NDLI: Dynamic Partitioning-based JPEG Decompression on Heterogeneous Multicore Architectures

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Dynamic Partitioning-based JPEG Decompression on Heterogeneous Multicore Architectures

Content Provider	ACM Digital Library
Author	Kim, Shin-Dug Hong, Jingun Burgstaller, Bernd Chung, Seongwook Lim, Yeongkyu Sodsong, Wasuwee
Abstract	With the emergence of social networks and improvements in computational photography, billions of JPEG images are shared and viewed on a daily basis. Desktops, tablets and smartphones constitute the vast majority of hardware platforms used for displaying JPEG images. Despite the fact that these platforms are heterogeneous multicores, no approach exists yet that is capable of joining forces of a system's CPU and GPU for JPEG decoding. In this paper we introduce a novel JPEG decoding scheme for heterogeneous architectures consisting of a CPU and an OpenCL-programmable GPU. We employ an offline profiling step to determine the performance of a system's CPU and GPU with respect to JPEG decoding. For a given JPEG image, our performance model uses (1) the CPU and GPU performance characteristics, (2) the image entropy and (3) the width and height of the image to balance the JPEG decoding workload on the underlying hardware. Our runtime partitioning and scheduling scheme exploits task, data and pipeline parallelism by scheduling the non-parallelizable entropy decoding task on the CPU, whereas inverse cosine transformations (IDCTs), color conversions and upsampling are conducted on both the CPU and the GPU. Our kernels have been optimized for GPU memory hierarchies. We have implemented the proposed method in the context of the libjpeg-turbo library, which is an industrial-strength JPEG encoding and decoding engine. Libjpeg-turbo's hand-optimized SIMD routines for ARM and x86 architectures constitute a competitive yardstick for the comparison to the proposed approach. Retro-fitting our method with libjpeg-turbo provides insights on the software-engineering aspects of re-engineering legacy code for heterogeneous multicores. We have evaluated our approach for a total of 7194 JPEG images across three high- and middle-end CPU--GPU combinations. We achieve speedups of up to 4.2x over the SIMD-version of libjpeg-turbo, and speedups of up to 8.5x over its sequential code. Taking into account the non-parallelizable JPEG entropy decoding part, our approach achieves up to 95% of the theoretically attainable maximal speedup, with an average of 88%.
Starting Page	80
Ending Page	91
Page Count	12
File Format	PDF
ISBN	9781450326575
DOI	10.1145/2560683.2560684
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-02-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Jpeg decoding Gpu programming Heterogeneous multicores Dynamic work partitioning Opencl Offline profiling
Content Type	Text
Resource Type	Article

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