Multiplication on Tensor Cores with Asynchronous and Balanced Kernel Optimization

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# Voltrix-SpMM 
🔥🔥🔥**Artifact Evaluation for our USENIX ATC '25 paper** : *"Voltrix: Sparse Matrix-Matrix Multiplication on Tensor Cores with Asynchronous and Balanced Kernel Optimization"*

Welcome to Voltrix-SpMM! This is a high-performance sparse matrix-matrix multiplication (SpMM) library specifically optimized for the NVIDIA Hopper architecture. It leverages advanced GPU features of modern hardware, such as the Tensor Memory Accelerator (`TMA`) and `mbarrier`, to achieve state-of-the-art performance.

---

## ⚙️ Requirements

### Hardware

- **GPU Architecture:**  NVIDIA Hopper or newer (compute capability **≥ 90** ).

Voltrix **requires NVIDIA Hopper-specific architectural features** , including **Tensor Memory Accelerator (TMA)**  for high-throughput memory movement and **mbarrier**  for efficient thread synchronization.

### Software

- **CMAKE VERSION:**  ≥ **3.17**

- **GCC/G++ VERSION:**  ≥ **11**
 
- **CUDA VERSION:**  ≥ **12.1**
 
- **Python:**  ≥ **3.12**
 
- **PyTorch:**  ≥ **2.1**  (with Hopper support)

---

## Clone the Repository

```bash
git clone https://github.com/YaqiXia/Voltrix-SpMM.git
cd Voltrix-SpMM
```

---

## 🧰 Environment Setup

We recommend using `conda` for environment management.

### Step 1: Create and activate the environment

```bash
conda create -n voltrix python=3.12
conda activate voltrix
```

### Step 2: Install dependencies

We recommend installing PyTorch via conda to ensure compatibility with your CUDA version:

```bash
# Recommended: Install PyTorch via conda (ensure compatibility with your CUDA 12.1+)
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia

# Alternatively, if you prefer pip and have ensured your CUDA environment is correctly configured:
# pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# Install other dependencies
pip install scipy pandas matplotlib seaborn
```

### Step 3: Install Voltrix-SpMM in editable mode

```bash
pip install -e .
```
This allows your modifications to the source code to take effect immediately without reinstalling.

### Step 4: Test JIT compilation

```bash
python tests/test_jit.py
```
Voltrix uses extensive template programming to optimize kernel performance. With JIT (Just-In-Time) compilation, kernel parameters are tuned dynamically at runtime, allowing Voltrix to select the **optimal kernel**  based on hardware capabilities. This tuning process involves automatically adjusting kernel configurations to achieve the best performance, making Voltrix highly efficient for SpMM on Tensor Cores.

Expected output:

```bash
...
JIT test passed
```

This confirms that the **JIT compilation was successful** , and the kernel tuning has been completed.

---

## 🔧 Environment Variables Configuration

Voltrix-SpMM exposes several environment variables that act as **debugging flags** to observe **performance tuning** and **custom compilation workflows**. These variables are defined in [project/const.py] and can be configured at runtime.

### Key Environment Variables

| Variable | Description | 
| --- | --- | 
| VOLTRIX_JIT_DEBUG | Enables verbose debug output during JIT compilation. | 
| VOLTRIX_NVCC_COMPILER | Specifies the absolute path to a custom nvcc compiler binary. | 
| VOLTRIX_CACHE_DIR | Sets the directory for caching compiled binaries (default: ~/.voltrix-spmm/). | 
| VOLTRIX_PTXAS_VERBOSE | Enables verbose output from the ptxas compiler backend. | 
| VOLTRIX_JIT_PRINT_NVCC_COMMAND | Prints the full nvcc command used for JIT compilation. | 
| VOLTRIX_PRINT_AUTO_TUNE | Prints detailed logs during kernel auto-tuning. |

### Example: Setting Variables in Shell

You can optinally configure these variables by exporting them in your shell session:

```bash
export VOLTRIX_JIT_DEBUG=1
export VOLTRIX_NVCC_COMPILER=/usr/local/cuda/bin/nvcc
export VOLTRIX_CACHE_DIR=~/.voltrix-spmm/
export VOLTRIX_PTXAS_VERBOSE=1
export VOLTRIX_JIT_PRINT_NVCC_COMMAND=1
export VOLTRIX_PRINT_AUTO_TUNE=1
```

---

## 🧭 Run SpMM Test

Execute the following command to run a basic SpMM functionality test:

```bash
python tests/test_spmm.py
```

**Accuracy Test:** 
 
  - The **difference rate**  represents the **discrepancy**  between Voltrix-SpMM's output and the reference output from **cuSPARSE**  (NVIDIA’s cuSPARSE library).
 
  - A **low difference rate**  indicates that Voltrix-SpMM is producing highly accurate results, close to the standard reference.
 
**Performance Test:** 
 
  - The test also reports the **execution time**  for both Voltrix-SpMM and cuSPARSE, allowing for a comparison of **performance**  in terms of **milliseconds (ms)**

Example output:

```yaml
difference rate: 0.00%
voltrix time: 2.23 ms
cusparse time: 3.45 ms
```

This output confirms that the core functionality of Voltrix-SpMM is working correctly and shows a comparison with cuSPARSE results.

---

## 🚀 Benchmark Reproduction Results from the Paper

Follow these steps to reproduce the benchmark results presented in the paper:

### Step 0: Set Voltrix project path

```bash
export VOLTRIX_PATH=$(pwd)
```

### Step 1: Build Third-Party Libraries

#### 1.1 Initialize Git Submodules

```bash
git submodule update --init --recursive
```

**Note:**  Running the above commands may produce the following error messages:

```pgsql
fatal: No url found for submodule path 'third-party/TC-GNN/TCGNN-bSpmm/cusparse/AIPowerMeter' in .gitmodules
Failed to recurse into submodule path 'third-party/TC-GNN/TCGNN-bSpmm'
Failed to recurse into submodule path 'third-party/TC-GNN'
```

These errors can be safely ignored and do not affect the normal usage of the code.

#### 1.2 Build TCGNN-PyTorch

```bash
cd $VOLTRIX_PATH/third-party/TC-GNN/TCGNN_conv
TORCH_CUDA_ARCH_LIST="9.0a;9.0" python setup.py install # Hopper architecture
```

#### 1.3 Build DTC-SpMM

```bash
cd $VOLTRIX_PATH/third-party/
bash ./build_dtc.sh
```

#### 1.4 Build RoDe & Sputnik

```bash
cd $VOLTRIX_PATH/third-party/RoDe
# Apply necessary patches
git apply ../../patches/rode_fix.patch

mkdir -p build && cd build

# Set GLOG path (a submodule of DTC-SpMM)
GLOG_ROOT_DIR=$VOLTRIX_PATH/third-party/DTC-SpMM/third_party/glog

cmake .. \
  -DCMAKE_CUDA_FLAGS="--use_fast_math -Xcompiler=-fopenmp -gencode arch=compute_90,code=sm_90 -Xcompiler=-I/usr/include,-I/usr/include/x86_64-linux-gnu" \
  -DCMAKE_CUDA_ARCHITECTURES="90" \
  -DGLOG_INCLUDE_DIR=$GLOG_ROOT_DIR/build/include \
  -DGLOG_LIBRARY=$GLOG_ROOT_DIR/build/lib/libglog.so
make -j $(nproc) # Use all available cores for parallel compilation
```

**Note**: The build process may takes a considerable amount of time.

---

### Step 2: Build TC-GNN and GE-SpMM Benchmark Scripts

```bash
cd $VOLTRIX_PATH/bench/scripts
bash build.sh
```

---

### Step 3: Download Benchmark Datasets

```bash
cd $VOLTRIX_PATH
wget https://drive.google.com/file/d/1MuMsRr_Swi66isVHZYNkCWTnPblROT2g/view?usp=sharing
unzip datasets.zip
rm datasets.zip
```
**Note**: Direct download links for Google Drive can sometimes change or require confirmation. If the `wget` command above fails, you may need to manually download `datasets.zip` from `https://drive.google.com/file/d/1MuMsRr_Swi66isVHZYNkCWTnPblROT2g/view?usp=sharing` into the `$VOLTRIX_PATH` directory and then unzip it.

---

### Step 4: Configure Benchmark Environment Variables

```bash
export TCGNN_PATH=$VOLTRIX_PATH/third-party/TC-GNN
export DATASET_PATH=$VOLTRIX_PATH/datasets
export RODE_HOME=$VOLTRIX_PATH/third-party/RoDe
export LD_LIBRARY_PATH=$VOLTRIX_PATH/third-party/DTC-SpMM/third_party/sputnik/build/sputnik:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=$VOLTRIX_PATH/third-party/DTC-SpMM/third_party/glog/build:$LD_LIBRARY_PATH
```

---

### Step 5: Run the Benchmark

```bash
cd $VOLTRIX_PATH/bench
python bench_all.py
```

This will run all benchmarks and compare Voltrix-SpMM against baseline methods. Results are output to the console and write to `bench/results.csv` file.

**Note:** The full benchmarking process may take several hours to complete depending on your hardware configuration. Please be patient.

### Step 6 : Reproduce Main Results (Figure 9)
```bash
python plot.py
```

This script will generate a performance comparison plot between **Voltrix-SpMM**  and existing baselines such as **cuSPARSE** ,**Sputnik-SpMM**, **GE-SpMM**, **RoDe-SpMM**, **TC-GNN** , and **DTC-SpMM** , highlighting the average speedup across multiple datasets.
The resulting figure will be saved to the current directory as `results.png`.

---

## 📚 Citation

If you find this repository helpful for your research, please cite the following paper:

```bibtex
@inproceedings{xia2025voltrix,
  title={Voltrix: Sparse $\{$Matrix-Matrix$\}$ Multiplication on Tensor Cores with Asynchronous and Balanced Kernel Optimization},
  author={Xia, Yaqi and Wang, Weihu and Yang, Donglin and Zhou, Xiaobo and Cheng, Dazhao},
  booktitle={2025 USENIX Annual Technical Conference (USENIX ATC 25)},
  pages={699--714},
  year={2025}
}
```
---

## 📄 License

This project is released under the **MIT License** .

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