Summary of Accelerating Ai Performance Using Anderson Extrapolation on Gpus, by Saleem Abdul Fattah Ahmed Al Dajani et al.
Accelerating AI Performance using Anderson Extrapolation on GPUs
by Saleem Abdul Fattah Ahmed Al Dajani, David E. Keyes
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Performance (cs.PF); Numerical Analysis (math.NA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a new approach for improving AI performance by using Anderson extrapolation, a technique that maps vectors based on historical iterations. The method reduces the number of iterations required for convergence, balancing speed, memory usage, and accuracy. By identifying the crossover point where a penalty is incurred, the algorithm optimizes its performance, demonstrating significant improvements in both training and inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a new way to make AI work better by using an old idea called Anderson extrapolation. It looks at how computers have done things in the past and uses that information to make it faster and use less energy. This helps make AI more efficient and able to do big jobs on powerful computers. |
Keywords
* Artificial intelligence * Inference
