Data movement is all you need: A case study on optimizing transformers
Proceedings of Machine Learning and Systems, 2021•proceedings.mlsys.org
Transformers are one of the most important machine learning workloads today. Training one
is a very compute-intensive task, often taking days or weeks, and significant attention has
been given to optimizing transformers. Despite this, existing implementations do not
efficiently utilize GPUs. We find that data movement is the key bottleneck when training. Due
to Amdahl's Law and massive improvements in compute performance, training has now
become memory-bound. Further, existing frameworks use suboptimal data layouts. Using …
is a very compute-intensive task, often taking days or weeks, and significant attention has
been given to optimizing transformers. Despite this, existing implementations do not
efficiently utilize GPUs. We find that data movement is the key bottleneck when training. Due
to Amdahl's Law and massive improvements in compute performance, training has now
become memory-bound. Further, existing frameworks use suboptimal data layouts. Using …
Abstract
Transformers are one of the most important machine learning workloads today. Training one is a very compute-intensive task, often taking days or weeks, and significant attention has been given to optimizing transformers. Despite this, existing implementations do not efficiently utilize GPUs. We find that data movement is the key bottleneck when training. Due to Amdahl's Law and massive improvements in compute performance, training has now become memory-bound. Further, existing frameworks use suboptimal data layouts. Using these insights, we present a recipe for globally optimizing data movement in transformers. We reduce data movement by up to 22.91% and overall achieve a 1.30 x performance improvement over state-of-the-art frameworks when training a BERT encoder layer and 1.19 x for the entire BERT. Our approach is applicable more broadly to optimizing deep neural networks, and offers insight into how to tackle emerging performance bottlenecks.
proceedings.mlsys.org
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