Characterizing the Performance and Usability of GPU JIT Compilation Interfaces Using Proteus

Just-In-Time (JIT) compilation is an important technique to specialize and optimize code, by using runtime context to overcome the limitations of statically optimizing compilers. Different JIT compilers offer different code input methods that range from inputting source code as a string, building code using an embedded domain-specific language, or annotating the source code for JIT compilation and optimization.

We extend Proteus, a state-of-the-art JIT compiler for C/C++ based on code annotations, with a source code string interface and an embedded domain-specific language for JIT compilation and optimization. Using the compilation infrastructure of Proteus as common ground, we evaluate those interfaces on a set of benchmark programs, focusing on the more challenging use-case of JIT compilation for GPUs, to assess their performance, overhead, and usability. Results on two different GPUs, an AMD MI300A and an NVIDIA H100, reveal that despite their differences, all those interfaces are performance portable although they present different trade-offs in terms of JIT compilation overhead and usability.

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Contributors:

• Konstantinos Parasyris
• David Beckingsale
• Giorgis Georgakoudis