Spectral Data Normalization with WebGL Performance Optimization

Develop a high-performance JavaScript module for processing large-scale spectral datasets from mass spectrometry experiments. Create a pipeline that can handle >500MB of raw spectral data, implementing WebGL-accelerated normalization techniques. The solution must support multiple normalization methods (min-max, z-score, log transformation) with real-time performance metrics and memory usage tracking. Include error handling for corrupted data streams and provide a modular architecture that allows plug-and-play normalization strategies.