The JPEG still picture compression standard
Communications of the ACM - Special issue on digital multimedia systems
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
Architectural exploration of heterogeneous multiprocessor systems for JPEG
International Journal of Parallel Programming - Special Issue on Multiprocessor-based embedded systems
Parallel Image Processing Based on CUDA
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 03
Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping
Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture
Finding a needle in Haystack: facebook's photo storage
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer
ISPASS '11 Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software
On the characterization of statistically synchronizable variable-length codes
IEEE Transactions on Information Theory
Accelerating MapReduce on a coupled CPU-GPU architecture
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Cooperative heterogeneous computing for parallel processing on CPU/GPU hybrids
INTERACT '12 Proceedings of the 2012 16th Workshop on Interaction between Compilers and Computer Architectures (INTERACT)
CAP: co-scheduling based on asymptotic profiling in CPU+GPU hybrid systems
Proceedings of the 2013 International Workshop on Programming Models and Applications for Multicores and Manycores
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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%.