Guided Optimization for Image Processing Pipelines
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2021
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| _version_ | 1866916471293083648 |
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| author | Ikarashi, Yuka Ragan-Kelley, Jonathan Fukusato, Tsukasa Kato, Jun Igarashi, Takeo |
| author_facet | Ikarashi, Yuka Ragan-Kelley, Jonathan Fukusato, Tsukasa Kato, Jun Igarashi, Takeo |
| contents | Writing high-performance image processing code is challenging and labor-intensive. The Halide programming language simplifies this task by decoupling high-level algorithms from "schedules" which optimize their implementation. However, even with this abstraction, it is still challenging for Halide programmers to understand complicated scheduling strategies and productively write valid, optimized schedules. To address this, we propose a programming support method called "guided optimization." Guided optimization provides programmers a set of valid optimization options and interactive feedback about their current choices, which enables them to comprehend and efficiently optimize image processing code without the time-consuming trial-and-error process of traditional text editors. We implemented a proof-of-concept system, Roly-poly, which integrates guided optimization, program visualization, and schedule cost estimation to support the comprehension and development of efficient Halide image processing code. We conducted a user study with novice Halide programmers and confirmed that Roly-poly and its guided optimization was informative, increased productivity, and resulted in higher-performing schedules in less time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2107_12567 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Guided Optimization for Image Processing Pipelines Ikarashi, Yuka Ragan-Kelley, Jonathan Fukusato, Tsukasa Kato, Jun Igarashi, Takeo Human-Computer Interaction Programming Languages Writing high-performance image processing code is challenging and labor-intensive. The Halide programming language simplifies this task by decoupling high-level algorithms from "schedules" which optimize their implementation. However, even with this abstraction, it is still challenging for Halide programmers to understand complicated scheduling strategies and productively write valid, optimized schedules. To address this, we propose a programming support method called "guided optimization." Guided optimization provides programmers a set of valid optimization options and interactive feedback about their current choices, which enables them to comprehend and efficiently optimize image processing code without the time-consuming trial-and-error process of traditional text editors. We implemented a proof-of-concept system, Roly-poly, which integrates guided optimization, program visualization, and schedule cost estimation to support the comprehension and development of efficient Halide image processing code. We conducted a user study with novice Halide programmers and confirmed that Roly-poly and its guided optimization was informative, increased productivity, and resulted in higher-performing schedules in less time. |
| title | Guided Optimization for Image Processing Pipelines |
| topic | Human-Computer Interaction Programming Languages |
| url | https://arxiv.org/abs/2107.12567 |