Seminar Details
2026-03-05 (13:00) : xPUBench: Scalable and Energy-Efficient GPU and DPU-Accelerated Network Functions
At Nyquist Maxwell a.164
Organized by Computer Science and Engineering
Speaker :
Maxime Vanliefde (ICTEAM)
Abstract :
The rapid increase in network speeds makes packet processing on general-purpose CPUs increasingly challenging. At 100 Gbps and beyond, CPUs struggle to sustain complex network functions without dedicated acceleration. This trend motivates the exploration and measurement of alternative compute platforms such as GPUs and embedded CPUs in Network Interface Cards (NICs). Modern NICs provide tighter integration with GPUs, with the ability to write received packets directly to GPU memory. SmartNICs, also known as DPUs, further feature embedded ARM or RISC cores capable of offloading NFV packet processing entirely. In this work, we introduce xPUBench, a benchmarking environment that systematically measures the performance and energy efficiency of packet processing across CPUs, GPUs, and DPUs. We evaluate several (co-)processing models relevant to Network Function Virtualization, including CPU+GPU hybrid, DPU-only, and GPU-only approaches. Our measurements show that, for a computation-heavy workload, current CPU-only implementations manage to handle up to 50% of the 100 Gbps NIC rate. In contrast, GPU implementations can saturate it. We also show that SmartNICs’ most powerful embedded cores can replace the main CPU for some traditional packet processing, alleviating the load on the host, which can now be entirely dedicated to running applications. We finally propose a novel energy-efficiency dimension, showing that DPUs outperform traditional CPUs for low-throughput processing, requiring only 24 W to sustain 10 Gbps, and that GPUs outperform CPUs for high-throughput processing. Our findings emphasize the need to assess both performance and energy in heterogeneous packet-processing pipelines, given the growing diversity of “xPUs” in networked systems.