Characterizing the Runtime Effects of Object-Oriented Workloads on GPUs

Abstract

Modern GPGPU programming extensions like OpenCL and CUDA have supported object-oriented workloads on GPUs for several generations. However, no analysis of object-oriented workloads running on massively parallel accelerators has been investigated. This extended abstract presents a performance analysis of object-oriented workloads on a PASCAL Titan X GPU. Our characterization demonstrates that GPUs have different performance trade-offs when running object-oriented code than traditional CPUs. Where CPUs are sensitive to the misprediction of indirect branches that result from virtual function calls, GPUs are more sensitive to the additional memory system pressure that comes from loading pointers and virtual function table entries.