arXiv:2605.08731v2 Announce Type: replace-cross Abstract: JPEG decode is routine ML infrastructure, but Python decoder choices are often justified by single-process, single-thread microbenchmarks. We audit this evaluation assumption with thirteen Python-accessible JPEG decode paths on five matched 16 vCPU Google Cloud CPUs: Intel Emerald Rapids, AMD Zen 4, AMD Zen 5, ARM Neoverse V2, and ARM Neoverse N1. ImageNet validation is the workload, not a new dataset contribution: each run decodes the full 50,000-image split from memory and reports single-thread throughput for all decoders, PyTorch \te
The proliferation of ML applications and the increasing scale of datasets make efficient data loading critical, coinciding with new CPU architectures becoming widely available.
Optimizing fundamental ML infrastructure components like JPEG decoding directly impacts training efficiency, cost, and the effective utilization of compute resources across various hardware architectures.
This research highlights that current assumptions about ML data loading performance are flawed, necessitating a re-evaluation of decoder choices and system configurations to maximize training throughput.
- · Developers optimizing ML infrastructure
- · Cloud providers offering diverse CPU architectures
- · Open-source projects developing optimized JPEG decoders
- · ML practitioners relying on sub-optimal default decoders
- · Inflexible ML training pipelines
- · Cloud providers with unoptimized offerings
Improved understanding of ML data loader performance across different CPU architectures.
Revision of best practices for ML model training, focusing on data loading strategy and decoder selection.
Potential shifts in preferred cloud computing instances for ML workloads based on data loading efficiency rather than just raw FLOPS.
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Read at arXiv cs.LG