Lift: a Functional Data-Parallel IR for High-Performance GPU Code Generation

Parallel patterns (e.g., map, reduce) have gained traction as an abstraction for targeting parallel accelerators and are a promising answer to the performance portability problem. However, compiling high-level programs into efficient low-level parallel code is challenging. Current approaches start from a high-level parallel IR and proceed to emit GPU code directly in one big step. Fixed strategies are used to optimize and map parallelism exploiting properties of a particular GPU generation leading to performance portability issues. We introduce the Lift IR, a new data-parallel IR which encodes OpenCL-specific constructs as functional patterns. Our prior work has shown that this functional nature simplifies the exploration of optimizations and mapping of parallelism from portable high-level programs using rewrite-rules. This paper describes how Lift IR programs are compiled into efficient OpenCL code. This is non-trivial as many performance sensitive details such as memory allocation, array accesses or synchronization are not explicitly represented in the Lift IR. We present techniques which overcome this challenge by exploiting the pattern’s high-level semantics. Our evaluation shows that the Lift IR is flexible enough to express GPU programs with complex optimizations achieving performance on par with manually optimized code