Multi-threaded "Slice All": Parallel processing for separate build plates #1045

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MrUnknownDE · 2026-04-05T16:43:54+02:00

MrUnknownDE commented

2026-04-05 16:43:54 +02:00

Originally created by @JebTheKerbal on 1/17/2026

Is there an existing issue for this feature request?

I have searched the existing issues

Yes. I frequently print large-scale multi-part projects (such as full cosplay armor sets) that are organized across many separate build plates within a single project file (.3mf).

Currently, when I select "Slice All", OrcaSlicer processes the plates sequentially (Plate 1 -> Plate 2 -> Plate 3). Even with a high-end multi-core CPU (e.g., Ryzen 9 7950X or Intel i9), the slicer often only utilizes a fraction of the available cores because the parallelism is limited to the scope of a single plate. This results in the CPU running at low utilization while the user waits for a long queue of plates to process one by one.

Which printers will be beneficial to this feature?

All

Describe the solution you'd like

I would like an option to enable concurrent/parallel slicing when the "Slice All" command is triggered.

Logic: The slicer should spawn separate slicing worker threads/processes for different plates simultaneously.

Implementation: If I have 16 cores and 8 plates, the software could theoretically slice all 8 plates at the same time, rather than in a linear queue.

Safety: This could be governed by a Preference setting (e.g., "Max concurrent slicing tasks") to prevent system freezing on systems with limited RAM.

Describe alternatives you've considered

Manual Multitasking: Opening 4 separate instances of OrcaSlicer and manually loading different subsets of parts. This breaks the workflow of having a single "master" .3mf file for the assembly.

Status Quo: Waiting for the sequential process to finish, which is inefficient on modern hardware.

Additional context

This feature targets "embarrassingly parallel" workloads. While parallelizing the geometry calculations of a single model has diminishing returns due to dependencies, slicing distinct plates is a perfect candidate for parallelization because Plate A's G-code generation has zero dependency on Plate B.

Expected Benefit: Drastic reduction in total slicing time for multi-plate projects by utilizing idle CPU cores.

*Originally created by @JebTheKerbal on 1/17/2026* ### Is there an existing issue for this feature request? - [x] I have searched the existing issues ### Is your feature request related to a problem? Yes. I frequently print large-scale multi-part projects (such as full cosplay armor sets) that are organized across many separate build plates within a single project file (.3mf). Currently, when I select "Slice All", OrcaSlicer processes the plates sequentially (Plate 1 -> Plate 2 -> Plate 3). Even with a high-end multi-core CPU (e.g., Ryzen 9 7950X or Intel i9), the slicer often only utilizes a fraction of the available cores because the parallelism is limited to the scope of a single plate. This results in the CPU running at low utilization while the user waits for a long queue of plates to process one by one. ### Which printers will be beneficial to this feature? All ### Describe the solution you'd like I would like an option to enable concurrent/parallel slicing when the "Slice All" command is triggered. Logic: The slicer should spawn separate slicing worker threads/processes for different plates simultaneously. Implementation: If I have 16 cores and 8 plates, the software could theoretically slice all 8 plates at the same time, rather than in a linear queue. Safety: This could be governed by a Preference setting (e.g., "Max concurrent slicing tasks") to prevent system freezing on systems with limited RAM. ### Describe alternatives you've considered Manual Multitasking: Opening 4 separate instances of OrcaSlicer and manually loading different subsets of parts. This breaks the workflow of having a single "master" .3mf file for the assembly. Status Quo: Waiting for the sequential process to finish, which is inefficient on modern hardware. ### Additional context This feature targets "embarrassingly parallel" workloads. While parallelizing the geometry calculations of a single model has diminishing returns due to dependencies, slicing distinct plates is a perfect candidate for parallelization because Plate A's G-code generation has zero dependency on Plate B. Expected Benefit: Drastic reduction in total slicing time for multi-plate projects by utilizing idle CPU cores.

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Reference: github/OrcaSlicer#1045