Search Results for "jpk data processing"
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Showing 12 open source projects for "jpk data processing"
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Run Any Workload on Compute Engine VMsCompute Engine delivers high-performance virtual machines for web apps, databases, containers, and AI workloads. Choose from general-purpose, compute-optimized, or GPU/TPU-accelerated machine types—or build custom VMs to match your exact specs. With live migration and automatic failover, your workloads stay online. New customers get $300 in free credits.
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Ship AI Apps Faster with Vertex AIShip AI apps and features faster with Vertex AI—your end-to-end AI platform. Access Gemini 3 and 200+ foundation models, fine-tune for your needs, and deploy with enterprise-grade MLOps. Build chatbots, agents, or custom models. New customers get $300 in free credit.
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HPCC Systems
End-to-end big data in a massively scalable supercomputing platform.
HPCC Systems® (www.hpccsystems.com) from LexisNexis® Risk Solutions is a proven, open source solution for Big Data insights that can be implemented by businesses of all sizes. With HPCC Systems, developers can design applications with Big Data at their core, enabling businesses to better analyze and understand data at scale, improving business time to results and decisions. HPCC Systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. -
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dispy
Distributed and Parallel Computing with/for Python.
dispy is a generic and comprehensive, yet easy to use framework for creating and using compute clusters to execute computations in parallel across multiple processors in a single machine (SMP), among many machines in a cluster, grid or cloud. dispy is well suited for data parallel (SIMD) paradigm where a computation (Python function or standalone program) is evaluated with different (large) datasets independently. dispy supports public / private / hybrid cloud computing, fog / edge computing. -
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FlowVR is an open source middleware tailored for high performance in situ data processing and analytics running on large parallel machines
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distributedPHP client
A simple script for distributed computing through PHP:
distributedPHP client is a simple PHP script that can simultaneously activate/send data to as many web scripts as you want. You must open and configure the distributedPHP .php file prior to running it. ditributedPHP client supports activating scripts without data, sending the same data to all scripts, sending unique data to each script or sending user input to each script. Examples of use include: distributed math computation, encryption breaking, SETI@home/folding@home (well, if they made the projects in php..) distributed bruteforce attacks, ddos attacks, distributed processing, etc.. ... -
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The Simple, Scalable, Script-based Science Processor for Measurements (S4PM) is a system for highly automated processing of science data, scalable up to large processing systems and scalable down to small, special-purpose processing strings.
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This project aims to create a method able to determine the most frequent word phrases in a large source of text data (>5 Gb) using the computational power of multiple processors.
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Open Source Remote Sensing OSSIM project, pronounced "awesome", will leverage existing algorithms/tools/packages from the open source community in construction of the ultimate Remote Sensing/Image Processing/GIS package. osgPlanet extends OSSIM and Op
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Collection of Java tools and packages related to server-side (web application-oriented) XML processing, such as Adaptive XML Inclusions system with demo. Tools for personalization of XML content, Java-XML data binding and others will come later.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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The DataTime Process Framework is intended to support the processing of time-based data in a modular, concurrent, distributed and extensible manner. C++, using YARP, ACE, Qt and MUSCLE on Linux, OSX, Windows and Solaris.
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Allows creation of a distributed data processing system. Provided is a pluggable Daemon and a framework for writing custom plugins. Plugins can be changed and configured remotely and on the fly. Configurations with example plugins are provided.
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An implementation of a high performance distributed storage and processing system for large amounts of structured data.
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