Multi-threading and multi-processing for faster parsing

### Question
I am using the latest version of Docling to convert a PDF of about 300 pages that has more images, tables, code blocks.It takes a lot of time to complete the conversion of a file, can I improve the speed effectively

```
Python Version: 3.12.3
docling Version: 2.28.2
OS: Ubuntu 24.04.2 LTS
```

```
(docling_env) xlab@xlab:/mnt/Agent/Docling$ docling --pipeline vlm --vlm-model smoldocling --device cuda -vv --from pdf --to md --num-threads 64 ug901-vivado-synthesis.pdf
INFO:docling.document_converter:Going to convert document batch...
INFO:docling.document_converter:Initializing pipeline for VlmPipeline with options hash 46a3b6d819846b105bc3bda802a8f22e
INFO:docling.utils.accelerator_utils:Accelerator device: 'cuda:0'
DEBUG:docling.models.hf_vlm_model:Available device for HuggingFace VLM: cuda:0
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): huggingface.co:443
DEBUG:urllib3.connectionpool:https://huggingface.co:443 "GET /api/models/ds4sd/SmolDocling-256M-preview/revision/main HTTP/1.1" 200 4311
INFO:docling.pipeline.base_pipeline:Processing document ug901-vivado-synthesis.pdf
Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.
DEBUG:docling.pipeline.base_pipeline:Finished converting page batch time=82009.799
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DEBUG:docling.pipeline.base_pipeline:Finished converting page batch time=87096.757
INFO:docling.document_converter:Finished converting document ug901-vivado-synthesis.pdf in 5145.76 sec.
INFO:docling.cli.main:writing Markdown output to ug901-vivado-synthesis.md
INFO:docling.cli.main:Processed 1 docs, of which 0 failed
INFO:docling.cli.main:All documents were converted in 5201.73 seconds.
```