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Heterogeneous and asymmetric computing systems are composed by a set of different processing units, each with its own unique performance and energy characteristics. Still, the majority of current network packet processing frameworks targets only a single device (the CPU or some accelerator), leaving the rest processing resources unused and idle. In this paper, we propose an adaptive scheduling approach that supports the heterogeneous and asymmetric hardware, tailored for network packet processing applications. Our scheduler is able to respond quickly to dynamic performance fluctuations that occur at real time, such as traffic bursts, application overloads, and system changes. The experimental results show that our system is able to match the peak throughput of a diverse set of packet processing applications, while consuming up to $3.5\times$ less energy. [ABSTRACT FROM AUTHOR]

Graphics processing units (GPUs) are a powerful platform for building the high-speed network traffic processing applications using low-cost hardware. The existing systems tap the massively parallel architecture of GPUs to speed up certain computationally intensive tasks, such as cryptographic operations and pattern matching. However, they still suffer from significant overheads due to critical-path operations that are still being carried out on the CPU, and redundant inter-device data transfers. In this paper, we present GASPP, a programmable network traffic processing framework tailored to modern graphics processors. GASPP integrates optimized GPU-based implementations of a broad range of operations commonly used in the network traffic processing applications, including the first purely GPU-based implementation of network flow tracking and TCP stream reassembly. GASPP also employs novel mechanisms for tackling the control flow irregularities across SIMT threads, and for sharing the memory context between the network interfaces and the GPU. Our evaluation shows that GASPP can achieve multigigabit traffic forwarding rates even for complex and computationally intensive network operations, such as stateful traffic classification, intrusion detection, and packet encryption. Especially when consolidating multiple network applications on the same system, GASPP achieves up to 16.2× speedup compared with different monolithic GPU-based implementations of the same applications. [ABSTRACT FROM PUBLISHER]