Systematically extending a high-level code generator with support for tensor cores

High-level code generators like Halide, Lift, and RISE make a compelling proposition: write programs in a simple high-level language and get high-performing GPU code "for free". They achieve this feat by restricting the input language to a specific domain (such as image and array processing in Halide) or to a fixed set of flexible parallel patterns (as Lift and RISE do). Implementing high-level code generators that produce high-performance code is challenging, specifically as the target hardware constantly evolves. In this paper, we discuss how we systematically extend the RISE high-level code generator with support for tensor cores, a specialized hardware feature of recent Nvidia GPUs. We highlight the design of RISE that makes it easily extensible by following a systematic bottom-up approach, that first, exposes the imperative tensor core API to the code generator, then, raises the abstractions to an internal low-level functional representation, that, finally, is targeted by a rewrite process that starts from a high-level functional program. Our experimental evaluation shows that RISE with support for tensor cores generates code of competitive performance to manually optimized CUDA code, which is only up to 36%, but on average only 10%, slower than Nvidia's highly optimized cuBLAS library, and clearly outperforms any code that does not exploit tensor cores.