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Gluon: A communication-optimizing substrate for distributed heterogeneous graph analytics
This paper introduces a new approach to building distributed-memory graph analytics
systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies …
systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies …
Automine: harmonizing high-level abstraction and high performance for graph mining
Graph mining algorithms that aim at identifying structural patterns of graphs are typically
more complex than graph computation algorithms such as breadth first search. Researchers …
more complex than graph computation algorithms such as breadth first search. Researchers …
Hitgraph: High-throughput graph processing framework on fpga
This paper presents, HitGraph, an FPGA framework to accelerate graph processing based
on the edge-centric paradigm. HitGraph takes in an edge-centric graph algorithm and …
on the edge-centric paradigm. HitGraph takes in an edge-centric graph algorithm and …
Subway: Minimizing data transfer during out-of-GPU-memory graph processing
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 …
GPU-based graph processing. When the graph size exceeds the device memory capacity …
Tigr: Transforming irregular graphs for gpu-friendly graph processing
Graph analytics delivers deep knowledge by processing large volumes of highly connected
data. In real-world graphs, the degree distribution tends to follow the power law--a small …
data. In real-world graphs, the degree distribution tends to follow the power law--a small …
C-SAW: A framework for graph sampling and random walk on GPUs
Many applications require to learn, mine, analyze and visualize large-scale graphs. These
graphs are often too large to be addressed efficiently using conventional graph processing …
graphs are often too large to be addressed efficiently using conventional graph processing …
Compressgraph: Efficient parallel graph analytics with rule-based compression
Modern graphs exert colossal time and space pressure on graph analytics applications. In
2022, Facebook social graph reaches 2.91 billion users with trillions of edges. Many …
2022, Facebook social graph reaches 2.91 billion users with trillions of edges. Many …
Emogi: Efficient memory-access for out-of-memory graph-traversal in gpus
Modern analytics and recommendation systems are increasingly based on graph data that
capture the relations between entities being analyzed. Practical graphs come in huge sizes …
capture the relations between entities being analyzed. Practical graphs come in huge sizes …
Grus: Toward unified-memory-efficient high-performance graph processing on gpu
Today's GPU graph processing frameworks face scalability and efficiency issues as the
graph size exceeds GPU-dedicated memory limit. Although recent GPUs can over-subscribe …
graph size exceeds GPU-dedicated memory limit. Although recent GPUs can over-subscribe …
Cggraph: An ultra-fast graph processing system on modern commodity cpu-gpu co-processor
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 …
developed in both academic and industrial to facilitate large-scale graph processing in …