Showing 2 open source projects for "gpu processing"

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    Context for your AI agents

    Crawl websites, sync to vector databases, and power RAG applications. Pre-built integrations for LLM pipelines and AI assistants.

    Build data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
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  • The Original Buy Center Software. Icon
    The Original Buy Center Software.

    Never Go To The Auction Again.

    VAN sources private-party vehicles from over 20 platforms and provides all necessary tools to communicate with sellers and manage opportunities. Franchise and Independent dealers can boost their buy center strategies with our advanced tools and an experienced Acquisition Coaching™ team dedicated to your success.
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    mapgraph

    mapgraph

    Massively Parallel Graph processing on GPUs -- now part of Blazegraph

    Mapgraph is SYSTAP’s disruptive new technology to exploit the main memory bandwidth advantages of GPUs. The early work was co-developed with the University of Utah SCI Institute and has its pedigree in the UINTAH software running on over 750M cores on the TITAN Super Computer. Today, SYSTAP has commercialized this technology into it’s Blazegraph Accelerator and Blazegraph HPC products. Checkout our options for GPU acceleration of graphs or contact us to learn more: ...
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    GENIE (GEne-geNe IntEraction)

    GPU based Parallel Gene-Gene Interaction Analysis

    ...Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. Citation: Chikkagoudar, S., Wang, K., & Li, M. (2011). GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores. BMC research notes, 4(1), 158.
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