[HAL] Allocation Pools And Async Frontier Substrate (#24236)

This PR introduces the generic allocation-pool substrate and the async
frontier machinery needed to schedule queue work without baking
allocation policy or dependency lifetime assumptions into individual
drivers.

## Why

The old HAL allocator surface mixed backing allocation, queue allocation
policy, and driver-specific pool behavior. That makes it hard to express
memory pressure without turning ordinary capacity outcomes into errors,
and it makes queue implementations grow local policy that should be
shared.

The new substrate separates those concerns: pool providers expose
backing memory, pools implement allocation policy, and queue/device
integration passes pool handles instead of implicit bitmasks. Capacity
pressure is represented as a pool acquisition result, not an
`iree_status_t`.

## What's Here

Async frontiers gain fixed storage helpers, status-free merge overflow
results, ref-counted frontier trackers, and explicit notification
observe/check/wait tokens. That gives queue scheduling code a shared
dependency substrate without borrowed-lifetime traps or hidden wake
behavior.

HAL gains `iree_hal_pool_t`, pool sets, slab providers/caches, CPU slab
providers, passthrough pools, fixed-block pools, and TLSF pools.
Oversized allocation routing is explicit instead of pretending a TLSF
slab can handle
requests larger than the slab itself.

Allocator and device integration moves queue allocation pools to
explicit pool handles. Existing drivers keep the least backend-specific
policy necessary to preserve current behavior.

The io_uring notification path intentionally stays eventfd-backed.
Validation found that FUTEX_WAIT relay registration has no
userspace-visible "armed" edge, so a register-then-signal sequence can
lose the wake before the SQE is active.
tree: 8b3054709d7b287718e79b73b66fd66b529e8812
  1. .github/
  2. build_tools/
  3. compiler/
  4. docs/
  5. experimental/
  6. integrations/
  7. lib/
  8. llvm-external-projects/
  9. runtime/
  10. samples/
  11. tests/
  12. third_party/
  13. tools/
  14. .bazel_to_cmake.cfg.py
  15. .bazelignore
  16. .bazelrc
  17. .bazelversion
  18. .clang-format
  19. .git-blame-ignore-revs
  20. .gitattributes
  21. .gitignore
  22. .gitmodules
  23. .pre-commit-config.yaml
  24. .yamllint.yml
  25. AUTHORS
  26. BUILD.bazel
  27. CITATION.cff
  28. CMakeLists.txt
  29. configure_bazel.py
  30. CONTRIBUTING.md
  31. LICENSE
  32. MAINTAINERS.md
  33. MODULE.bazel
  34. README.md
  35. RELEASING.md
README.md

IREE: Intermediate Representation Execution Environment

IREE (Intermediate Representation Execution Eenvironment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.

See our website for project details, user guides, and instructions on building from source.

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For more details on the release process, see https://iree.dev/developers/general/release-management/.

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  • MLIR topic within LLVM Discourse: IREE is enabled by and heavily relies on MLIR. IREE sometimes is referred to in certain MLIR discussions. Useful if you are also interested in MLIR evolution.

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IREE Architecture IREE Architecture

See our website for more information.

Presentations and talks

Community meeting recordings: IREE YouTube channel

DateTitleRecordingSlides
2025-06-10Data-Tiling in IREE: Achieving High Performance Through Compiler Design (AsiaLLVM)recordingslides
2025-05-17Introduction to GPU architecture and IREE's GPU CodeGen Pipelinerecordingslides
2025-02-12The Long Tail of AI: SPIR-V in IREE and MLIR (Vulkanised)recordingslides
2024-10-01Unveiling the Inner Workings of IREE: An MLIR-Based Compiler for Diverse Hardwarerecording
2021-06-09IREE Runtime Design Tech Talkrecordingslides
2020-08-20IREE CodeGen (MLIR Open Design Meeting)recordingslides
2020-03-18Interactive HAL IR Walkthroughrecording
2020-01-31End-to-end MLIR Workflow in IREE (MLIR Open Design Meeting)recordingslides

License

IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.