Ask HN: HPC Learning Path for a Data Scientist

I have a degree in mathematics and currently work as a data scientist. While I’m comfortable with Python and core machine learning techniques, I’ve realized that I need to deepen my understanding of high-performance computing (HPC) and performance engineering in order to optimize my code for speed and scale up algorithms for large systems.

Specifically, I’m interested in: * Writing high-performance, memory-efficient code (e.g., using C++, SIMD, GPU, parallel computing) * HPC system design and architecture * Optimizing large-scale data processing and ML infrastructure * Profiling, latency optimization, and memory management for data-heavy tasks

I’m looking for: 1. Books, resources, tutorials, online degrees that can guide me from a strong mathematical and ML foundation into performance optimization 2. Effective learning paths to transition from a general data science role to working with performance-critical systems and large-scale compute environments

I’m keen to improve my ability to build more efficient systems and handle large datasets or complex models with near real-time performance where necessary.

Would love any recommendations, personal experiences, or resources to help guide my learning!