There is growing interest in graph pattern mining (GPM) problems such as motif counting.
GPM systems have been developed to provide unified interfaces for programming …

Intel Optane DC Persistent Memory (Optane PMM) is a new kind of byte-addressable
memory with higher density and lower cost than DRAM. This enables the design of …

In many graph-based applications, the graphs tend to grow, imposing a great challenge for
GPU-based graph processing. When the graph size exceeds the device memory capacity …

Graphs play a key role in data analytics. Graphs and the software systems used to work with
them are highly diverse. Algorithms interact with hardware in different ways and which graph …

The performance of graph programs depends highly on the algorithm, the size and structure
of the input graphs, as well as the features of the underlying hardware. No single set of …

Vector search has drawn a rapid increase of interest in the research community due to its
application in novel AI applications. Maximizing its performance is essential for many tasks …

In many applications of the analysis of dynamic graph, many Timing iterative Graph
Processing (TGP) jobs usually need to be generated for the processing of the corresponding …

There are relatively few studies of distributed GPU graph analytics systems in the literature
and they are limited in scope since they deal with small data-sets, consider only a few …

Influence maximization (IM) is the problem of finding the k most influential nodes in a graph.
We propose distributed-memory parallel algorithms for the two main kernels of a state-of-the …

Many out-of-GPU-memory systems are recently designed to support iterative processing of
large-scale graphs. However, these systems still suffer from long time to converge because …