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 …
Scalable graph processing frameworks: A taxonomy and open challenges
The world is becoming a more conjunct place and the number of data sources such as
social networks, online transactions, web search engines, and mobile devices is increasing …
social networks, online transactions, web search engines, and mobile devices is increasing …
Sebs: A serverless benchmark suite for function-as-a-service computing
Function-as-a-Service (FaaS) is one of the most promising directions for the future of cloud
services, and serverless functions have immediately become a new middleware for building …
services, and serverless functions have immediately become a new middleware for building …
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 …
Graphit: A high-performance graph dsl
The performance bottlenecks of graph applications depend not only on the algorithm and
the underlying hardware, but also on the size and structure of the input graph. As a result …
the underlying hardware, but also on the size and structure of the input graph. As a result …
Slim fly: A cost effective low-diameter network topology
We introduce a high-performance cost-effective network topology called Slim Fly that
approaches the theoretically optimal network diameter. Slim Fly is based on graphs that …
approaches the theoretically optimal network diameter. Slim Fly is based on graphs that …
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 …
Stateful dataflow multigraphs: A data-centric model for performance portability on heterogeneous architectures
The ubiquity of accelerators in high-performance computing has driven programming
complexity beyond the skill-set of the average domain scientist. To maintain performance …
complexity beyond the skill-set of the average domain scientist. To maintain performance …
GraphBLAST: A high-performance linear algebra-based graph framework on the GPU
High-performance implementations of graph algorithms are challenging to implement on
new parallel hardware such as GPUs because of three challenges:(1) the difficulty of coming …
new parallel hardware such as GPUs because of three challenges:(1) the difficulty of coming …
Smash: Co-designing software compression and hardware-accelerated indexing for efficient sparse matrix operations
Important workloads, such as machine learning and graph analytics applications, heavily
involve sparse linear algebra operations. These operations use sparse matrix compression …
involve sparse linear algebra operations. These operations use sparse matrix compression …