GPUs have demonstrated the capability of significantly improving the performance of network functions (NF). In an Network Function Virtualization (NFV) system, multiple NFs form a service chain to provide services. However, NFs in state-of-the-art GPU-accelerated NFV systems still utilize a GPU independently where each NF needs to transfer data to the GPU memory for acceleration. As a result, a packet might be transferred into the GPU memory by each NF when it passes through the service chain. We find these expensive and repetitive transfers are the main factor that limits the overall performance of an NFV system. We propose Gaviss, a GPU-accelerated NFV system with effective data sharing. By sharing packets in the GPU memory among network functions, a packet needs to be transferred to the GPU only once, eliminating the performance overhead caused by repetitive transfers. Extensive experimental results show that Gaviss can improve the overall throughput by 2.6-13.2× and reduce the latency by up to 37.9%, when compared with state-of-the-art approaches. Moreover, Gaviss also demonstrates up to 2.5× higher price-performance ratio than CPU-based implementations, making GPUs competitive for building NFV systems.

Text clustering is a widely studied problem in the text mining domain. The Dirichlet Multinomial Mixture (DMM) model based clustering algorithms have shown good performance to cope with high dimensional sparse text data, obtaining reasonable results in both clustering accuracy and computational efficiency. However, the time complexity of DMM model training is proportional to the average document length and the number of clusters, making it inefficient for scaling up to long text and large corpora, which is common in realworld applications such as documents organization, retrieval and recommendation. In this paper, we leverage a symmetric prior setting for Dirichlet distribution, and build indices to decrease the time complexity of the sampling-based training for DMM from O(K∗L) to O(K∗U), where K is the number of clusters, L the average length of document, and U the average number of unique words in each document. We introduce a Metropolis-Hastings sampling algorithm, which further reduces the sampling time complexity from O(K∗U) to O(U) in the nearly-to-convergence training stages. Moreover, we also parallelize the DMM model training to obtain a further acceleration by using an uncollapsed Gibbs sampler. We combine all these optimizations into a highly efficient implementation, called X-DMM, which enables the DMM model to scale up for long and large-scale text clustering. We evaluate the performance of X-DMM on several real world datasets, and the experimental results show that XDMM achieves substantial speed up compared with existing state-of-the-art algorithms without clustering accuracy degradation.