Architecture decisions and optimization techniques from building a CUDA technical analysis library and GPU-first backtest optimization workflows. Every article includes benchmarks, profiling data, and implementation details from real VectorAlpha projects.
Start with the product pages for Technical Analysis Library and Backtesting Engine, then use the articles below for implementation details.
How the VectorAlpha technical analysis library uses CUDA to accelerate indicator workloads on modern GPUs. Walks through tiled ALMA kernels, shared memory layouts and multi series paths, and uses real benchmarks to show how a heavy ALMA batch reaches around 20x the throughput of an AVX512 CPU kernel for that workload.
How the VectorAlpha technical analysis library uses AVX2 and AVX512 kernels to accelerate heavy indicator workloads across 340 functions. Covers kernel selection, windowing patterns, streaming APIs and batch parameter sweeps built on top of a shared SIMD dot product core.
How VectorTA's CUDA dispatch layer separates the host compatibility path from the device-native path, uses validated device views for pointer-in and pointer-out execution, and makes upload-once, dispatch-many workflows practical. Includes the 3.129 ms ALMA benchmark and the 58,300-backtest Tauri demo result.
How VectorGrid turns exact grid search into the default product path by keeping price data, indicators, and backtest execution in VRAM. Includes the 58,300 pair ALMA benchmark on 200,000 bars, the tighter 1 GB VRAM budget run, and the CPU fallback story.
Watch our repositories for implementation details, benchmarks, and performance analysis updates.
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