Exploring the Design Space of SPMD Divergence Management on Data-Parallel Architectures

Data-parallel architectures must provide efficient support for complex control-flow constructs to support sophisticated applications coded in modern single-program multiple-data languages. As these architectures have wide data paths that process a single instruction across parallel threads, a mechanism is needed to track and sequence threads as they traverse potentially divergent control paths through the program. The design space for divergence management ranges from software-only approaches where divergence is explicitly managed by the compiler, to hardware solutions where divergence is managed implicitly by the micro architecture. In this paper, we explore this space and propose a new predication-based approach for handling control-flow structures in data-parallel architectures. Unlike prior predication algorithms, our new compiler analyses and hardware instructions consider the commonality of predication conditions across threads to improve efficiency. We prototype our algorithms in a production compiler and evaluate the tradeoffs between software and hardware divergence management on current GPU silicon. We show that our compiler algorithms make a predication-only architecture competitive in performance to one with hardware support for tracking divergence.


Yunsup Lee (University of California Berkeley)
Vinod Grover (NVIDIA)
Ronny Krashinsky (NVIDIA)
Krste Asanovic (University of California Berkeley)

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