Do I need a dedicated GPU to use this skill?

While the skill is optimized for NVIDIA GPUs via PyTorch and CUDA, it includes an automated CPU fallback mechanism to ensure functionality across all computing environments.

How does the caching mechanism prevent redundant calculations?

The skill generates a unique SHA256 hash based on sorted symbols, date ranges, and rolling window parameters, storing results in a local SQLite database for near-instant retrieval.

What kind of performance improvements can I expect?

Benchmarks demonstrate a ~20x speedup for initial GPU-accelerated runs and up to a ~150x speedup for subsequent runs leveraging the persistent cache.

Does this skill handle large symbol universes without crashing?

Yes, it utilizes torch.unfold() for memory-efficient views and processes windows in batches to prevent Out-of-Memory (OOM) errors even with hundreds of symbols.

GPU Correlation Accelerator & Cache

Name: GPU Correlation Accelerator & Cache
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Accelerates financial correlation matrix computations using GPU-vectorized PyTorch operations and persistent SQLite caching.

About

This skill eliminates performance bottlenecks in quantitative trading workflows by replacing slow, CPU-bound pandas correlation loops with optimized GPU-accelerated calculations via PyTorch. It features a sophisticated SQLite-based caching system that utilizes SHA256 hashing to store and retrieve pre-computed matrices, ensuring that identical data parameters—such as symbols, dates, and rolling windows—never require re-computation. By utilizing memory-efficient sliding window views and batch processing, it scales effortlessly to large symbol universes, reducing processing times from minutes to sub-second retrievals.

Key Features

GPU acceleration using PyTorch's vectorized BLAS operations and efficient unfold views
Automated CPU fallback mechanism for seamless operation in environments without CUDA support
Memory-efficient batch processing to prevent GPU Out-of-Memory (OOM) errors on large datasets
Persistent SQLite caching with SHA256-based key generation for instant data retrieval
0 GitHub stars
Order-independent symbol hashing to ensure consistent cache hits regardless of input sequence

Use Cases

Scaling financial data pipelines involving matrix-heavy computations for hundreds of symbols
Accelerating backtesting workflows that require intensive rolling correlation analysis
Optimizing universe selection and portfolio construction in algorithmic trading platforms

About

Key Features

GPU acceleration using PyTorch's vectorized BLAS operations and efficient unfold views
Automated CPU fallback mechanism for seamless operation in environments without CUDA support
Memory-efficient batch processing to prevent GPU Out-of-Memory (OOM) errors on large datasets
Persistent SQLite caching with SHA256-based key generation for instant data retrieval
0 GitHub stars
Order-independent symbol hashing to ensure consistent cache hits regardless of input sequence

Use Cases

Scaling financial data pipelines involving matrix-heavy computations for hundreds of symbols
Accelerating backtesting workflows that require intensive rolling correlation analysis
Optimizing universe selection and portfolio construction in algorithmic trading platforms