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34 changes: 33 additions & 1 deletion CHANGELOG.md
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## [4.6.0](https://github.com/NVIDIA/cutlass/releases/tag/v4.6.0) (2026-07-01)

* Release [documentation](https://docs.nvidia.com/cutlass/latest/master/media/docs/cpp/gemm_performance_measurement_methodology_guidelines.md) that explains how to accurately profiling GEMM performance.

### CuTe DSL
* New features
- New fine-grained compilation API: cute.compile_to that gives control over the what stage the compiler outputs. This feature allows customization of the path from compilation to runtime execution. cute.compile_to is considered experimental in 4.6.
- Experimental Feature: Added the IKET (In-Kernel-Event-Tracing) profiler for instrumentation-based intra-kernel activities tracing. This enables fine-grained profiling and makes it easier to understand persistent, warp-specialized kernels' performance. This is a beta feature provided by CUTLASS Python until a NVIDIA DevTools product is released, there is no guarantee that this interface will remain stable!
- Distribute compiler binaries to accomany cute.compile_to allowing users to build customized compile-exececute pipelines outside of Python. Both static and shared compiler and executor/runtime libraries will be provided. Compiler binaries will be uploaded to GitHub with each release.
- Supported AoT cross-compilation for aarch64‑linux‑gnu
- Supported AoT cross-compilation for aarch64-linux-gnu
- Support for two launch attributes: launch completion events (cudaLaunchAttributeLaunchCompletionEvent), for recording an event once all thread blocks have begun executing, and launch programatic events (cudaLaunchAttributeProgrammaticEvent), for PDL event-based synchronization
- Supported auto calculating per-kernel shared memory carveout preference, or use new laucnch option `preferred_smem_carveout` to set manually.
- Auto-deduced smem size for launching kernels
Expand Down Expand Up @@ -73,6 +75,12 @@ to discover CUTLASS Python DSL kernels & integrate them in your code.
* Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
* Optimal code generation with CUDA toolkit versions 13.3.

## [4.5.3](https://github.com/NVIDIA/cutlass/releases/tag/v4.5.3) (2026-07-03)

### CuTe DSL
* Bug fixing and improvements
- Fixed a compilation time regression issue in 4.5.0. Compilation times now match those in the 4.4 and 4.6 branches.

## [4.5.2](https://github.com/NVIDIA/cutlass/releases/tag/v4.5.2) (2026-05-22)

### CuTe DSL
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* Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
* Optimal code generation with CUDA toolkit versions 13.2.

## [4.4.3](https://github.com/NVIDIA/cutlass/releases/tag/v4.4.3) (2026-06-15)

### CUTLASS C++
* Make [version.h](https://github.com/NVIDIA/cutlass/blob/release/4.4/include/cutlass/version.h) NVRTC JIT compilation compatible.

## [4.4.2](https://github.com/NVIDIA/cutlass/releases/tag/v4.4.2) (2026-03-13)

### CuTe DSL
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* Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
* Optimal code generation with CUDA toolkit versions 13.1.

## [4.3.6](https://github.com/NVIDIA/cutlass/releases/tag/v4.3.6) (2026-06-17)

### CUTLASS C++
* Make [version.h](https://github.com/NVIDIA/cutlass/blob/release/4.4/include/cutlass/version.h) NVRTC JIT compilation compatible.
* Allow linking large cutlass library on 64bit platform.
* Fix alignment-related miscalculation for pipeline stages of Blackwell blockscaled GEMM.

## [4.3.5](https://github.com/NVIDIA/cutlass/releases/tag/v4.3.5) (2026-01-09)

### CuTe DSL
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* Work around a driver TMA descriptor related bug which will cause occasional errors on Blackwell when the tensor's backing memory allocation is less than 128KB and it is not a dense non-overlapping tensor.

## [4.3.3](https://github.com/NVIDIA/cutlass/releases/tag/v4.3.3) (2025-12-12)

### CuTe DSL
* New features
- Supported namedtuple and kwargs for JIT function arguments in tvm-ffi
- Supported variadic tuples for JIT function argument in tvm-ffi
Expand All @@ -338,6 +360,8 @@ to discover CUTLASS Python DSL kernels & integrate them in your code.
- Clearer error message for the case of runtime error cudaErrorInsufficientDriver

## [4.3.2](https://github.com/NVIDIA/cutlass/releases/tag/v4.3.2) (2025-12-05)

### CuTe DSL
* New features
- New env var `CUTE_DSL_CACHE_DIR` to specify the path for dumping caches

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* Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
* Optimal code generation with CUDA toolkit versions 13.0U1.

## [4.2.2](https://github.com/NVIDIA/cutlass/releases/tag/v4.2.2) (2026-06-20)

### CUTLASS C++
* Make [version.h](https://github.com/NVIDIA/cutlass/blob/release/4.2/include/cutlass/version.h) NVRTC JIT compilation compatible.
* Allow linking large cutlass library on 64bit platform.
* Fix alignment-related miscalculation for pipeline stages of Blackwell blockscaled GEMM.
* Fix for blockwise group gemm nosmem epilogues and no sfd with nosmem group gemm epilogues.

## [4.2.1](https://github.com/NVIDIA/cutlass/releases/tag/v4.2.1) (2025-09-22)

### CuTe DSL
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4 changes: 3 additions & 1 deletion README.md
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# CUTLASS 4.6.0

_CUTLASS 4.6.0 - June 2026_
_CUTLASS 4.6.0 - July 2026_

CUTLASS is a collection of abstractions for implementing high-performance matrix-matrix multiplication (GEMM)
and related computations at all levels and scales within CUDA. It incorporates strategies for
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# What's New in CUTLASS 4.6

* Release [documentation](https://docs.nvidia.com/cutlass/latest/master/media/docs/cpp/gemm_performance_measurement_methodology_guidelines.md) that explains how to accurately profiling GEMM performance.

## CuTe DSL
* New features
- New fine-grained compilation API: cute.compile_to that gives control over the what stage the compiler outputs. This feature allows customization of the path from compilation to runtime execution. cute.compile_to is considered experimental in 4.6.
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