UMAP is a popular dimension reduction algorithm used in fields like bioinformatics, NLP topic modeling, and ML preprocessing. It works by creating a k-nearest neighbors (k-NN) graph, which is known in literature as an all-neighbors graph, to build a fuzzy topological representation of the data, which is used to embed high-dimensional data into lower dimensions. RAPIDS cuML already contained��
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CUDA Graphs are a way to define and batch GPU operations as a graph rather than a sequence of stream launches. A CUDA Graph groups a set of CUDA kernels and other CUDA operations together and executes them with a specified dependency tree. It speeds up the workflow by combining the driver activities associated with CUDA kernel launches and CUDA API calls. It also enforces the dependencies with��
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Graphs form the foundation of many modern data and analytics capabilities to find relationships between people, places, things, events, and locations across diverse data assets. According to one study, by 2025 graph technologies will be used in 80% of data and analytics innovations, which will help facilitate rapid decision making across organizations. When working with graphs containing��
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Join us April 12 and ask experts about NVIDIA cuGraph with added support for GNN, accelerated aggregators, models, and extensions to DGL and PyG.
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With the latest Memgraph Advanced Graph Extensions (MAGE) release, you can now run GPU-powered graph analytics from Memgraph in seconds, while working in Python. Powered by NVIDIA cuGraph, the following graph algorithms now execute on GPU: This tutorial shows you how to use PageRank graph analysis and Louvain community detection to analyze a Facebook dataset containing 1.3��
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Data scientists across various domains use clustering methods to find naturally ��similar�� groups of observations in their datasets. Popular clustering methods can be: The Hierarchical Density-Based Spatial Clustering of Applications w/ Noise (HDBSCAN) algorithm is a density-based clustering method that is robust to noise (accounting for points in sparser regions as either cluster��
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Graphs are mathematical structures used to model many types of relationships and processes in physical, biological, social and information systems. They are also used in the solution of various high-performance computing and data analytics problems. The computational requirements of large-scale graph processing for cyberanalytics, genomics, social network analysis and other fields demand powerful��
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Graph analysis is a fundamental tool for domains as diverse as social networks, computational biology, and machine learning. Real-world applications of graph algorithms involve tremendously large networks that cannot be inspected manually. Betweenness Centrality (BC) is a popular analytic that determines vertex influence in a graph. It has many practical use cases, including finding the best��
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