Exploiting integrated GPUs for network packet processing workloads

Janet Tseng, Ren Wang, James Tsai, Saikrishna Edupuganti, Alexander W. Min, Shinae Woo, Stephen Junkins, Tsung-Yuan Charlie Tai
2016 2016 IEEE NetSoft Conference and Workshops (NetSoft)  
Software-based network packet processing on standard high volume servers promises better flexibility, manageability and scalability, thus gaining tremendous momentum in recent years. Numerous research efforts have focused on boosting packet processing performance by offloading to discrete graphics processing units (GPUs). While integrated GPUs, residing on the same die with the CPU, offer many advanced features such as on-chip interconnect CPU-GPU communication, and shared physical/virtual
more » ... ysical/virtual memory, their applicability for packet processing workloads has not been fully understood and exploited. In this paper, we conduct in-depth profiling and analysis to understand the integrated GPU's capabilities, and performance potential for packet processing workloads. Based on that understanding, we introduce a GPU accelerated network packet processing framework that fully utilizes integrated GPU's massive parallel processing capability without the need for large numbers of packet batching, which might cause a significant processing delay. We implemented the proposed framework and evaluated the performance with several common, light-weight packet processing workloads on the Intel R Xeon R Processor E3-1200 v4 product family (codename Broadwell) with an integrated GT3e GPU. The results show that our GPU accelerated packet processing framework improved the throughput performance by 2-2.5x, compared to optimized CPU-only for packet processing.
doi:10.1109/netsoft.2016.7502464 dblp:conf/netsoft/TsengWTEMWJT16 fatcat:nucu2vmqnfcyngxf4om5h6yh2i