AUTHOR=Georganas Evangelos , Kalamkar Dhiraj , Avancha Sasikanth , Adelman Menachem , Aggarwal Deepti , Anderson Cristina , Breuer Alexander , Bruestle Jeremy , Chaudhary Narendra , Kundu Abhisek , Kutnick Denise , Laub Frank , Md Vasimuddin , Misra Sanchit , Mohanty Ramanarayan , Pabst Hans , Retford Brian , Ziv Barukh , Heinecke Alexander TITLE=Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning and HPC Workloads JOURNAL=Frontiers in Applied Mathematics and Statistics VOLUME=8 YEAR=2022 URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2022.826269 DOI=10.3389/fams.2022.826269 ISSN=2297-4687 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 and 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 Instruction Set Architecture (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 and viability of our approach using standalone kernels and end-to-end DL & High Performance Computing (HPC) workloads expressed entirely via TPPs that outperform state-of-the-art implementations on multiple platforms.