Dynamic partitioning‐based JPEG decompression on heterogeneous multicore architectures

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 OpenCLprogrammable 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 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%.

JParEnt: Parallel entropy decoding for JPEG decompression on heterogeneous multicore architectures

“…The performance improvement of JParEnt over our previous work on parallel JPEG decoding is shown in Figure . JParEnt parallelizes entropy decoding, which was the performance roadblock in our previous work.…”

“…On Windows, we compiled our code and the JPEG library on Cygwin . To compare our results to the state‐of‐the‐art, we integrated our entropy decoding scheme with the parallel JPEG decompression method by Sodsong, Hong et al We compared our performance to libjpeg‐turbo (ie, SeqEnt: sequential entropy decoding and SIMD‐accelerated IDCT, upsampling and color‐conversion).…”

“…Speedup of JParEnt over the parallel JPEG decoder proposed by Sodsong, Hong et al Please note that the y‐axis scales are different across the diagrams, to facilitate reading…”

“…The only visible CPU‐to‐GPU communication is the transfer of the DCT block boundary positions, which become available only after the boundary scan has completed. This transfer is significantly smaller than our previous approach, where compressed DCT coefficients had to be transferred …”

“…In previous work, we have devised a JPEG decoder capable of accelerating inverse discrete cosine transformations (IDCT), image upsampling and YCbCr to RGB color space conversions on systems consisting of a CPU and a GPU. Entropy decoding is the only remaining sequential task with this decoding scheme and thus stands in the way to further speedups.…”