This paper introduces a new approach to building distributed-memory graph analytics
systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies …

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 …

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 …

High-level GPU graph processing frameworks are an attractive alternative for achieving both
high productivity and high performance. Hence, several high-level frameworks for graph …

Existing state-of-the-art CPU graph frameworks take advantage of multiple cores, but not the
SIMD capability within each core. In this work, we retarget an existing GPU graph algorithm …

With the rise of streaming data for dynamic graphs, large-scale graph analytics meets a new
requirement of Incremental Computation because the larger the graph, the higher the cost …

Efficient processing of graph applications on heterogeneous CPU–GPU systems require
effectively harnessing the combined power of both the CPU and GPU devices. This paper …

Graph models of social information systems typically contain trillions of edges. Such big
graphs cannot beprocessed on a single machine. The graph object must bepartitioned and …

Domain-specific language compilers need to close the gap between the domain
abstractions of the language and the low-level concepts of the target platform. This can be …

Large graphs are widely used in real world graph analytics. Memory available in a single
machine is usually inadequate to process these graphs. A good solution is to use a …