This project implements a high-performance parallel pricing engine for American options using the Binomial Options Pricing Model (BOPM), engineered to scale efficiently from multi-core CPUs to GPU-accelerated clusters. By leveraging OpenMP, CUDA and MPI, it addresses the algorithm’s sequential bottlenecks through a diverse set of optimization strategies. More details at: https://github.com/l1-ca0/parallel-binomial-option-pricing
I just completed a project that lets people ask database questions in plain English and get back proper SQL queries using a fine-tuned large language model. For the base model, I chose Mistral-7B-v3 and fine-tuned it specifically for SQL generation. Using QLoRA for efficient training, I was able to train the 7-billion parameter model on a single consumer-grade GPU (Nvidia Tesla P100) in around 3 hours. The resulting model performs well on common SQL patterns like filtering, joins, and aggregations, effectively handling the majority of real-world database queries. That said, it can be less accurate for complex subqueries or really intricate nested queries due to the limitations of the Mistral-7B model —- a larger model would handle these cases better, but this was a tradeoff between performance and computational requirements. ...