GPU Acceleration Setup

Transform your quantitative finance computations with GPU acceleration. VectorAlpha leverages CUDA to deliver 10-30x performance improvements for backtesting, Monte Carlo simulations, and real-time indicator calculations.

CUDA 12.x Support

VectorAlpha fully supports CUDA 12.x with the latest Rust CUDA ecosystem updates. Our GPU kernels are optimized for modern NVIDIA architectures including Ampere and Hopper.

Environment Setup

System Requirements

Hardware Requirements

  • ✓ GPU: NVIDIA GPU with Compute Capability 7.0+ (RTX 20 series or newer)
  • ✓ VRAM: Minimum 8GB for production workloads
  • ✓ Driver: NVIDIA Driver 525.60+ (for CUDA 12.x)
  • ✓ OS: Linux (Ubuntu 20.04+) or Windows 10/11

Installing CUDA Toolkit

# Ubuntu/Debian
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt-get install cuda-12-3

# Add to PATH
echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc

# Verify installation
nvcc --version
nvidia-sm

Rust CUDA Setup

VectorAlpha uses both cudarc for host-side operations and rust-cuda for kernel development:

Configuration Example Coming Soon

Configuration examples will be available in the next update.

GPU Programming with VectorAlpha

Basic GPU Operations

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Custom CUDA Kernels

Write custom CUDA kernels for specialized calculations:

CUDA Example Coming Soon

GPU programming examples will be available in the next update.

Rust Integration

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Performance Optimization

Memory Optimization

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Kernel Optimization Techniques

Optimization Checklist

  • ✓ Coalesced Access: Ensure adjacent threads access adjacent memory
  • ✓ Shared Memory: Use for frequently accessed data within thread blocks
  • ✓ Occupancy: Balance registers and threads per block
  • ✓ Warp Divergence: Minimize conditional branches

Real-World Use Cases

Monte Carlo Simulations

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Large-Scale Backtesting

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Multi-GPU Scaling

Scale computations across multiple GPUs for massive datasets:

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Performance Tips

For optimal performance: Use pinned memory for faster transfers, overlap computation with data transfer using CUDA streams, and profile your kernels with nsys or Nsight Compute to identify bottlenecks.

Benchmarks

GPU vs CPU Performance

Operation CPU Time GPU Time Speedup
SMA (10M points) 850ms 28ms 30.4x
Monte Carlo (1M paths) 12.5s 0.45s 27.8x
Backtest (1000 strategies) 180s 8.2s 22.0x
Portfolio Optimization 45s 2.1s 21.4x

Benchmarks on NVIDIA RTX 4090 vs AMD Ryzen 9 7950X

Next Steps

Cloud Deployment →

Deploy GPU workloads to the cloud

Performance Guide →

Advanced optimization techniques