Design space exploration of on-chip ring interconnection for a CPU–GPU heterogeneous architecture

Abstract

Incorporating a GPU architecture into CMP, which is more efficient with certain types of applications, is a popular architecture trend in recent processors. This heterogeneous mix of architectures will use an on-chip interconnection to access shared resources such as last-level cache tiles and memory controllers. The configuration of this on-chip network will likely have a significant impact on resource distribution, fairness, and overall performance.The heterogeneity of this architecture inevitably exerts different pressures on the interconnection due to the differing characteristics and requirements of applications running on CPU and GPU cores. CPU applications are sensitive to latency, while GPGPU applications require massive bandwidth. This is due to the difference in the thread-level parallelism of the two architectures. GPUs use more threads to hide the effect of memory latency but require massive bandwidth to supply those threads. On the other hand, CPU cores typically running only one or two threads concurrently are very sensitive to latency.This study surveys the impact and behavior of the interconnection network when CPU and GPGPU applications run simultaneously. Among our findings, we observed that significant interference exists between CPU and GPU applications and resource partitioning, in particular virtual and physical channel partitioning, shows effectiveness to solve the interference problem. Also, heterogeneous link configurations show promising results by optimizing traffic hotspots in the network. Finally, we evaluated different placement policies and found that how to place different components in the network significantly affects the performance. Based on these findings, we suggest an optimal ring interconnect network. Our study will shed light on other architectural interconnection studies on CPU–GPU heterogeneous architectures.