Toward a progress indicator for program compilation

Luo, Gang; Chen, Tong; Yu, Han‐Qing

doi:10.1002/spe.792

Cited by 8 publications

(8 citation statements)

References 14 publications

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“…For machine learning model training, we have built sophisticated progress indicators for decision tree, random forest, and neural network when the number of epochs needed for model training is known beforehand [7], [11]. In addition, sophisticated progress indicators have been proposed for database queries [8], [14]- [17], static program analysis [18], program compilation [19], subgraph queries [20], MapReduce jobs [21], [22], and automatic machine learning model selection [23], [24]. As each kind of task has its own unique properties, we cannot directly adopt the existing techniques [7], [8], [11], [14]- [24] to implement progress indicators for deep learning model training when early stopping is allowed.…”

Section: A Sophisticated Progress Indicatorsmentioning

confidence: 99%

Progress Indication for Deep Learning Model Training: A Feasibility Demonstration

Dong

Luo

2020

IEEE Access

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Deep learning is the state-of-the-art learning algorithm for many machine learning tasks. Yet, training a deep learning model on a large data set is often time-consuming, taking several days or even months. During model training, it is desirable to offer a non-trivial progress indicator that can continuously project the remaining model training time and the fraction of model training work completed. This makes the training process more user-friendly. In addition, we can use the information given by the progress indicator to assist in workload management. In this paper, we present the first set of techniques to support non-trivial progress indicators for deep learning model training when early stopping is allowed. We report an implementation of these techniques in TensorFlow and our evaluation results for both convolutional and recurrent neural networks. Our experiments show that our progress indicator can offer useful information even if the run-time system load varies over time. In addition, the progress indicator can self-correct its initial estimation errors, if any, over time.

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Section: A Sophisticated Progress Indicatorsmentioning

confidence: 99%

Progress Indication for Deep Learning Model Training: A Feasibility Demonstration

Dong

Luo

2020

IEEE Access

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“…Researchers have built sophisticated progress indicators for database queries [5, 12, 16–18], subgraph queries [33], static program analysis [13], program compilation [15], and MapReduce jobs [21, 22]. In addition, we have designed sophisticated progress indicators for automatic machine learning model selection [14, 19].…”

Section: Related Workmentioning

confidence: 99%

Progress Indication for Machine Learning Model Building

Luo

2018

SIGKDD Explor. Newsl.

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Progress indicators are desirable for machine learning model building that often takes a long time, by continuously estimating the remaining model building time and the portion of model building work that has been finished. Recently, we proposed a high-level framework using system approaches to support non-trivial progress indicators for machine learning model building, but offered no detailed implementation technique. It remains to be seen whether it is feasible to provide such progress indicators. In this paper, we fill this gap and give the first demonstration that offering such progress indicators is viable. We describe detailed progress indicator implementation techniques for three major, supervised machine learning algorithms. We report an implementation of these techniques in Weka.

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“…Tools for monitoring the progress of tasks have been studied in various contexts (e.g., HCI [Myers 1985], program compilation [Luo et al 2007], and file downloads) but there exists limited work on this topic in a data management context. Recent work on query progress indicators [Luo et al 2005[Luo et al , 2004Chaudhuri et al 2005Chaudhuri et al , 2004Mishra and Koudas 2007] introduced several novel ideas.…”

Section: Related Workmentioning

confidence: 99%

The design of a query monitoring system

Mishra

Koudas

2009

ACM Trans. Database Syst.

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Query monitoring refers to the problem of observing and predicting various parameters related to the execution of a query in a database system. In addition to being a useful tool for database users and administrators, it can also serve as an information collection service for resource allocation and adaptive query processing techniques. In this article, we present a query monitoring system from the ground up, describing various new techniques for query monitoring, their implementation inside a real database system, and a novel interface that presents the observed and predicted information in an accessible manner. To enable this system, we introduce several lightweight online techniques for progressively estimating and refining the cardinality of different relational operators using information collected at query execution time. These include binary and multiway joins as well as typical grouping operations and combinations thereof. We describe the various algorithms used to efficiently implement estimators and present the results of an evaluation of a prototype implementation of our framework in an open-source data management system. Our results demonstrate the feasibility and practical utility of the approach presented herein.

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Toward a progress indicator for program compilation

Cited by 8 publications

References 14 publications

Progress Indication for Deep Learning Model Training: A Feasibility Demonstration

Progress Indication for Deep Learning Model Training: A Feasibility Demonstration

Progress Indication for Machine Learning Model Building

The design of a query monitoring system

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