The Click modular router has been one of the most popular software router platforms for rapid prototyping and new protocol development. Unfortunately, its internal architecture has not caught up with recent hardware advancements, and the performance remains sub-optimal in high-speed networks despite its benefit of flexible module composition.In this work, we identify the performance bottlenecks of the existing Click router and extend it to scale with modern computer systems. Our improvements focus on both I/O and computation batching, and include various optimizations for multi-core systems and multi-queue network cards. We find that these techniques improve the performance by almost a factor of 10, and the maximum throughput reaches 28 Gbps of minimum-sized IPv4 packet forwarding speed on a single machine.

Despite the popularity of GPUs in high-performance and scientific computing, and despite increasingly general-purpose hardware capabilities, the use of GPUs in network servers or distributed systems poses significant challenges. GPUnet is a native GPU networking layer that provides a socket abstraction and high-level networking APIs for GPU programs. We use GPUnet to streamline the development of high-performance, distributed applications like in-GPU-memory MapReduce and a new class of low-latency, high-throughput GPU-native network services such as a face verification server.