Add to ORKGAbstract We introduce MALT, a machine learning library that integrates with existing machine learning software and provides peer-to-peer data parallel machine learning. MALT provides abstractions for fine-grained in-memory updates using one-sided RDMA, limiting data movement costs during incremental model updates. MALT allows machine learning developers to specify the dataflow and apply communication and representation optimizations. In our results, we find that MALT provides fault tolerance, network efficiency and speedup to SVM, matrix factorization and neural networks
As Machine Learning (ML) applications embrace greater data size and model complexity, practitioners ...
The rise of big data has led to new demands for machine learning (ML) systems to learn complex model...
To support large-scale machine learning, distributed training is a promising approach as large-scale...
Machine learning methods, such as SVM and neural net-works, often improve their accuracy by using mo...
<p>Distributed machine learning has typically been approached from a data parallel perspective, wher...
Machine learning (ML) has become a powerful building block for modern services, scientific endeavors...
Large scale machine learning has many characteristics that can be exploited in the system designs to...
As Machine Learning (ML) applications are becoming ever more pervasive, fully-trained systems are ma...
Many large-scale machine learning (ML) applications use it-erative algorithms to converge on paramet...
Many large-scale machine learning (ML) applications use it-erative algorithms to converge on paramet...
As ML applications are becoming ever more pervasive, fully-trained systems are made increasingly ava...
As Machine Learning (ML) applications embrace greater data size and model complexity, practition-ers...
Training large machine learning (ML) models with many variables or parameters can take a long time i...
ABSTRACTThe rise of big data has led to new demands for machine learning (ML) systems to learn compl...
High-performance computing (HPC) and machine learning (ML) have been widely adopted by both academia...
As Machine Learning (ML) applications embrace greater data size and model complexity, practitioners ...
The rise of big data has led to new demands for machine learning (ML) systems to learn complex model...
To support large-scale machine learning, distributed training is a promising approach as large-scale...
Machine learning methods, such as SVM and neural net-works, often improve their accuracy by using mo...
<p>Distributed machine learning has typically been approached from a data parallel perspective, wher...
Machine learning (ML) has become a powerful building block for modern services, scientific endeavors...
Large scale machine learning has many characteristics that can be exploited in the system designs to...
As Machine Learning (ML) applications are becoming ever more pervasive, fully-trained systems are ma...
Many large-scale machine learning (ML) applications use it-erative algorithms to converge on paramet...
Many large-scale machine learning (ML) applications use it-erative algorithms to converge on paramet...
As ML applications are becoming ever more pervasive, fully-trained systems are made increasingly ava...
As Machine Learning (ML) applications embrace greater data size and model complexity, practition-ers...
Training large machine learning (ML) models with many variables or parameters can take a long time i...
ABSTRACTThe rise of big data has led to new demands for machine learning (ML) systems to learn compl...
High-performance computing (HPC) and machine learning (ML) have been widely adopted by both academia...
As Machine Learning (ML) applications embrace greater data size and model complexity, practitioners ...
The rise of big data has led to new demands for machine learning (ML) systems to learn complex model...
To support large-scale machine learning, distributed training is a promising approach as large-scale...