39 citations · 42 across the 7 of their papers we have counts for
4 papers · 1 filter
Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization
Siddharth Singh, Zachary Sating, Abhinav Bhatele
Despite their better convergence properties compared to first-order optimizers, second-order optimizers for deep learning have been less popular due to their significant computatio…
A 4D Hybrid Algorithm to Scale Parallel Training to Thousands of GPUs
Siddharth Singh, Prajwal Singhania, Aditya K. Ranjan +2
Heavy communication, in particular, collective operations, can become a critical performance bottleneck in scaling the training of billion-parameter neural networks to large-scale…
A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training
Siddharth Singh, Olatunji Ruwase, Ammar Ahmad Awan +3
Mixture-of-Experts (MoE) is a neural network architecture that adds sparsely activated expert blocks to a base model, increasing the number of parameters without impacting computat…
Exploiting Sparsity in Pruned Neural Networks to Optimize Large Model Training
Siddharth Singh, Abhinav Bhatele
Parallel training of neural networks at scale is challenging due to significant overheads arising from communication. Recently, deep learning researchers have developed a variety o…