Our Technology Stack
How we use GPU and SIMD acceleration to deliver production performance for quantitative workloads.
Parallel Processing at Scale
Our technology stack uses modern hardware—GPU acceleration and SIMD instructions—to deliver measurable speedups on quantitative workloads.
GPU Acceleration
Custom CUDA kernels process millions of data points in parallel
SIMD Optimization
Vectorized CPU instructions for when GPU isn't available
In‑VRAM Processing
Keep data on the GPU during the whole calculation to avoid slow CPU↔GPU transfers
Performance Multiplier vs Standard CPU
Note: Benchmarks based on processing 1M candlestick data points for technical indicators
Benchmarked on NVIDIA RTX 4090 with one million candles per indicator
GPU Acceleration
Our CUDA approach keeps data on the GPU (in VRAM) for the entire calculation. We minimize round‑trips to system memory so parallel work on the device isn’t cancelled out by transfer overhead.
- • In‑VRAM pipelines (avoid unnecessary CPU↔GPU transfers)
- • Efficient memory access and batching
- • Parallel kernels tuned for technical workloads
- • Overlapped work with multiple streams when helpful
SIMD Optimization
Leveraging AVX2 and AVX-512 instruction sets for vectorized CPU computations, processing multiple data points in a single instruction.
- • Hand-tuned assembly for critical paths
- • Runtime CPU feature detection
- • Aligned memory allocation
- • Compiler intrinsics for portability
Why keep data on the GPU?
Moving large arrays back and forth between the CPU and GPU uses a relatively slow bus. By doing the full calculation in VRAM and only returning compact results, you get the benefit of parallel hardware without paying repeated transfer costs.
Performance Benchmarks
Simple Moving Average
1M candles processed
faster than CPU
RSI Calculation
100k candles processed
faster with SIMD
Bollinger Bands
1M candles processed
GPU acceleration
Benchmarked on NVIDIA RTX 4090 | AMD 9950X
*Performance varies by workload, data size, and hardware configuration
Open Source Philosophy
All VectorAlpha projects are open source, fostering transparency and community collaboration in quantitative finance.
Why Open Source?
- Transparency builds trust in financial systems
- Community contributions improve quality
- Reproducible research advances the field
- Lower barriers to entry democratize finance
Apache License 2.0
All VectorAlpha projects are released under the Apache License 2.0, providing maximum flexibility for both commercial and non-commercial use while ensuring contributions remain open.
This permissive license allows you to use, modify, and distribute our software in your own projects without viral licensing concerns.
Built with Modern Technologies
Rust
Memory safety without garbage collection. Zero-cost abstractions and fearless concurrency.
CUDA
Direct GPU programming for maximum performance. Custom kernels for financial computations.
WebAssembly
Near-native performance in browsers. Interactive demos without server infrastructure.
What's Next?
Ready to accelerate your quantitative workflows?
Join developers using VectorAlpha for high‑performance financial computing