The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey
Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …
neural network architecture is capable of processing graph structured data and bridges the …
FPGA HLS today: successes, challenges, and opportunities
The year 2011 marked an important transition for FPGA high-level synthesis (HLS), as it
went from prototy** to deployment. A decade later, in this article, we assess the progress …
went from prototy** to deployment. A decade later, in this article, we assess the progress …
A modern primer on processing in memory
Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …
design choice goes directly against at least three key trends in computing that cause …
Dorylus: Affordable, scalable, and accurate {GNN} training with distributed {CPU} servers and serverless threads
A graph neural network (GNN) enables deep learning on structured graph data. There are
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …
{LegoOS}: A disseminated, distributed {OS} for hardware resource disaggregation
The monolithic server model where a server is the unit of deployment, operation, and failure
is meeting its limits in the face of several recent hardware and application trends. To improve …
is meeting its limits in the face of several recent hardware and application trends. To improve …
Aligraph: A comprehensive graph neural network platform
An increasing number of machine learning tasks require dealing with large graph datasets,
which capture rich and complex relationship among potentially billions of elements. Graph …
which capture rich and complex relationship among potentially billions of elements. Graph …
AWB-GCN: A graph convolutional network accelerator with runtime workload rebalancing
Deep learning systems have been successfully applied to Euclidean data such as images,
video, and audio. In many applications, however, information and their relationships are …
video, and audio. In many applications, however, information and their relationships are …
Clipper: A {Low-Latency} online prediction serving system
Clipper: A Low-Latency Online Prediction Serving System Page 1 This paper is included in the
Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation …
Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation …
Snap: A general-purpose network analysis and graph-mining library
Large networks are becoming a widely used abstraction for studying complex systems in a
broad set of disciplines, ranging from social-network analysis to molecular biology and …
broad set of disciplines, ranging from social-network analysis to molecular biology and …
Processing data where it makes sense: Enabling in-memory computation
Today's systems are overwhelmingly designed to move data to computation. This design
choice goes directly against at least three key trends in systems that cause performance …
choice goes directly against at least three key trends in systems that cause performance …