Toward Efficient Online Scheduling for Distributed Machine Learning Systems

We consider the question: what is the abstraction that should be implemented by the computational engine of a machine learning system? Current machine learning systems typically push whole tensors through a series of compute kernels such as matrix multiplications or activation functions, where each kernel runs on an AI accelerator (ASIC) such as a GPU. This implementation abstraction provides little built-in support for ML systems to scale past a single machine, or for handling large models with matrices or tensors that do not easily fit into the RAM of an ASIC. In this paper, we present an alternative implementation abstraction called the tensor relational algebra (TRA). The TRA is a set-based algebra based on the relational algebra. Expressions in the TRA operate over binary tensor relations, where keys are multi-dimensional arrays and values are tensors. The TRA is easily executed with high efficiency in a parallel or distributed environment, and amenable to automatic optimization. Our empirical study shows that the optimized TRA-based back-end can significantly outperform alternatives for running ML workflows in distributed clusters.

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Online scheduling of heterogeneous distributed machine learning jobs

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing ◽

10.1145/3397166.3409128 ◽

2020 ◽

Author(s):

Qin Zhang ◽

Ruiting Zhou ◽

Chuan Wu ◽

Lei Jiao ◽

Zongpeng Li

Keyword(s):

Machine Learning ◽

Online Scheduling ◽

Distributed Machine Learning

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Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

OLYMPIADS IN INFORMATICS ◽

10.15388/ioi.2018.07 ◽

2018 ◽

Vol 12 ◽

pp. 85-98

Author(s):

Bojan Kostadinov ◽

Mile Jovanov ◽

Emil STANKOV

Keyword(s):

Machine Learning ◽

Data Collection ◽

Educational Policy ◽

Learning Systems ◽

Data Sources ◽

Or Education ◽

Student Activity ◽

The World ◽

E Learning ◽

Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

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