JParEnt's entropy data scan consumes 45% of the entropy decoding time of libjpeg-turbo on average. For a selection of more than 1000 JPEG images, JParEnt outperforms the SIMD–implementation of the libjpeg-turbo library by up to a factor of 4.3×, and the previously fastest JPEG decompression method for heterogeneous multicores by up to a factor of 2.2×. Our experimental evaluation of JParEnt was conducted on six heterogeneous multicore systems: one server and two desktops with dedicated GPUs, one desktop with an IGP, and two embedded systems. For systems providing a unified address space between CPU and GPU, we employ zero-copy to completely eliminate the data transfer overhead. We leverage additional parallelism through pipelined execution across CPU and GPU.
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This configuration has become common with the advent of SoCs with integrated graphics processors (IGPs). We introduce a dynamic workload partitioning scheme to account for GPUs of low compute power relative to the CPU.
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The block boundary scan constitutes a reinterpretation of the Huffman-coded entropy data to determine codeword boundaries in the bitstream. JParEnt conducts JPEG decompression in two steps: (1) an efficient sequential scan of the entropy data on the CPU to determine the start-positions (boundaries) of coefficient blocks in the bitstream, followed by (2) a parallel entropy decoding step on the graphics processing unit (GPU). We present JParEnt, a new approach to parallel entropy decoding for JPEG decompression on heterogeneous multicores. To determine the start position of a codeword in the bitstream, the previous codeword must be decoded first. Huffman codewords are of variable length, which makes parallel entropy decoding a difficult problem.
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The JPEG format employs Huffman codes to compress the entropy data of an image.