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Distributed neural network training frameworks typically optimize for specific architectures while minimizing communication overhead. Transformer layers can be efficiently parallelized, but other operations such as convolutions often remain inefficient. This creates bottlenecks for complex model architectures. Moreover, existing tensor parallelism strategies typically replicate input data across all processes, creating redundant I/O that scales poorly with input size. In applications with heavy I/O demands-weather forecasting, medical imaging, or video processing-unsharded input data creates additional data-loading bottlenecks that could benefit from parallelization. Jigsaw is a PyTorch library that shards both model weights and input data across parallel processes. It maintains a PyTorch-like interface while parallelizing activations, convolutions, linear layers, and attention through a distributed matrix multiplication backend. We demonstrate the usability of Jigsaw across a wide range of model architectures and shows performance when scaling multi-billion-parameter models sharded across up to 8 processes and compares the scalability to DDP, FSDP, and Megatron-LM approaches.
Doctoral Researcher, Karlsruhe Institute of Technology
Deifilia Kieckhefen is a doctoral researcher at the Karlsruhe Institute of Technology. She works on scalable and distributed training of neural network architectures.
