Recent GPU architectures support unified virtual memory (UVM), which offers great
opportunities to solve larger problems by memory oversubscription. Although some studies …

Graph sampling and random walk operations, capturing the structural properties of graphs,
are playing an important role today as we cannot directly adopt computing-intensive …

In recent years, many CPU-GPU heterogeneous graph processing systems have been
developed in both academic and industrial to facilitate large-scale graph processing in …

Existing disaggregated memory (DM) systems face a problem of underutilized far memory
bandwidth, which greatly limits the data throughput when processing data-intensive …

Processing large graphs with memory-limited GPU needs to resolve issues of host-GPU
data transfer, which is a key performance bottleneck. Existing GPU-accelerated graph …

Multi-GPU systems have become popular to cater to the growing demands for high
parallelism and large memory capacity. However, the delivered performance is constrained …

Disaggregated architecture brings new opportunities to memory-consuming applications like
graph processing. It allows one to outspread memory access pressure from local to far …

Hybrid transactional/analytical processing (HTAP) workloads on graph data can significantly
benefit from GPU accelerators. However, to exploit the full potential of GPU processing …

The challenge to fully exploit the potential of existing and upcoming scientific instruments
like large single-dish radio telescopes is to process the collected massive data effectively …

Graph sampling and random walk algorithms are playing increasingly important roles today
because they can significantly reduce graph size while preserving structural information …