Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries
Numerous irregular graph datasets, for example social networks or web graphs, may contain
even trillions of edges. Often, their structure changes over time and they have domain …
even trillions of edges. Often, their structure changes over time and they have domain …
Sisa: Set-centric instruction set architecture for graph mining on processing-in-memory systems
Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
DAMOV: A new methodology and benchmark suite for evaluating data movement bottlenecks
Data movement between the CPU and main memory is a first-order obstacle against improv
ing performance, scalability, and energy efficiency in modern systems. Computer systems …
ing performance, scalability, and energy efficiency in modern systems. Computer systems …
Parallel and distributed graph neural networks: An in-depth concurrency analysis
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …
routinely solve complex problems on unstructured networks, such as node classification …
Adapt-noc: A flexible network-on-chip design for heterogeneous manycore architectures
The increased computational capability in heterogeneous manycore architectures facilitates
the concurrent execution of many applications. This requires, among other things, a flexible …
the concurrent execution of many applications. This requires, among other things, a flexible …
Hermes: Accelerating long-latency load requests via perceptron-based off-chip load prediction
Long-latency load requests continue to limit the performance of modern high-performance
processors. To increase the latency tolerance of a processor, architects have primarily relied …
processors. To increase the latency tolerance of a processor, architects have primarily relied …
Graph processing on fpgas: Taxonomy, survey, challenges
Graph processing has become an important part of various areas, such as machine
learning, computational sciences, medical applications, social network analysis, and many …
learning, computational sciences, medical applications, social network analysis, and many …
Graphminesuite: Enabling high-performance and programmable graph mining algorithms with set algebra
We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that
facilitates evaluating and constructing high-performance graph mining algorithms. First …
facilitates evaluating and constructing high-performance graph mining algorithms. First …
Practice of streaming processing of dynamic graphs: Concepts, models, and systems
Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …
machine learning, medical applications, social network analysis, computational sciences …
High-performance parallel graph coloring with strong guarantees on work, depth, and quality
M Besta, A Carigiet, K Janda… - … Conference for High …, 2020 - ieeexplore.ieee.org
We develop the first parallel graph coloring heuristics with strong theoretical guarantees on
work and depth and coloring quality. The key idea is to design a relaxation of the vertex …
work and depth and coloring quality. The key idea is to design a relaxation of the vertex …