This century has been a major avenue for revolutionary changes in technology and industry.
Industries have transitioned towards intelligent automation, relying less on human …

Graph is a well known data structure to represent the associated relationships in a variety of
applications, eg, data science and machine learning. Despite a wealth of existing efforts on …

With the emergence of data science, graph computing has become increasingly important
these days. Unfortunately, graph computing typically suffers from poor performance when …

Processing a one trillion-edge graph has recently been demonstrated by distributed graph
engines running on clusters of tens to hundreds of nodes. In this paper, we employ a single …

There has been significant recent interest in parallel graph processing due to the need to
quickly analyze the large graphs available today. Many graph codes have been designed …

Graph analytics is an emerging application which extracts insights by processing large
volumes of highly connected data, namely graphs. The parallel processing of graphs has …

Graph processing is an important analysis technique for a wide range of big data
applications. The ability to explicitly represent relationships between entities gives graph …

While unified virtual memory and demand paging in modern GPUs provide convenient
abstractions to programmers for working with large-scale applications, they come at a …

In this paper we introduce LDBC Graphalytics, a new industrial-grade benchmark for graph
analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic …

As memory capacity has outstripped TLB coverage, large data applications suffer from
frequent page table walks. We investigate two complementary techniques for addressing …