LPAQ is a powerful context-based lossless compression algorithm ranking top on compression ratio on many benchmarks. However, its application is limited due to its high computational complexity, extremely slow compression speed, and large memory usage. In this paper, we introduce LpaqHP, an FPGA-based design, to accelerate LPAQ. We speed up LPAQ by eliminating the bit-level dependency within a byte in the three main components of LPAQ. In other words, LpaqHP can compress all eight bits within a byte in parallel. In the meanwhile, we managed to maintain the compression ratio in LpaqHP by implementing two dedicated schemes to compensate for the compression ratio. Experiments show that LpaqHP achieves a throughput of 67.96MB/s on Xilinx Virtex UltraScale plus VCU118 card, 234 × faster than executing on Intel Xeon E5-2650 at 2.2GHz and 5.97 × faster than the state-of-the-art work pLPAQ.

It is efficient to accelerate SQL queries by utilizing Computational Storage Device (CSD). Since current CSDs have limited processing capacity and cannot accelerate all SQL queries, existing schemes typically offload filter operators to significantly reduce data movement to the computational storage device. However, leaving all filter operators in SQL queries to the computational storage device directly leads to poor performance. To address these problems, we propose the Self-Adaptive Query Offloading (SAQO) strategy for offloading filtering operators. The SAQO strategy dynamically chooses the best execution location for filtering operators by considering the selectivity of the operator, the computational storage device's load, and the task characteristics, which ultimately accelerates the speed of executing data query tasks. The real-world applications Spark SQL and TPC-H are used to evaluate the SAQO strategy. The SAQO strategy reduces the execution time of data query tasks by 58.7% compared to the host execution strategy and 9.4% compared to the state-of-the-art offloading strategy.

Emotion cause identification aims at identifying the potential causes that lead to a certain emotion expression in text. Several techniques including rule based methods and traditional machine learning methods have been proposed to address this problem based on manually designed rules and features. More recently, some deep learning methods have also been applied to this task, with the attempt to automatically capture the causal relationship of emotion and its causes embodied in the text. In this work, we find that in addition to the content of the text, there are another two kinds of information, namely relative position and global labels, that are also very important for emotion cause identification. To integrate such information, we propose a model based on the neural network architecture to encode the three elements (i.e., text content, relative position and global label), in an unified and end-to-end fashion. We introduce a relative position augmented embedding learning algorithm, and transform the task from an independent prediction problem to a reordered prediction problem, where the dynamic global label information is incorporated. Experimental results on a benchmark emotion cause dataset show that our model achieves new state-ofthe-art performance and performs significantly better than a number of competitive baselines. Further analysis shows the effectiveness of the relative position augmented embedding learning algorithm and the reordered prediction mechanism with dynamic global labels.

The emotion cause extraction (ECE) task aims at discovering the potential causes behind a certain emotion expression in a document. Techniques including rule-based methods, traditional machine learning methods and deep neural networks have been proposed to solve this task. However, most of the previous work considered ECE as a set of independent clause classification problems and ignored the relations between multiple clauses in a document. In this work, we propose a joint emotion cause extraction framework, named RNN-Transformer Hierarchical Network (RTHN), to encode and classify multiple clauses synchronously. RTHN is composed of a lower word-level encoder based on RNNs to encode multiple words in each clause, and an upper clause-level encoder based on Transformer to learn the correlation between multiple clauses in a document. We furthermore propose ways to encode the relative position and global predication information into Transformer that can capture the causality between clauses and make RTHN more efficient. We finally achieve the best performance among 12 compared systems and improve the F1 score of the state-of-the-art from 72.69% to 76.77%.