Graph Neural Networks (GNNs) are a powerful tool for handling structured...
We introduce Graph of Thoughts (GoT): a framework that advances promptin...
Graph databases (GDBs) are crucial in academic and industry applications...
In this paper, we present PolarStar, a novel family of diameter-3 networ...
2.5D integration is an important technique to tackle the growing cost of...
Chips with hundreds to thousands of cores require scalable networks-on-c...
Graph databases (GDBs) enable processing and analysis of unstructured,
c...
Important graph mining problems such as Clustering are computationally
d...
In this paper we present PolarFly, a diameter-2 network topology based o...
High-performance clusters and datacenters pose increasingly demanding
re...
Graph neural networks (GNNs) are among the most powerful tools in deep
l...
Triangle count and local clustering coefficient are two core metrics for...
We present a parallel k-clique listing algorithm with improved work boun...
Matrix factorizations are among the most important building blocks of
sc...
The growing size of data center and HPC networks pose unprecedented
requ...
Determining I/O lower bounds is a crucial step in obtaining
communicatio...
Simple graph algorithms such as PageRank have been the target of numerou...
We propose GraphMineSuite (GMS): the first benchmarking suite for graph
...
Function-as-a-Service (FaaS) is one of the most promising directions for...
Graphs are by nature unifying abstractions that can leverage
interconnec...
We reduce the cost of communication and synchronization in graph process...
Today's graphs used in domains such as machine learning or social networ...
Developing high-performance and energy-efficient algorithms for maximum
...
Emerging chips with hundreds and thousands of cores require networks wit...
Vectorization and GPUs will profoundly change graph processing. Traditio...
We propose a topology-aware distributed Reader-Writer lock that accelera...
Atomic operations (atomics) such as Compare-and-Swap (CAS) or Fetch-and-...
We propose Atomic Active Messages (AAM), a mechanism that accelerates
ir...
Remote Memory Access (RMA) is an emerging mechanism for programming
high...
Dense linear algebra kernels, such as linear solvers or tensor contracti...
We develop the first parallel graph coloring heuristics with strong
theo...
The recent line of research into topology design focuses on lowering net...
Modern interconnects offer remote direct memory access (RDMA) features. ...
Graph processing has become an important part of various areas of comput...
We introduce a high-performance cost-effective network topology called S...
We propose Slim Graph: the first programming model and framework for
pra...
Jaccard Similarity index is an important measure of the overlap of two s...
Remote memory access (RMA) is an emerging high-performance programming m...
Graph processing has become an important part of multiple areas of compu...
We propose COSMA: a parallel matrix-matrix multiplication algorithm that...
Applications often communicate data that is non-contiguous in the send- ...
We introduce FatPaths: a simple, generic, and robust routing architectur...
Graph processing has become an important part of various areas, such as
...
We introduce Deep500: the first customizable benchmarking infrastructure...
Various graphs such as web or social networks may contain up to trillion...
Betweenness centrality (BC) is a crucial graph problem that measures the...