The explosion of digital data and the ever-growing need for fast data analysis have made in-
memory big-data processing in computer systems increasingly important. In particular, large …

Processing-in-memory (PIM) is rapidly rising as a viable solution for the memory wall crisis,
rebounding from its unsuccessful attempts in 1990s due to practicality concerns, which are …

Many modern and emerging applications must process increasingly large volumes of data.
Unfortunately, prevalent computing paradigms are not designed to efficiently handle such …

Near-Data-Processing (NDP) architectures present a promising way to alleviate data
movement costs and can provide significant performance and energy benefits to parallel …

Writing high-performance GPU implementations of graph algorithms can be challenging. In
this paper, we argue that three optimizations called throughput optimizations are key to high …

Graph analysis is a powerful method in data analysis. Although several frameworks have
been proposed for processing large graph instances in distributed environments, their …

Due to the growing need to process large graph and network datasets created by modern
applications, recent years have witnessed a surging interest in developing big graph …

In this paper, we propose ExtraV, a framework for near-storage graph processing. It is based
on the novel concept of graph virtualization, which efficiently utilizes a cache-coherent …

Graph processing has become an important part of various areas, such as machine
learning, computational sciences, medical applications, social network analysis, and many …

Near-Memory Processing (NMP) systems that integrate accelerators within DIMM (Dual-
Inline Memory Module) buffer chips potentially provide high performance with relatively low …