GPU Acceleration Setup

Use GPU acceleration to speed up quantitative workloads. VectorAlpha uses CUDA to deliver substantial speedups—often 10–30× on parallel workloads—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-smi

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

Kernel Example Coming Soon

Detailed kernel implementations will be added shortly.

Performance Tips

  • Batch workloads: Group operations to minimize host/device transfers.
  • Use pinned memory: Leverage pinned buffers for faster DMA transfers.
  • Profile regularly: Use `nsys` and `nvprof` to analyze kernel efficiency.
  • Monitor thermals: Ensure adequate cooling to prevent thermal throttling.