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Hardware architectures and machine learning (ML) libraries evolve rapidly.Traditional compilers often fail to generate high-performance code across thespectrum of new hardware offerings. To mitigate, engineers develop hand-tunedkernels for each ML library update and hardware upgrade. Unfortunately, thisapproach requires excessive engineering effort to scale or maintain with anydegree of state-of-the-art performance. Here we present a Nested PolyhedralModel for representing highly parallelizable computations with limiteddependencies between iterations. This model provides an underlying frameworkfor an intermediate representation (IR) called Stripe, amenable to standardcompiler techniques while naturally modeling key aspects of modern MLcomputing. Stripe represents parallelism, efficient memory layout, and multiplecompute units at a level of abstraction amenable to automatic optimization. Wedescribe how Stripe enables a compiler for ML in the style of LLVM that allowsindependent development of algorithms, optimizations, and hardwareaccelerators. We also discuss the design exploration advantages of Stripe overkernel libraries and schedule-based or schedule-space-based code generation.

Stripe: Tensor Compilation via the Nested Polyhedral Model