Supporting Dynamic Data Structures in a Shared-Memory based GPGPU Programming Framework

Abstract

Although Graphics Processing Unit (GPU) is expected to be a practical high performance computing platform, current programming frameworks such as CUDA and OpenCL require large programming cost. Therefore, we are developing a new framework MESI-CUDAproviding shared variables to hide low-level data management in CUDA. However, handling dynamic data structures is difficult in current MESI-CUDA because shared variables cannot be dynamically created and pointer fields are not allowed in them. Thus, we extended MESI-CUDA to remove such restrictions. Introducing dynamic management of shared variables and automatic pointer conversion on data transfer, any pointer-based dynamic data structure can be shared between the CPU and GPU with only small changes from the C code. As the results of the evaluation, pointer conversion increased the transfer time of data structures approximately 3.3 times larger in the worst case, and 1.3–2 times larger in the practical cases. Considering that non-conversion alternatives cause overhead in pointer dereferences, we regard this overhead is practical in most cases.