Our Technology Stack
How we use GPU and SIMD acceleration to deliver production performance for quantitative workloads.
Parallel Processing at Scale
Our technology stack leans on modern hardware such as GPUs and SIMD instructions to speed up real 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: CUDA bar reflects current aggregate benchmark results on RTX 4090 + Ryzen 9 9950X.
Representative indicators shown: ATR, RSI, EMA, ALMA, MACD, and Bollinger Bands. CUDA speedups come from benchmark results, and scalar baselines are estimated from your 2.0x to 2.5x guidance for 1M candle runs.
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.
SIMD Optimization
Leveraging AVX2 and AVX-512 instruction sets for vectorized CPU computations, processing multiple data points in a single instruction.
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
CUDA vs Rust (Overall)
1M candles x 250 parameters
123 indicators faster on CUDA
Median CUDA Speedup
Across CUDA-faster indicators
64 indicators are above 10x
All CUDA Kernels
Overall aggregate speedup
Includes faster and slower CUDA kernels
Benchmarked on NVIDIA RTX 4090 | AMD 9950X
*1M candles x 250 parameter batch benchmarks. Performance varies by workload and hardware.
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
- Accessible tools expand market participation
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