🚀 Accelerating Nonlinear Programming on GPUs: rAPDHG + L-BFGS-B for Large-Scale Problems Solving large-scale Nonlinear Programming (NLP) problems efficiently is a challenge — especially when the problem includes linear equality constraints and box constraints. Traditional CPU-based solvers can be fast for small problems, but when you scale up to millions of variables, they start to feel… well, a bit like dial-up internet.
Sometimes, PyTorch might not natively support a specific operation you need, or its existing implementation leads to redundant calculations. In such scenarios, implementing a customized operation using a custom CUDA kernel can significantly improve performance. This blog post will guide you step-by-step through the process of binding a custom CUDA kernel with PyTorch. Also, this blog willl contain the process of implementing the api of PyTorch’s autograd.
If you’re delving into GPU computing with NVIDIA CUDA, understanding your hardware’s capabilities and interconnections is crucial. The CUDA samples provide an excellent starting point for this exploration. This guide will walk you through downloading these samples, compiling them, and then using them to assess your GPU’s performance and connectivity.
This guide will walk you through setting up a server or cluster for deep learning tasks, particularly for Large Language Models (LLMs). The content was originally documented in my wolai notes and is now shared here. Feel free to ask questions in the comments!