Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning Workloads

• URL: http://arxiv.org/abs/2104.05755v2
• Date: Wed, 14 Apr 2021 15:38:38 GMT
• Title: Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning Workloads
• Authors: Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha, Menachem Adelman, Cristina Anderson, Alexander Breuer, Narendra Chaudhary, Abhisek Kundu, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Barukh Ziv, Alexander Heinecke
• Abstract summary: This work introduces the Processing Primitives (TPP), a programming abstraction striving for efficient, portable implementation of Deep Learning-workloads with high-productivity. TPPs define a compact, yet versatile set of 2D-tensor operators (or a virtual ISA), which can be utilized as building-blocks to construct complex operators on high-dimensional tensors. We demonstrate the efficacy of our approach using standalone kernels and end-to-end DL-workloads expressed entirely via TPPs that outperform state-of-the-art implementations on multiple platforms.
• Score: 86.62083829086393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: During the past decade, novel Deep Learning (DL) algorithms/workloads and hardware have been developed to tackle a wide range of problems. Despite the advances in workload/hardware ecosystems, the programming methodology of DL-systems is stagnant. DL-workloads leverage either highly-optimized, yet platform-specific and inflexible kernels from DL-libraries, or in the case of novel operators, reference implementations are built via DL-framework primitives with underwhelming performance. This work introduces the Tensor Processing Primitives (TPP), a programming abstraction striving for efficient, portable implementation of DL-workloads with high-productivity. TPPs define a compact, yet versatile set of 2D-tensor operators (or a virtual Tensor ISA), which subsequently can be utilized as building-blocks to construct complex operators on high-dimensional tensors. The TPP specification is platform-agnostic, thus code expressed via TPPs is portable, whereas the TPP implementation is highly-optimized and platform-specific. We demonstrate the efficacy of our approach using standalone kernels and end-to-end DL-workloads expressed entirely via TPPs that outperform state-of-the-art implementations on multiple platforms.

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