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Programmable accelerators aim to provide the flexibility of traditional CPUs with significantly improved performance. A well-known impediment to the widespread adoption of programmable accelerators, like GPUs, is the software engineering overhead involved in porting the code. Existing support for C++ on GPUs allows programmers to port polymorphic code with little effort. However, the overhead from the virtual functions introduced by polymorphic code has not been well studied or mitigated on GPUs. To alleviate the performance cost of virtual functions, we propose two novel techniques that determine an object’s type based only on the object’s address, without accessing the object’s embedded virtual table pointer. The first technique, Coordinated Object Allocation and function Lookup (COAL), is a software-only solution that allocates objects by type and uses the compiler and runtime to find the object’s vTable without accessing an embedded pointer. COAL improves performance by 80%, 47%, and 6% over contemporary CUDA, prior research, and our newly-proposed type-based allocator, respectively. The second solution, TypePointer, introduces a hardware modification that allows unused bits in the object pointer to encode the object’s type, improving performance by 90%, 56%, and 12% over CUDA, prior work, and our new allocator. TypePointer can also be used with the default CUDA allocator to achieve an 18% performance improvement without modifying object allocation.

Judging a type by its pointer: optimizing GPU virtual functions