Choosing a JPEG Decoder for PyTorch DataLoaders: Workload-Specific Throughput on Four CPUs

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arXiv:2605.08731v3 Announce Type: replace-cross Abstract: A JPEG decoder benchmark can combine worker counts, CPUs, and datasets in one large result matrix. We simplify that comparison by fixing a PyTorch DataLoader at eight workers and asking one question: how much faster is each decoder than Pillow on the same CPU and JPEG workload? We benchmark 12 Python decoders on four 16-vCPU Google Cloud platforms and two workloads from the Forchheim Image Database. The first contains 324 large camera originals. The second contains 1,668 originals and copies processed by social media services. Every decoder-CPU-workload setting has five isolated repetitions. JPEG bytes reside in memory, and each timed decode returns a complete three-channel RGB array. The workload changes the practical result. On camera originals, the geometric mean across CPUs ranges from 1.00 to 1.09 times Pillow. On the mixed workload, simplejpeg, imagecodecs, and turbojpeg reach 1.39 - 1.40 Pillow. The exact leader still changes by CPU. We therefore recommend comparing decoders on representative JPEGs, while using the fixed eight-worker results as a compact starting point. Full worker curves and absolute throughput tables are in the appendix.

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