Bo Liu

In the big data era, much real-world data can be naturally represented as graphs. Consequently, many application domains can be modeled as graph processing. Graph processing, especially the processing of the large-scale graphs with the number of vertices and edges in the order of billions or even hundreds of billions, has attracted much attention in both industry and academia. It still remains a great challenge to process such large-scale graphs. Researchers have been seeking for new possible solutions. Because of the massive degree of parallelism and the high memory access bandwidth in GPU, utilizing GPU to accelerate graph processing proves to be a promising solution. This article surveys the key issues of graph processing on GPUs, including data layout, memory access pattern, workload mapping, and specific GPU programming. In this article, we summarize the state-of-the-art research on GPU-based graph processing, analyze the existing challenges in detail, and explore the research opportunities for the future.

S QDs were used, for the first time, as a sonosensitizer in the SDT field. In this study, we integrated the advantages of the natural enzyme system and SDT to develop a novel approach for effective non-invasive treatment of cancer.

We functionalized double-walled carbon nanotubes (DWCNTs) as artificial water channel proteins. For the first time, molecular dynamics simulations show that the bilayer structure of DWCNTs is advantageous for carbon nanotube based transmembrane channels. The shielding of the amphiphilic outer layer could guarantee biocompatibility of the synthetic channel and protect the inner tube (functional part) from disturbance of the membrane environment. This novel design could promote more sophisticated nanobiodevices which could function in a bioenvironment with high biocompatibility.