Hashing has been widely used for nearest neighbors search over big data. Hashing encodes high dimensional data points into binary codes. Most hashing methods use the single-bit quantization (SBQ) strategy for coding the data. However, this strategy oft…
Solving least squares (LS) problems is a major topic in many applications. With recent data explosion, traditional approach is no longer suitable while working with large datasets, instead, randomized algorithms become popular in addressing this issue….
It is important to scale out deep neural network (DNN) training for reducing model training time. The high communication overhead is one of the major performance bottlenecks for distributed DNN training across multiple GPUs. Our investigations have sho…
Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. However, predicting the urban water quality is a challenging task since the water quality varies in …
Big Data processing systems (e.g., Spark) have a number of resource configuration parameters, such as memory size, CPU allocation, and the number of running nodes. Regular users and even expert administrators struggle to understand the mutual relation …
Checkpoint/recovery has been widely used in many high available and reliable systems. This paper proposes Shadow, an application-transparent and in-memory checkpointing mechanism based on hybrid memory system composed of DRAM and emerging Non-volatile …
The number of triangles (hereafter denoted by $Delta$Δ) is an important metric to analyze massive graphs. It is also used to compute clustering coefficient in networks. This paper proposes a new algorithm called PES (Priority Edge Sampling) to e…
Cross-modal hashing can efficiently retrieve data across different modalities and has been successfully applied in various domains. Although many supervised cross-modal hashing methods have been proposed, they generally focus on two modals only and ass…
This paper presents a network-based template for analyzing large-scale dynamic data. Specifically, we propose a novel shared-memory parallel algorithm for updating tree-based structures or properties, such as connected components (CC) and minimum spann…
Recent decades have seen a significant increase in the frequency, intensity, and impact of natural disasters and other emergencies, forcing the governments around the world to make emergency response and disaster management national priorities. The gro…