triton Documentation

File: README.md Copy Chapter

Triton Conference 2025

The 3rd Triton Developer Conference took place on October 21, 2025 at the Microsoft Silicon Valley Campus in Mountain View, California.

Conference Materials Copy Section

Conference recordings and materials are now available online:

• Conference Videos: YouTube Playlist
• Conference Slides: Google Drive Folder

For previous conference materials, see:

• 2024 Conference Materials
• 2023 Conference Materials

Triton

This is the development repository of Triton, a language and compiler for writing highly efficient custom Deep-Learning primitives. The aim of Triton is to provide an open-source environment to write fast code at higher productivity than CUDA, but also with higher flexibility than other existing DSLs.

The foundations of this project are described in the following MAPL2019 publication: Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations. Please consider citing this work if you use Triton!

The official documentation contains installation instructions and tutorials. See also these third-party Triton puzzles, which can all be run using the Triton interpreter -- no GPU required.

Quick Installation

You can install the latest stable release of Triton from pip:

pip install triton

Binary wheels are available for CPython 3.10-3.14.

Install from source

git clone https://github.com/triton-lang/triton.git
cd triton

pip install -r python/requirements.txt # build-time dependencies
pip install -e .

Or with a virtualenv:

git clone https://github.com/triton-lang/triton.git
cd triton

python -m venv .venv --prompt triton
source .venv/bin/activate

pip install -r python/requirements.txt # build-time dependencies
pip install -e .

Building with a custom LLVM

Triton uses LLVM to generate code for GPUs and CPUs. Normally, the Triton build
downloads a prebuilt LLVM, but you can also build and use LLVM from source.

LLVM does not have a stable API, so the Triton build will not work at an
arbitrary LLVM version.

For convenience, use the following command to build LLVM and install Triton with the custom LLVM:

make dev-install-llvm

Alternatively, follow these steps to build LLVM from source manually.

1. Find the version of LLVM that Triton builds against. Check the llvm_hash
   field in cmake/llvm-info.json to see the current version. For example, if it
   says:
   "llvm_hash": "49af6502c6dcb4a7f7520178bd14df396f78240c"

   This means that the version of Triton you have builds against
   LLVM 49af6502.

2. git checkout LLVM at this revision. Optionally, make additional
   modifications to LLVM.

3. Build LLVM. For example, you might run:

   text Copy

   $ cd $HOME/llvm-project  # your clone of LLVM.
   $ mkdir build
   $ cd build
   $ cmake -G Ninja -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=ON ../llvm -DLLVM_ENABLE_PROJECTS="mlir;llvm;lld;clang" -DLLVM_TARGETS_TO_BUILD="host;NVPTX;AMDGPU"
   $ ninja

4. Grab a snack, this will take a while.

5. Build Triton as above, but set the following environment variables:

   text Copy

   # Modify as appropriate to point to your LLVM build.
   $ export LLVM_BUILD_DIR=$HOME/llvm-project/build
   
   $ cd 
   $ LLVM_INCLUDE_DIRS=$LLVM_BUILD_DIR/include \
     LLVM_LIBRARY_DIR=$LLVM_BUILD_DIR/lib \
     LLVM_SYSPATH=$LLVM_BUILD_DIR \
     pip install -e .

Tips for building

• Set TRITON_BUILD_WITH_CLANG_LLD=true as an environment variable to use clang
  and lld. lld in particular results in faster builds.

• Set TRITON_BUILD_WITH_CCACHE=true to build with ccache.

• Set TRITON_HOME=/some/path to change the location of the .triton
  directory where Triton's cache is located and downloads are stored
  during the build. By default, this is the user's home directory. It
  can be changed anytime.

• If you're running out of memory when building Triton, specify the MAX_JOBS
  environment variable (to the pip install -e . command) to limit the
  number of jobs.

• Pass --no-build-isolation to pip install to make nop builds faster.
  Without this, every invocation of pip install uses a different symlink to
  cmake, and this forces ninja to rebuild most of the .a files.

• The build system creates a compile_commands.json file under the Triton repo
  directory. This file is used by VSCode IntelliSense and clangd to provide
  code completion and other features for C++ code.

  If IntelliSense does not work, you can try the following steps:

  • Do a local build. Run command pip install -e ..
  • Get the full path to the compile_commands.json file produced by the build:
    find ./build -name 'compile_commands.json' | xargs readlink -f.
    You might get a full path similar to /Users/{username}/triton/build/cmake.macosx-11.1-arm64-cpython-3.12/compile_commands.json.
  • In VSCode, install the
    C/C++
    extension,
    then open the command palette (Shift + Command + P on Mac, or Shift + Ctrl + P on Windows/Linux) and open C/C++: Edit Configurations (UI).
  • Open "Advanced Settings" and paste the full path to
    compile_commands.json into the "Compile Commands" textbox.

Running tests

There currently isn't a turnkey way to run all the Triton tests, but you can
follow the following recipe:

# One-time setup.  Note this will reinstall local Triton because torch
# overwrites it with the public version.
$ make dev-install

# To run all tests (requires a GPU)
$ make test

# Or, to run tests without a gpu
$ make test-nogpu

Tips for hacking

For detailed instructions on how to debug Triton's frontend, please refer to this tutorial. The following includes additional tips for hacking on Triton's backend.

Configuration knobs

See python/triton/knobs.py for the full list of configuration knobs. You can set those knobs directly in python or use environment variables to control them. Below are some of the environment variables you can specify (see knobs.py for the full list):

• MLIR_ENABLE_DUMP=1 dumps the IR before every MLIR pass Triton runs, for all
  kernels. Use MLIR_ENABLE_DUMP=kernelName to dump for a specific kernel only.

  • Triton cache can interfere with the dump. In cases where MLIR_ENABLE_DUMP=1 does not work, try cleaning your triton cache: rm -r ~/.triton/cache/*.

• MLIR_DUMP_PATH specifies where MLIR_ENABLE_DUMP will dump to. If unset will dump to stderr.

• LLVM_IR_ENABLE_DUMP=1 dumps the IR before every pass run over the LLVM IR.

• TRITON_REPRODUCER_PATH=<reproducer_path> will generate an MLIR reproducer file
  at <reproducer_path> before each MLIR compiler stage. If any of the stages fail,
  <reproducer_path> will be a local MLIR reproducer captured right before the failing pass.

• TRITON_INTERPRET=1 uses the Triton interpreter instead of running on the
  GPU. You can insert Python breakpoints in your kernel code!

• TRITON_ENABLE_LLVM_DEBUG=1 passes -debug to LLVM, printing a lot of
  debugging information to stdout. If this is too noisy, run with just
  TRITON_LLVM_DEBUG_ONLY instead to limit the output.

  • An alternative way to reduce output noisiness is running with
    LLVM_IR_ENABLE_DUMP=1, extract the IR before the LLVM pass of interest, and
    then run LLVM's opt standalone, perhaps passing -debug-only=foo on the
    command line.

• TRITON_LLVM_DEBUG_ONLY=<comma-separated> is the equivalent of LLVM's
  -debug-only command-line option. This limits the LLVM debug output to
  specific pass or component names (which are specified using #define DEBUG_TYPE throughout LLVM and Triton) in order to allow the debug output to
  be less noisy. TRITON_LLVM_DEBUG_ONLY allows for one or more comma
  separated values to be specified (eg
  TRITON_LLVM_DEBUG_ONLY="tritongpu-remove-layout-conversions" or
  TRITON_LLVM_DEBUG_ONLY="tritongpu-remove-layout-conversions,regalloc").

• TRITON_ENABLE_ASAN=1 invokes the LLVM address sanitizer for
  memory leak and out of bounds access detection. Currently only supported on the AMD
  backend. This must be run using the ASAN libraries documented here.

  • When enabling the address sanitizer it is recommended to disable various memory caching strategies
    both within the ROCm stack and PyTorch. This will give the address sanitizer the best chance at finding the
    memory fault where it originates. See this test for more details.

• USE_IR_LOC={ttir,ttgir} reparses the IR such that the location information
  will be the line number of the IR file with that particular extension,
  instead of line number of the python file. This can provide a direct mapping
  from the IR to llir/ptx. When used with performance tools, it can provide a
  breakdown on IR instructions.

• TRITON_PRINT_AUTOTUNING=1 prints out the best autotuning config and total time
  spent for each kernel after autotuning is complete.

• DISABLE_LLVM_OPT will disable llvm optimizations for make_llir and make_ptx
  if its value is true when parsing as Bool. Otherwise, it will be parsed as a list
  of flags to disable llvm optimizations. One usage case is
  DISABLE_LLVM_OPT="disable-lsr"
  Loop strength reduction is known to cause up to 10% performance changes for
  certain kernels with register pressure.

• TRITON_ALWAYS_COMPILE=1 forces to compile kernels regardless of cache hit.

• MLIR_ENABLE_TIMING dumps the timing information for each MLIR pass.

• LLVM_ENABLE_TIMING dumps the timing information for each LLVM pass.

• TRITON_DEFAULT_FP_FUSION overrides the default behavior of allowing fp fusion (mul+add->fma).

• MLIR_ENABLE_DIAGNOSTICS=<comma-separated> controls diagnostic emission in MLIR.
  Options are: warnings, remarks, stacktraces, operations.
  Use comma-separated values to customize output. For example,
  MLIR_ENABLE_DIAGNOSTICS=remarks,operations enables remarks and IR operations,
  while MLIR_ENABLE_DIAGNOSTICS=warnings,stacktraces enables warnings with
  stacktraces. By default, only errors are shown. Setting warnings includes
  errors and warnings; remarks includes errors, warnings, and remarks.

• MLIR_ENABLE_REMARK is deprecated. Please use MLIR_ENABLE_DIAGNOSTICS=remarks.

• TRITON_KERNEL_DUMP enables the dumping of the IR from each compilation stage and the final ptx/amdgcn.

• TRITON_DUMP_DIR specifies the directory to save the dumped IR and ptx/amdgcn when TRITON_KERNEL_DUMP is set to 1.

• TRITON_KERNEL_OVERRIDE enables the override of the compiled kernel with a user-specified IR/ptx/amdgcn at the beginning of each compilation stage.

• TRITON_OVERRIDE_DIR specifies the directory from which to load the IR/ptx/amdgcn files when TRITON_KERNEL_OVERRIDE is set to 1.

• TRITON_F32_DEFAULT sets the default input precision of tl.dot when using 32-bit floats, which can be either ieee, tf32, or tf32x3.

• TRITON_FRONT_END_DEBUGGING=1 disables exception wrapping when an error occurs in the compiler frontend, allowing the full stack trace to be seen.

• TRITON_DISABLE_LINE_INFO=1 removes all line information from the module.

• PTXAS_OPTIONS passes additional command-line options to the PTX assembler ptxas (only on NVIDIA).

• LLVM_EXTRACT_DI_LOCAL_VARIABLES emit full debug info, allowing for eval of values in gpu debuggers (ie cuda-gdb, rocm-gdb etc)

• TRITON_DEFAULT_BACKEND= optionally sets the default backend used by Triton when
  constructing the active driver (i.e., triton.runtime.driver.active).

[!NOTE]
Some of these environment variables don't have a knob in knobs.py-- those are only relevant to the C++ layer(s), hence they don't exist in the python layer.

Kernel Override Steps

export TRITON_ALWAYS_COMPILE=1
export TRITON_KERNEL_DUMP=1
export TRITON_DUMP_DIR=<dump_dir>
export TRITON_KERNEL_OVERRIDE=1
export TRITON_OVERRIDE_DIR=<override_dir>
# Step 1: Run the kernel once to dump kernel's IRs and ptx/amdgcn in $TRITON_DUMP_DIR
# Step 2: Copy $TRITON_DUMP_DIR/<kernel_hash> to $TRITON_OVERRIDE_DIR
# Step 3: Delete the stages that you do not want to override and modify the stage you do want to override
# Step 4: Run the kernel again to see the overridden result

Compiler Pipeline Inspection Steps
To introspect the pipeline add_stages, before running your kernels, simply set
the add_stages_inspection_hook like so:

def inspect_stages(_self, stages, options, language, capability):
    # inspect or modify add_stages here
triton.knobs.runtime.add_stages_inspection_hook = inspect_stages

Examples of how to use this for out of tree plugin passes is here

Changelog

Version 2.0 is out! New features include:

• Many, many bug fixes
• Performance improvements
• Backend rewritten to use MLIR
• Support for kernels that contain back-to-back matmuls (e.g., flash attention)

Contributing

Community contributions are more than welcome, whether it be to fix bugs or to add new features at github. For more detailed instructions, please visit our contributor's guide.

Compatibility

Supported Platforms:

• Linux

Supported Hardware:

• NVIDIA GPUs (Compute Capability 8.0+)
• AMD GPUs (ROCm 6.2+)
• Under development: CPUs

Development Container (Dev Container)

Dev Containers for the Triton project are available from
the triton-dev-containers repository.

Key Benefits: Copy Section

• Consistency: All developers can work with the same development
  environment, ensuring uniform behavior across different systems.
• Isolation: The container prevents potential conflicts with software
  installed on your local machine.
• Portability: Easily share the development environment with team members,
  minimizing onboarding time and setup issues.

How to Use the Dev Container: Copy Section

For detailed instructions on how to use the dev containers, please see
the dev container user guide.

File: docs/getting-started/installation.rst Copy Chapter

============ Installation

For supported platform/OS and supported hardware, review the Compatibility <https://github.com/triton-lang/triton?tab=readme-ov-file#compatibility>_ section on Github.

Binary Distributions Copy Chapter

You can install the latest stable release of Triton from pip:

.. code-block:: bash

pip install triton

Binary wheels are available for CPython 3.10-3.14.

From Source Copy Chapter

++++++++++++++
Python Package
++++++++++++++

You can install the Python package from source by running the following commands:

.. code-block:: bash

git clone https://github.com/triton-lang/triton.git
  cd triton

  pip install -r python/requirements.txt # build-time dependencies
  pip install -e .

Note that, if llvm is not present on your system, the setup.py script will download the official LLVM static libraries and link against that.

For building with a custom LLVM, review the Building with a custom LLVM <https://github.com/triton-lang/triton?tab=readme-ov-file#building-with-a-custom-llvm>_ section on Github.

You can then test your installation by running the tests:

.. code-block:: bash

# One-time setup
  make dev-install

  # To run all tests (requires a GPU)
  make test

  # Or, to run tests without a GPU
  make test-nogpu

File: docs/meetups/12-13-2023/notes.md Copy Chapter

Agenda:

Items:

1. Refactoring plan for 3rd party backends
2. Front end refactoring (AMD)
3. Things like block pointers, ptr_analysis, mask_analysis can be used for GPUs, is there a plan to incrementally include components from Triton shared for GPU development.

Minutes:

Recording link here

1. Refactoring plan for 3rd party backends
   • Refactoring to be completed by end of the year so that all GPU backends can be individual passes on Triton GPU IR instead of being completely out of tree. The goal is for users to get other GPUs besides Cuda when they install Triton. Non-GPU Triton IR expected to stay as is.
2. Front end refactoring (AMD)
   • Will work with Phil for AMD related refactoring. Will share more details in next meetup about where AMD has diverged from Triton GPU IR and in the codeflow.
3. Things like block pointers, ptr_analysis, mask_analysis can be used for GPUs, is there a plan to incrementally include components from Triton shared for GPU development.
   • Can look at it on a case by case basis.

File: docs/meetups/10-25-2023/notes.md Copy Chapter

Agenda:

Items:

1. H100 updates
2. Triton-Shared layer updates
3. Intel update
4. Open discussion

Minutes:

Recording link here

1. H100 updates

   • Enabled WGMMA by default, now any matmul can reuse it.
   • fp8 formats enabled – 1.3 Petaflops on dense matmul on H100 (gemm performance)
   • Enabled Flash Attention using wgmma, resulting in 450 teraflop on fwd pass and 250 on backward pass – still working on perf for flash attention
   • fp8 numbers with flash attention running in fp8 with matmul is tricky, because the fp8 layout is significantly different than what is returned by wgmma, still wip

2. Triton-Shared layer

   • Please refer to slides for more details
   • Created a repo where you can find the middle layer
   • Available as a plugin into triton

3. Intel Update

   • Please refer to slides for more details

File: docs/meetups/08-22-2023/notes.md Copy Chapter

Agenda:

Announcements:

1. Triton conference registration opening soon. Conference on 20th September at the Microsoft Silicon Valley Campus.

Items:

1. H100 updates
2. Triton release plan update
3. Linalg updates
4. Intel GPU Backend status update.
5. Intel working on the CPU backend for Triton.
6. AMD updates
7. Open discussion

Minutes:

Recording link here

1. H100 updates:
   • Preliminary support is merged, disabled by default, can be enabled with env variables
   • Supports latest tensor cores, FP8s. Support for Flash Attention on the main branch coming soon.
   • Performance is very good on Matmuls, 80-90% of cublas on large Matmuls right now, will eventually reach parity with cublas. Above 600 teraflops on fp16 on xxm card, cublas is 670 on random input data. FP8 is twice that, around 1.2 petaflops.
   • Hopper support includes the full FP8 support for compute.
2. Triton release plan update
   • No specific dates for now, plan is to release before end of 2023.
   • Will move to 3.0 release due to minor backward compatibility breaking changes. For eg. Will move compiler options in the indexing operators as hardcoded operators in the kernel, will bump the major version.
   • Functionally the main goal will be to have 3rd party plugins for Intel and AMD gpus.
   • May synchronise with a PyTorch release so that PyTorch can benefit from the latest features, however continuous integration workflow is the default release cadence expected.
   • Will switch the default behavior to optimized mode for the release, needs more discussion with Nvidia.
   • Will expose flags for a user to enable kernel selection themselves.
   • Open question: Pytorch hasn’t rebased to latest triton, it is close to PyTorch code freeze – will PyTorch still sync with Triton 2.0? Will we have another release to support triton 2.0?
   • Community can start with the latest stable branch and rebase 3rd party plugin on top of that. OAI has no resources to commit to, but community can contribute.
3. Linalg updates
   • Discussion on Github for Linalg as a middle layer between the language and target hardware. Includes support for block pointers and modulo operators.
   • Please join the conversation here
   • Branch pushed is behind the tip, will work on getting it caught up on the tip.
4. Intel GPU Backend status update.
   • Please refer to slides here
5. Intel working on the CPU backend for Triton.
   • Please refer to slides here
6. AMD updates
   • Please refer to slides here.

File: docs/meetups/08-06-2024/notes.md Copy Chapter

Agenda:

1. Triton-CPU Update
2. Intel GPU backend update

Items:

Meeting notes:

1. Triton-CPU Update: Intel and Meta jointly presented the work on Triton-CPU, highlighting good progress on coverage and performance improvements. They also covered some of the optimizations they leveraged to get performance comparable to torch-native and torch-inductor. More details are in their slides.
2. Intel GPU Backend: Intel GPU backend shows good performance close to expert-tuned kernels and the use of block pointers for performance gains. There were questions around the future of block pointers and their importance for performance gains. With block-pointer deprecation there is a need for a more generic interface to support various backends including Intel GPU.
3. The 2024 Triton conference is on September 17th 2024 in Fremont California! Please register here.

Minutes:

Recording link here

Presentations repo here

File: docs/meetups/07-18-2023/notes.md Copy Chapter

Agenda:

Announcements:

1. Triton conference planned mid September in the Microsoft Silicon Valley Campus.

Items:

1. Alternative backend development approach (e.g. AMD, Intel)
2. State of the documentation, is there a planned effort? If yes, what do you think is the priority?
3. Mechanisms for smaller technical discussions: Slack channel per topic? Dedicated meetings for some topics?
4. Stability, testing, regressions: Improving CI and conformance/testing for validating new back-ends.
5. Language improvements/pain points
6. Windows Support
7. Discussion of known/anticipated design changes for H100
8. Some specific more tactical areas:
   • int8.
   • A low hanging fruit is to let tl.dot take int8 and leverage mma.
   • Sm75.
   • device functions. How hard is this to support while Triton frontend traverses AST?
   • remove torch dependencies from the frontend. (it sounds like there is already progress on this but could be worth discussing)

Minutes

Recording link here

1. Backend plans/broader roadmap:
   • Plan is for major updates to come in the Triton development meetup which will happen mid-September. For major design changes, currently the plan is to not upstream them directly but have a staging state and different backends can be integrated through a plugin mechanism where Triton provides a layer at the Triton IR layer that is generic and other backends can plug into that.
   • Short term roadmap plans are very focused on things like improving all FP8 things on Ampere and Hopper support (end of August). After Hopper support lands, priorities will include refactoring codebase to increase maintainability.
   • Linalg – upstreaming on hold due to limited dev bandwidth. Want to build an ecosystem where others can leverage Linalg like passes developed in their backend.
   • For now, peak performance on Nvidia GPUs needs Nvidia specific things, but the convergence of programming models for different backends will allow convergence of hardware backend support in Triton.
2. Documentation:
   • OpenAI has included comments in the backend code.
   • Seek community involvement to improve tutorials, based on new users knowing what is missing.
   • Seek community involvement for signature changes and doc updates.
   • Thread created in slack for suggestions on areas needing doc updates. Ian Bearman and his team may have bandwidth to update certain documentation.
3. Discussion channels:
   • Preferred #dev channel in slack for technical discussions.
   • Between GitHub and Slack it would be good to post links into places so folks know discussions are happening elsewhere
4. CI/testing:
   • Pretty liberal in terms of accepting regression tests and integration tests for Nvidia.
   • Plugin interface tested like everything else, and regressions there would block merges into main.
   • Correctness/Performance of external backends are tested nightly, but regressions do not prevent wheels from being built.
5. Language improvements:
   • Have added location information support into Triton codegen.
   • Feel free to bring up pain points in slack.
6. Windows Support: Technically not difficult to get a preliminary version. Most of the maintenance burden would come from having to support it when it breaks.

File: docs/meetups/05-07-2024/notes.md Copy Chapter

Agenda:

1. Triton CPU summary
2. Triton introduced a new Triton layout redesign (linear layout PR3794 ). Does this layout try to cover Triton CPU backend for SIMD instructions.
3. Triton Stream-k on AMD GPUs

Items:

Meeting notes:

1. Triton CPU backend: The Meta team presented their motivation, design, and progress on developing a CPU backend for Triton.
   There is a demand for heterogeneity and portability across different CPU architectures, especially for small batch sizes and inference workloads.
   They proposed to use MLIR and vector dialect to lower Triton IR to LLVM IR, and to leverage existing dialects and transformations for GPU backends.
   There maybe a possible refactoring of the CPU backend to make it more general and modular.
   Currently they have done initial work on plumbing the CPU backend and implementing a basic vector load operation using transfer read.
   Repo and other details are in the slides below.
   Open questions: How to handle different vector widths and operations, how to support ARM Neon, how to set performance goals and criteria, and how to coordinate with other Triton developers and contributors.
2. Stream-k for AMD: The AMD team presented their implementation and evaluation of Stream-k, a load-balanced scheme for matrix multiplication that can handle different tile sizes and split K dimensions.
   They compared it with PyTorch Matmul and Triton Matmul. Other details are in the slides below.

Minutes:

Recording link here

Presentations repo here

File: docs/meetups/04-02-2024/notes.md Copy Chapter

Agenda:

Items:

1. Interpreter update
2. Experience with TMA support and future plans for it
3. CGO trip report
4. Triton upstream CI and unit test status from AMD
5. Open discussion

Minutes:

Recording link here

Presentations repo here

1. Triton interpreter mode: The Open AI presented the interpreter mode for Triton code, which allows users to debug and inspect individual GPU programs using native Python print or PDB. It is currently being turned on using an environment variables, code decorators for individual functions being interpreted are still TBD. It can also run on CPU without GPU. For more details about the presentation please refer slides.
2. Tensor Memory Access (TMA) discussion: The current implementation of TMA in Triton has some limitations, so has been removed for now. The plan is to rethink how to do it better in the future. The goal is to support TMA implicitly, but the challenge is to handle the different memory layouts for different backends. There is a pull request to improve the launch overhead of kernels, which is related to TMA, but it would require extensive review and testing.
3. CGO trip report: Ian Bearman from Microsoft shared his experience of attending CGO and the Compilers for Machine Learning workshop. He and Javed Absar from Qualcomm gave talks about Triton shared and answered questions about Triton. There was a lot of interest in Triton as a cross-platform kernel language and questions were around the PyTorch integration, the performance portability, and the codegen bugs. It will be good to make the Triton-Pytorch connection more visible. There was also another project called Turbine that was similar to Triton. Please refer to the slides for more details.
4. AMD upstream CI and unit tests status: The AMD team discussed CI and enabling tests for MI 210 and MI 300. Work is in progress for performance gaps, compilation errors and fixes for FP8IN and flash attention kernels. The plan is to upstream these changes soon. Please refer to the slides for more details.
5. Third party CPU backend: The Intel team is driving discussions for community collaboration on a proof of concept for a CPU backend for Triton, using MLIR and OpenMP. There will be a follow-up meeting to discuss the logistics and design. Please refer to the third-party channel in slack for more details.

File: docs/meetups/02-20-2024/notes.md Copy Chapter

Agenda:

Items:

1. Intel update
2. AMD update
3. Profiler update
4. We are in the process of transitioning to a pro slack plan, so everybody will be able to see history. Expect this to take a few more weeks.
5. We are still working on finalizing a document about our technical governance structure. Expect this to take a few more weeks too.4. Open discussion.

Minutes:

Recording link here

1. Intel GPU integration with Triton and Pytorch:
   • No strong requirement from PyTorch for specific backends to be part of Triton official release.
   • Can use a separate branch/fork for CI/CD and testing.
   • Intel team will work with Pytorch offline to close.
2. AMD GPU backend update:
   • AMD team shared the refactored design for AMD backend.
   • The new design is modularized and reduces clutter and duplication in upstream Triton.
   • Further work needed for regression testing and secure runners.
3. Proton profiler update:
   • Keren from the OpenAI team presented a new profiler tool for Triton kernels, which supports multiple vendors, metrics, and formats.
   • Outlined the plan for open-sourcing, integrating, and extending the tool.