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Recent trend towards increasing large machine learning models require bothtraining and inference tasks to be distributed. Considering the huge cost oftraining these models, it is imperative to unlock optimizations in computationand communication to obtain best performance. However, current logicalseparation between computation and communication kernels in deep learningframeworks misses the optimization opportunities across such barrier. Breakingthis abstraction with a holistic consideration can provide many optimizationsto provide performance improvements in distributed workloads. Manually applyingthese optimizations needs modifications in underlying computation andcommunication libraries for each scenario, which is time consuming anderror-prone.  Therefore, we present CoCoNeT, with a DSL to express a program with bothcomputation and communication. CoCoNeT contains several machine learning awaretransformations to optimize a program and a compiler to generate highperformance kernels. Providing both computation and communication as firstclass constructs allows users to work on a high-level abstraction and applypowerful optimizations, such as fusion or overlapping of communication andcomputation. CoCoNeT enables us to optimize data-, model-and pipeline-parallelworkloads in large language models with only a few lines of code. Experimentsshow CoCoNeT significantly outperforms state-of-the-art distributed machinelearning implementations.

Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads