Add to ORKG• Iterativeness arises in some ML apps • Consequence: repeated data access sequences • Repeating pattern can be exploited • Detect with minor effort – Either in a real or a "virtual " iteration • Specialize structures and policies to known pattern – Data partitioning, prefetching, lock avoidance, pre-marshalled structures, etc. • Next • Parallel machine learning • PageRank as one exampl
In distributed ML applications, shared parameters are usually replicated among computing nodes to mi...
Cloud intelligence applications often perform iterative computa-tions (e.g., PageRank) on constantly...
<p>Many modern machine learning (ML) algorithms are iterative, converging on a final solution via 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 Machine Learning (ML) applications increase in data size and model complexity, practitioners turn...
As Machine Learning (ML) applications embrace greater data size and model complexity, practitioners ...
Large scale machine learning has many characteristics that can be exploited in the system designs to...
As Machine Learning (ML) applications embrace greater data size and model complexity, practition-ers...
Machine learning application developers and data scientists spend inordinate amount of time iteratin...
Distributed machine learning has typically been approached from a data parallel perspective, where b...
Recent large language models have been trained on vast datasets, but also often on repeated data, ei...
Machine learning (ML), a computational self-learning platform, is expected to be applied in a variet...
A major bottleneck to applying advanced ML programs at industrial scales is the migration of an acad...
<p>In distributed ML applications, shared parameters are usually replicated among computing nodes to...
In distributed ML applications, shared parameters are usually replicated among computing nodes to mi...
Cloud intelligence applications often perform iterative computa-tions (e.g., PageRank) on constantly...
<p>Many modern machine learning (ML) algorithms are iterative, converging on a final solution via 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 Machine Learning (ML) applications increase in data size and model complexity, practitioners turn...
As Machine Learning (ML) applications embrace greater data size and model complexity, practitioners ...
Large scale machine learning has many characteristics that can be exploited in the system designs to...
As Machine Learning (ML) applications embrace greater data size and model complexity, practition-ers...
Machine learning application developers and data scientists spend inordinate amount of time iteratin...
Distributed machine learning has typically been approached from a data parallel perspective, where b...
Recent large language models have been trained on vast datasets, but also often on repeated data, ei...
Machine learning (ML), a computational self-learning platform, is expected to be applied in a variet...
A major bottleneck to applying advanced ML programs at industrial scales is the migration of an acad...
<p>In distributed ML applications, shared parameters are usually replicated among computing nodes to...
In distributed ML applications, shared parameters are usually replicated among computing nodes to mi...
Cloud intelligence applications often perform iterative computa-tions (e.g., PageRank) on constantly...
<p>Many modern machine learning (ML) algorithms are iterative, converging on a final solution via ma...