Towards unified ad-hoc data processing

Shi, Xiaogang; Cui, Bin; Dobbie, Gillian; Ooi, Beng Chin

doi:10.1145/2588555.2610492

Cited by 5 publications

(1 citation statement)

References 31 publications

(28 reference statements)

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“…Naturally, a higher-level abstraction provides more space for optimization. For example, query planning [42,31], lazy evaluation [57], materialization [55] and operator optimization [1] could be considered in a fine-grained manner.…”

Section: Introductionmentioning

confidence: 99%

Model slicing for supporting complex analytics with elastic inference cost and resource constraints

et al. 2019

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Deep learning models have been used to support analytics beyond simple aggregation, where deeper and wider models have been shown to yield great results. These models consume a huge amount of memory and computational operations. However, most of the large-scale industrial applications are often computational budget constrained. In practice, the peak workload of inference service could be 10x higher than the average cases, with the presence of unpredictable extreme cases. Lots of computational resources could be wasted during off-peak hours and the system may crash when the workload exceeds system capacity. How to support deep learning services with dynamic workload cost-efficiently remains a challenging problem. In this paper, we address the challenge with a general and novel training scheme called model slicing , which enables deep learning models to provide predictions within the prescribed computational resource budget dynamically. Model slicing could be viewed as an elastic computation solution without requiring more computational resources. Succinctly, each layer in the model is divided into groups of contiguous block of basic components (i.e. neurons in dense layers and channels in convolutional layers), and then partially ordered relation is introduced to these groups by enforcing that groups participated in each forward pass always starts from the first group to the dynamically-determined rightmost group. Trained by dynamically indexing the rightmost group with a single parameter slice rate , the network is engendered to build up group-wise and residual representation. Then during inference, a sub-model with fewer groups can be readily deployed for efficiency whose computation is roughly quadratic to the width controlled by the slice rate. Extensive experiments show that models trained with model slicing can effectively support on-demand workload with elastic inference cost.

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Section: Introductionmentioning

confidence: 99%

Model slicing for supporting complex analytics with elastic inference cost and resource constraints

et al. 2019

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Extracting Equivalent SQL from Imperative Code in Database Applications

Emani

Ramachandra

Bhattacharya

et al. 2016

Proceedings of the 2016 International Conference on Management of Data

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Sloth

Cheung

Madden

Solar-Lezama

2016

ACM Trans. Database Syst.

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Many web applications store persistent data in databases. During execution, such applications spend a significant amount of time communicating with the database for retrieval and storing of persistent data over the network. These network round-trips represent a significant fraction of the overall execution time for many applications (especially those that issue a lot of database queries) and, as a result, increase their latency. While there has been prior work that aims to eliminate round-trips by batching queries, they are limited by (1) a requirement that developers manually identify batching opportunities, or (2) the fact that they employ static program analysis techniques that cannot exploit many opportunities for batching, as many of these opportunities require knowing precise information about the state of the running program. In this article, we present S loth , a new system that extends traditional lazy evaluation to expose query batching opportunities during application execution, even across loops, branches, and method boundaries. Many such opportunities often require expensive and sophisticated static analysis to recognize from the application source code. Rather than doing so, S loth instead makes use of dynamic analysis to capture information about the program state and, based on that information, decides how to batch queries and when to issue them to the database. We formalize extended lazy evaluation and prove that it preserves program semantics when executed under standard semantics. Furthermore, we describe our implementation of S loth and our experience in evaluating S loth using over 100 benchmarks from two large-scale open-source applications, in which S loth achieved up to a 3 × reduction in page load time by delaying computation using extended lazy evaluation.

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Towards unified ad-hoc data processing

Cited by 5 publications

References 31 publications

Model slicing for supporting complex analytics with elastic inference cost and resource constraints

Model slicing for supporting complex analytics with elastic inference cost and resource constraints

Extracting Equivalent SQL from Imperative Code in Database Applications

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