Machine Learning in Compiler Optimization

Wang, Zheng; O’Boyle, Michael

doi:10.1109/jproc.2018.2817118

Cited by 149 publications

(111 citation statements)

References 196 publications

(264 reference statements)

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“…Energy and power optimization for embedded and mobile systems is an intensely studied field. There is a wide range of activities on exploiting compiler-based code optimization [12], [13], runtime task scheduling [14], [15], or a combination of both [7] to optimize different workloads for energy efficiency. Other relevant work in web browsing optimization exploits application knowledge to batch network communications [16], [17], and parallel downloading [18], which primarily target the initial page loading phase.…”

Section: A Energy Optimizationmentioning

confidence: 99%

“…Specifically, PES employs an analytical model to choose the operating frequency of the processor to reduce energy consumption. Developing an effective analytical model requires deep knowledge of the underlying hardware and the application domains [7]. As a result, PES offers a poor hardware portability because tuning a model for a new hardware platform could involve significant overhead.…”

Section: Introductionmentioning

confidence: 99%

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Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Systems

et al. 2019

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The web has become a ubiquitous application development platform for mobile systems. Yet, web access on mobile devices remains an energy-hungry activity. Prior work in the field mainly focuses on the initial page loading stage, but fails to exploit the opportunities for energy-efficiency optimization while the user is interacting with a loaded page. This paper presents a novel approach for performing energy optimization for interactive mobile web browsing. At the heart of our approach is a set of machine learning models, which estimate at runtime the frames per second for a given user interaction input by running the computation-intensive web render engine on a specific processor core under a given clock speed. We use the learned predictive models as a utility function to quickly search for the optimal processor setting to carefully trade responsive time for reduced energy consumption. We integrate our techniques to the opensource Chromium browser and apply it to two representative mobile user events: scrolling and pinching (i.e., zoom in and out). We evaluate the developed system on the landing pages of the top-100 hottest websites and two big.LITTLE heterogeneous mobile platforms. Our extensive experiments show that the proposed approach reduces the system-wide energy consumption by over 36% on average and up to 70%. This translates to an over 17% improvement on energy-efficiency over a state-of-the-art event-based web browser scheduler, but with significantly fewer violations on the quality of service.

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Section: A Energy Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Systems

et al. 2019

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“…Our approach avoids the pitfall by automatically learning how to best schedule rendering process. There are also works use statistical modeling or control theories to optimize energy efficiency on mobiles [16], [17], [18], [19], [20], [21]. While not specific to web browsing, these studies demonstrate the advantages and needs for adaptive systemlevel optimizations.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Web Browsing on Mobile Heterogeneous Multi-cores

Ren¹,

Wang²,

Fang³

et al. 2024

IEEE Comput. Arch. Lett.

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Web browsing is an important application domain, but it imposes a significant power burden on mobile devices. While heterogeneous multi-cores offer the potential for energy-efficient computing, existing web browsers fail to exploit the hardware to optimize mobile web browsing. Our work aims to offer a better way to optimize web browsing on heterogeneous mobile devices. We achieve this by developing a machine learning based approach to predict the optimal processor setting for rendering the web content. The prediction is based on the web content, the network status and the optimization goal. We evaluate our approach by applying it to the Chromium browser and testing it on a representative big.LITTLE mobile platform. We apply our approach to the top 1,000 hottest websites across seven typical networking environments. Our approach achieves over 80% of the performance delivered by a perfect predictor. Our approach achieves over 30%, 50%, and 60% improvement respectively for load time, energy consumption and the energy delay product when compared to two state-of-the-art approaches.

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“…Predictive Modeling. Recent studies have shown that machine learning based predictive modeling is effective in code optimization [43], [44], performance predicting [45], [46], parallelism mapping [20], [47], [48], [49], [50], and task scheduling [51], [52], [53], [54], [55], [56]. Its great advantage is its ability to adapt to the ever-changing platforms as it has no prior assumption about their behavior.…”

Section: Domain-specific Optimizationsmentioning

confidence: 99%

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Zhang

Fang

Yang

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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As many-core accelerators keep integrating more processing units, it becomes increasingly more difficult for a parallel application to make effective use of all available resources. An effective way for improving hardware utilization is to exploit spatial and temporal sharing of the heterogeneous processing units by multiplexing computation and communication tasks -a strategy known as heterogeneous streaming. Achieving effective heterogeneous streaming requires carefully partitioning hardware among tasks, and matching the granularity of task parallelism to the resource partition. However, finding the right resource partitioning and task granularity is extremely challenging, because there is a large number of possible solutions and the optimal solution varies across programs and datasets. This article presents an automatic approach to quickly derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogeneous many-core architectures. Our approach employs a performance model to estimate the resulting performance of the target application under a given resource partition and task granularity configuration. The model is used as a utility to quickly search for a good configuration at runtime. Instead of hand-crafting an analytical model that requires expert insights into low-level hardware details, we employ machine learning techniques to automatically learn it. We achieve this by first learning a predictive model offline using training programs. The learnt model can then be used to predict the performance of any unseen program at runtime. We apply our approach to 39 representative parallel applications and evaluate it on two representative heterogeneous many-core platforms: a CPU-XeonPhi platform and a CPU-GPU platform. Compared to the single-stream version, our approach achieves, on average, a 1.6x and 1.1x speedup on the XeonPhi and the GPU platform, respectively. These results translate to over 93% of the performance delivered by a theoretically perfect predictor.

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Machine Learning in Compiler Optimization

Cited by 149 publications

References 196 publications

Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Systems

Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Systems

Adaptive Web Browsing on Mobile Heterogeneous Multi-cores

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

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