  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 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 & HPC workloads expressed entirely via TPPs that outperform state-of-the-art
implementations on multiple platforms.
