A Parameter Server is a distributed computing concept commonly used in machine learning and deep learning frameworks. It serves as a centralized system for storing and managing the parameters of machine learning models in a distributed computing environment. The key purpose of a parameter server is to coordinate and synchronize the updates to model parameters across multiple computing nodes.

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SplitBrain: Hybrid Data and Model Parallel Deep Learning

SplitBrain: Hybrid Data and Model Parallel Deep Learning The recent success of deep learning applications has coincided with those widely available powerful computational resources for training sophisticated machine learning models with huge datasets. Nonetheless, training large models such as convolutional neural networks using model parallelism (as opposed to data parallelism) is challenging because the complex nature of communication between model shards makes it difficult to partition the computation efficiently across multiple machines with an acceptable trade off. This paper presents SplitBrain, a high performance distributed deep learning framework supporting hybrid data and model parallelism. Specifically, SplitBrain provides layer specific partitioning that co locates compute intensive convolutional layers while sharding memory demanding layers. A novel scalable group communication is proposed to further improve the training throughput with reduced communication overhead. The results show that SplitBrain can achieve nearly linear speedup while saving up to 67% of memory consumption for data and model parallel VGG over CIFAR 10.