Hidet, an open-source Python-based deep-learning compiler by CentML Inc., tackles the vital need for optimized inference workloads in deep learning. Its unique approach introduces task mappings, automates fusion optimization, and demonstrates significant performance improvement and reduced tuning times compared to existing frameworks. Hidet aims to set new efficiency and performance standards in deep learning compilation.
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The Value of Hidet: An Open-Source Python-based Deep Learning Compiler
Improving Deep Learning Model Efficiency and Performance
- Hidet streamlines the compilation process for DNN models from PyTorch and ONNX to efficient CUDA kernels, focusing on NVIDIA GPUs.
- It addresses the challenge of reducing deep learning model inference latency, ensuring efficient model serving across various platforms, from cloud services to edge devices.
- Offers a unique task-mapping programming paradigm, enabling fine-grained manipulations at a program-statement level for expressible optimizations.
- Automates the fusion process after scheduling, reducing engineering efforts and significantly cutting tuning times by 20x to 11x compared to existing solutions.
Superior Performance and Efficiency
- Experiments showcase a 1.22x improvement in performance, outperforming state-of-the-art DNN inference frameworks.
- Hidet slashes tuning times compared to existing solutions, setting new standards for efficiency and performance in deep learning compilation.
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