d936751aad
* Reserve the first core for the main thread Currently in "auto affinity" mode the worker threads are affinized to cores 0..(N-1), leaving the very last core for the main thread. This patch preserves core #0 for the main thread, and affinizes the worker threads to cores 1..N. * Avoid unneeded spin_pause in thread pool's worker threads Remove unneeded PAUSE instruction (0.1-0.2 usec latency) after a worker thread finds a task to execute. * MLAS/x86: optimize QLinearConv on hybrid CPUs Existing 4x task granularity for task partitioning on hybrid CPUs is not sufficient to compensate the difference of VNNI instructions throughput between performance and efficient cores. This patch... * Increases granularity for QLinearConv by 2x, to have 2x more tasks with 2x smaller output count * Limits QLinearConv task count from above, to avoid output count per task getting smaller than kernel's capability * Remove hardcoded task count for QLineConv as it limited scaling on 16+ cores CPUs * MLAS/x86: optimize QLinearConv on hybrid CPUs Existing 4x task granularity for task partitioning on hybrid CPUs is not sufficient to compensate the difference of VNNI instructions throughput between performance and efficient cores. This patch... * Increases granularity for QLinearConv by 2x, to have 2x more tasks with 2x smaller output count * Limits QLinearConv task count from above, to avoid output count per task getting smaller than kernel's capability * Remove hardcoded task count for QLineConv as it limited scaling on 16+ cores CP * Addressing comments * combining x86 ARM branches in qlinearconv threaded job partition * revert first core assignment Co-authored-by: Saurabh <saurabh.tangri@intel.com> Co-authored-by: Chen Fu <fuchen@microsoft.com> |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
Data/Telemetry
Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.