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Gpu for large cytoscape networks10/2/2023 ![]() it worked! We have reached sustainable growth & measurably best-in-class performance, so we are now growing, releasing more (including another big OSS visual auto-AI release just this week), and overall moving to next phases. servers having Graphical Processing Unit (GPU) or high-end computing. We are aiming for a model basically somewhere between gitlab and GitHub, and as we hit more sustainability, keep biasing for more free & OSS. Cytoscape (Otasek et al., 2019 Cytoscape, 2020), available both as a JavaScript. Data round-trip between Cytoscape and igraph is easy. Likewise, we have + are steadily launching things not in Gephi yet you'd expect of modern team+enterprise tools like sharing, RBAC, SSO, daily-scanned docker/k8s/AMIs, etc. It is written in C++ and easily scales to massive networks with hundreds of. by automating UMAP and graph neural network flows). So our free SaaS tier aims to include everything in Gephi, and a lot missing in it for modern use: GPU accel, DB connectors & visual playbooks (already in self-hosted), visual graph ETL, and launching a bunch of graph AI stuff (entity linking, event scoring, recommendations. Creating Networks¶ There are 4 different ways of creating networks in Cytoscape: Importing pre-existing, fixed-format network files. GPU-accelerated Rendering for Cytoscpe: Design and develop the prototype of 2d network graph renderer, which takes advantage of high speed GPU calculation. We prioritized sustainability as an engine for reliable & growable OSS, which has worked (ex: you may have heard of Apache Arrow, which we helped kick off). Gephi got a bit frozen in time due to the usual problem of struggling for post-phd sustainability by not building it in: I'm a big fan of the founders and their work, and just like Graphviz (att research canceled it), it was painful watching them having to leave something so cool. Mostly data scientists today, and as we have been launching no-code & low-code features, a diverse broader analyst community has been growing, who has been inspiring. It's used a lot by folks doing fraud, IT, social, security, supply chains, anti-misinfo, finance, bio, etc. This approach utilizes the full computing power of multi-core central processing units and graphics processing units. Our visual graph AI tool includes a GPU-accelerated take on gephi's flows and puts on the web, including a free GPU-accelerated tier with no-code UIs, embedding & control APIs (python, js, react, arrow), and deep pydata integration (Jupyter, RAPIDS, dashboarding like databricks & streamlit.
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