KAUST researchers use Anderson exploit to maximize GPU efficiency with increased model accuracy and generalization

The scaling of ai means greater spending on infrastructure. Massive, multidisciplinary research puts economic pressure on institutions, as high-performance computing (HPC) costs an arm and a leg. HPC is financially draining and has a critical impact on energy consumption and the environment. It is predicted that by 2030, ai will account for 2% of global electricity consumption. New approaches are required to maximize computational efficiency while reducing iterations toward convergence. Anderson extrapolation is a low-speedup memory technique that could be used to achieve the above goal. This article delves into the latest research by applying it to GPUs to maximize the return on computational investments.

Researchers at King Abdullah University of Science and technology used matrix-free Anderson extrapolation on GPUs. They showed their influence on training models and forward passes (i.e. running inferences on the models). This method accelerated ai performance by reusing previous iterations to avoid unnecessary gradient calculations, obtaining the benefits expected from second-order methods. Let's define what Anderson Exploitation means to set the stage for the rest of this article. It is a vector-to-vector mapping technique based on a window of historical iterations. This technique is used to accelerate nonlinear fixed point iterations and is widely used in subdisciplines of physics, such as kinetic theory, density functional theory, etc. Anderson Exploitation is suitable for memory parallelization, making it compatible with GPUs. There are several open source libraries available that provide this functionality, such as PETSc, SUNDIALS, etc. Improves GPU performance by reusing cached state vector data, promoting fewer, more expensive steps.

To test the effectiveness of the above idea, the authors used deep equilibrium neural networks. DEQa are huge neural networks with a number of layers that tend to infinity. Their architecture approximates many explicit layers with a single implicit layer with exponentially fewer parameters through a backward pass. This phenomenon introduces the scope of nonlinear vector-to-vector mapping techniques. Vector-to-vector mapping techniques outperform standard direct iteration by combining information from previous iterations to span a search subspace to extrapolate the next iteration, improving convergence rates at the expense of memory usage in each iteration.

Experimental results showed that Anderson acceleration achieved higher accuracies in training and testing in less time than direct iteration. It showed fewer fluctuations in accuracy, especially on the test data, unlike the rapid fluctuation of direct iteration, which indicated overfitting time and time again. Anderson thus made the training more generalizable. Anderson on GPU performed much better than the standard direct iterations and Anderson on CPU. This is because the parallel processing capabilities of GPUs balance out Anderson's additional computational expense. However, there is a trade-off between accuracy and calculation time. In this sense, its contrary and direct iteration maintained a more consistent calculation time as the number of epochs increased. In Anderson's case, an increase in computation time with successive iterations arose from the process of residual minimization during each acceleration step. Even after this trade-off, Anderson improved DEQ performance in a fraction of the time required for direct iterations to stabilize with comparable accuracy.

Conclusion

Anderson acceleration substantially improved the accuracy of deep equilibrium models along with the computational efficiency and generalization ability of the model. This research shows a bright future in applying vector-to-vector mapping techniques to CPU and GPU architectures. Even in the slightest, further acceleration could be examined by stochastically varying Anderson's exploitation.

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Adeeba Alam Ansari is currently pursuing her dual degree from the Indian Institute of technology (IIT) Kharagpur, where she earned a bachelor's degree in Industrial Engineering and a master's degree in Financial Engineering. With a keen interest in machine learning and artificial intelligence, she is an avid reader and curious person. Adeeba firmly believes in the power of technology to empower society and promote well-being through innovative solutions driven by empathy and a deep understanding of real-world challenges.