OCR filled the GPU: 9 GB for the mobile model on gpu-bench's 30 images, past
16 GB for the server model, which Windows then paged into shared memory (1.5 to
11 s a capture). onnxruntime's logged arena extensions showed why: the arena
keeps every block it takes from CUDA and never joins or frees them, and OCR's
inputs grow from run to run (recognizer batches went narrowest first; images in
any order across calls), so each larger run left another block behind: 5.7 GB
of recognizer blocks where the widest batch needs 0.9 GB.
- Detection runs the largest images first, recognition the widest batches.
- The detector's and recognizer's arenas shrink once each stage of an
ocr_process call is done, by a run on a small input with
memory.enable_memory_arena_shrinkage. Shrinking after every run cost 33 to
59% of the speed.
- The worker threads are made with the engine and kept, not made for every
stage of every call (onnxruntime keeps GPU state for each thread that runs a
session, microsoft/onnxruntime#33037); 6% faster.
- OCR's arenas grow by what is asked (kSameAsRequested).
- ort.log_level sets what the engines' onnxruntime environments log; INFO shows
every arena extension.
Mobile OCR on gpu-bench (RTX 5080 laptop, 4 workers): peak 5.7 GB over the
baseline, 2 GB between calls, 142 ms a capture, against 9.0 GB and 126 ms. The
text read differs by a line in 2,986 (batches are grouped differently). The
server model is not yet measured again.
