[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.
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.
Releases notes are published on GitHub releases.
| Package | Release status |
|---|---|
| GitHub release (stable) | |
| GitHub release (nightly) | |
iree-base-compiler | |
iree-base-runtime |
For more details on the release process, see https://iree.dev/developers/general/release-management/.
| Operating system | Build status |
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| Linux | |
| macOS | |
| macOS |
For the full list of workflows see https://iree.dev/developers/general/github-actions/.
See our website for more information.
Community meeting recordings: IREE YouTube channel
| Date | Title | Recording | Slides |
|---|---|---|---|
| 2025-06-10 | Data-Tiling in IREE: Achieving High Performance Through Compiler Design (AsiaLLVM) | recording | slides |
| 2025-05-17 | Introduction to GPU architecture and IREE's GPU CodeGen Pipeline | recording | slides |
| 2025-02-12 | The Long Tail of AI: SPIR-V in IREE and MLIR (Vulkanised) | recording | slides |
| 2024-10-01 | Unveiling the Inner Workings of IREE: An MLIR-Based Compiler for Diverse Hardware | recording | |
| 2021-06-09 | IREE Runtime Design Tech Talk | recording | slides |
| 2020-08-20 | IREE CodeGen (MLIR Open Design Meeting) | recording | slides |
| 2020-03-18 | Interactive HAL IR Walkthrough | recording | |
| 2020-01-31 | End-to-end MLIR Workflow in IREE (MLIR Open Design Meeting) | recording | slides |
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.