Abhishek Vijaya Kumar

Abhishek Vijaya Kumar, Eric Ding, Arjun Devraj et al.

We develop Morphlux, a server-scale programmable photonic fabric to interconnect accelerators within servers. We show that augmenting state-of-the-art torus-based ML data-centers with Morphlux can improve the bandwidth of tenant compute allocations by up to 66%, reduce compute fragmentation by up to 70%, and minimize the blast radius of chip failures. We develop a novel end-to-end hardware prototype of Morphlux to demonstrate these performance benefits which translate to 1.72X improvement in training throughput of ML models. By rapidly programming the server-scale fabric in our hardware testbed, Morphlux can replace a failed accelerator chip with a healthy one in 1.2 seconds.

Arjun Devraj, Eric Ding, Abhishek Vijaya Kumar et al.

Distributed machine learning workloads use data and tensor parallelism for training and inference, both of which rely on the AllReduce collective to synchronize gradients or activations. However, AllReduce algorithms are delayed by the slowest GPU to reach the synchronization barrier before the collective (i.e., the straggler). To address this challenge, we propose StragglAR: a parallel algorithm for AllReduce that accelerates distributed training and inference by exploiting natural variation in GPU execution times. StragglAR implements a ReduceScatter among the remaining GPUs during the straggler-induced delay, and then executes a novel collective algorithm to complete the AllReduce once the final GPU reaches the synchronization barrier. StragglAR achieves a 2x theoretical speedup over popular bandwidth-efficient algorithms for large GPU clusters, surpassing the lower bound for bandwidth-optimal synchronous AllReduce by leveraging the asymmetry in when GPUs reach the synchronization barrier. On an 8-GPU server, StragglAR provides a 25% speedup over state-of-the-art AllReduce algorithms.