Presentation
Efficient Large Dynamic Graph Analysis on Emerging Storage Technology
DescriptionGraph-structured data analysis is extensively used in various real-world applications, including biology, social media, and recommendation systems. With the increasing prevalence of real-time data, many graphs become dynamic and evolve over time. Thus, dynamic graph processing systems become a necessary tool to store these real-time updates and continuously run analytic algorithms to provide insights into the data. However, these systems require special design to efficiently support both tasks. As a result, we have seen a growing demand in this research direction in recent years, as numerous low-level data structures and high-level systems have addressed different aspects of dynamic graph processing.
With the high demand for data-intensive systems and the growing volume of data, many emerging storage hardware technologies have been added to the storage hierarchy, including persistent memory. Due to its promising features such as low latency, high density, and byte-addressable accessibility, persistent memory has gained the attention of researchers and developers of high-performance data-intensive applications. As such, it is not surprising that we expect to see persistent memory usage in dynamic graph processing systems due to the need for high performance and capacity. Therefore, our research aims to explore efficient ways of designing and implementing dynamic graph processing systems on persistent memory.
With the high demand for data-intensive systems and the growing volume of data, many emerging storage hardware technologies have been added to the storage hierarchy, including persistent memory. Due to its promising features such as low latency, high density, and byte-addressable accessibility, persistent memory has gained the attention of researchers and developers of high-performance data-intensive applications. As such, it is not surprising that we expect to see persistent memory usage in dynamic graph processing systems due to the need for high performance and capacity. Therefore, our research aims to explore efficient ways of designing and implementing dynamic graph processing systems on persistent memory.