GPU Based Acceleration of SystemC and Transaction Level Models for MPSOC Simulation

Abstract

With increasing number of cores on a chip, the complexity of modeling hardware using virtual prototype is increasing rapidly. Typical SOCs today have multipro-cessors connected through a bus or NOC architecture which can be modeled using SystemC framework. SystemC is a popular language used for early design exploration and performance analysis of complex embedded systems. TLM2.0, an extension of SystemC, is increasingly used in MPSOC designs for simulating loosely and approxi-mately timed transaction level models. The OSCI reference kernel which implements SystemC library runs on a single thread, slowing up the simulation speed to a large extent. Previous works have used the computational power of multi-core systems and GPUs which can run multiple threads simultaneously, speeding up the simu-lation. Multi-core simulations are not as effective in cases where thread runtime is low, because synchronization overhead becomes comparable to thread runtime. Modern GPUs can run thousands of threads at a time and have shown good results for synthesizable designs in recent efforts. However, development in these works are limited to synthesizable subsets of SystemC models, not supporting timed events for process communication. In this research work, a methodology is proposed for accelerating timed event based SystemC TLM2.0 model to GPU based kernel, which maps SystemC processes to CUDA threads in GPU, providing high data level par-allelism. This work aims to provide a scalable solution for simulating large MPSOC designs, facilitating early design exploration and performance analysis. Experiments have shown that the proposed technique provides a speed-up of the order of 100x for typical MPSOC designs.