Search Results for "parallel computing" - Page 2
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Showing 66 open source projects for "parallel computing"
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End-To-End Document Management SoftwareUnForm® is a platform-independent software product that creates, delivers, stores and retrieves graphically enhanced documents from ERP application printing. A complete, end-to-end document management solution, UnForm interfaces at the point of printing to produce documents in various formats for printing and electronic delivery.
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Connect with customers in one appDialpad Connect is an AI-powered unified communications platform that combines voice, video, and messaging to enhance team collaboration and customer interactions. It features real-time call transcription, automated call summaries, and AI-generated action items to help users stay focused during conversations. The platform integrates seamlessly with popular business apps like Salesforce, Zendesk, Microsoft Teams, and Google Workspace to streamline workflows. Designed for businesses of all sizes, Dialpad Connect delivers enterprise-grade reliability with 100% uptime SLA and robust disaster recovery. Security and privacy are core priorities, meeting standards like GDPR, HIPAA, and SOC 2 compliance. Dialpad Connect helps companies elevate customer experiences while boosting team productivity.
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A framework to run MATLAB programs as batch jobs. Features a structured input description, integrity constraints and GUI.Independent parts of a job can execute in parallel on a cluster computer. Developed at Freiburg Brain Imaging (FBI) - http://fbi.uniklinik-freiburg.de/
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Chapel
a Productive Parallel Programming Language
Chapel is an emerging parallel programming language whose design and development are being led by HPE in collaboration with academia, computing labs, and industry. Chapel's goal is to improve the productivity of parallel programmers, from laptops to supercomputers. **Please note that Chapel development has moved to GitHub** -
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PyCNN
Image Processing with Cellular Neural Networks in Python
Image Processing with Cellular Neural Networks in Python. Cellular Neural Networks (CNN) are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors. -
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Bat2015
Bachelor of Science (Informatik)
...With a focus on the MILP methods we implement a load balancing and speed up the solving process in a multiplicative way. Sometimes we have super-linear speedup with a small set of hardware. With a splitting of problems, parallel computing and distributing the actual best solution to all running processes we solve CBP much faster than a sequential processing can do. -
Modernize Your Lab with the #1 Rated LIMSNothing is more critical to a lab’s success than the quality, security, and traceability of samples. The Lockbox LIMS system provides robust sample management functionality to laboratory professionals, giving them full visibility on every aspect of a sample’s journey, from accessioning to long-term storage.
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Center for Parallel Computing
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neuranep
Neural Network Engineering Platform
A parallel-programming framework for concurrently running large numbers of small autonomous jobs, or microthreads, across multiple cores in a CPU or CPUs in a cluster. NeuraNEP emulates a distributed processing environment capable of handling millions of microthreads in parallel, for example running neural networks with millions of spiking cells. Microthreads are general processing elements that can also represent non-neural elements, such as cell populations, extracellular space, emulating... -
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PUB-Web is a peer-to-peer based volunteer computing environment for parallel applications written in Java.
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Genetic Programming in OpenCL is a parallel implementation of genetic programming targeted at heterogeneous devices, such as CPU and GPU. It is written in OpenCL, an open standard for portable parallel programming across many computing platforms.
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PeerGFS PEER Software - File Sharing and CollaborationPeerGFS is a software-only solution developed to solve file management/file replication challenges in multi-site, multi-platform, and hybrid multi-cloud environments.
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Fast Matrix for Java
General purpose matrix utilities for Java in Parallel Computing
Fast Matrix for Java (fm4j) is a general-purpose matrix utility library for computing with dense matrices. fm4j encapsulated different underlying implementations and select the optimal one in run-time depending on the size of the input matrix. Moreover, fm4j employs Java (Tm) Concurrency to take advantage of the computation power of multi-cor processors. -
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PPSeq: Parallel NGS Analysis
Parallel Processing for Next-Generation Sequencing (NGS) Analysis
High-throughput next generation sequencing (NGS) technology has quickly emerged as a powerful tool in many aspects of biomedical research. However, along with its rapid development, the data magnitude and analysis complexity for NGS far exceed the capacity and capability of traditional small-scale computing facilities, such as multithreading algorithms on standalone workstations. To address this issue, here we present a solution using the ever-increasing supply of processing power by massive parallel processing (MPP) systems. -
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AWK~plus is the next generation script practice environment. The AWK Language specifications and a main extension of GNU GAWK. Combination of Dynamic and Static typing. Parallel computing that a lock is free, and is thread safe at a language level.
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Tuple Spaces
Tuple space with time outs and transactions.
Java implementation of a Tuplespace. Moved to https://github.com/mike-k-houghton/tuplespace A Tuple is an ordered list of items. A Tuple Space is a form of associative memory where entries, tuples, are stored in the space and are retrieved using search criteria that are based on the intrinsic properties of the tuples. The two key operations are put get And that is it! There are refinements on these operations including, for example, timeouts where the tuple will only exist... -
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Project provides a set of concurrent building blocks (Java & C/C++) that can be used to develop parallel/multi-threaded applications. Components are grouped into 4 categories: 1.Data Structures 2. Parallel Patterns 3.Parallel functions 4.Atomics and STM
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Cca-forum unifies the Common Component Architecture tools and tutorial. It includes the CCA specifications, the Ccaffeine framework for HPC, and related tools. These support multilanguage scientific and parallel computing.
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GridABM is an easy-to-use toolkit to develop high-performance agent-based models (ABMs) in Repast. GridABM facilitates large-scale simulation of ABM on multicore computers, local computing clusters, or computational grids.
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Distributed Parallel Programming for Python! This package builds on traditional Python by enabling users to write distributed, parallel programs based on MPI message passing primitives. General python objects can be messaged between processors. Ru
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Roomy is a programming language extension for writing parallel disk-based applications. All details of parallelism and disk I/O are hidden within the Roomy library.
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Tsunami is an open-source high performance computing language. With it you can write streaming data-parallel algorithms that utilize GPGPUs for orders-of-magnitude speed-up with the ease of writing sequential algorithms.
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PyMW is a Python module for parallel master-worker computing in a variety of environments. With the PyMW module, users can write a single program that scales from multicore machines to global computing platforms.
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Parallelizable is a tiny framework in complement for java.util.conccurent api. Parallelizable is an abstract class to extend for making asynchronous parallel execution on a synchronous task. Very usefull if task often calls a very long process.
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Meerkat is a distributed programming environment. It consists of a virtual machine which is suited to parallel processing. The data model is based on the concept of actors, although it is much more permissive than the traditional description.
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DKU is an embedded parallel language, or "piggy-back" language that uses function calls to invoke the language's execution model. It tunes task-size to the hardware while hiding hardware details for high performance portability of parallel code.
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LIME (Less-is-More) is parallel/concurrent programming environment based on C. Internally, it uses XML technology to describe tasks and their dependencies. Externally, it offers the ANSI C99 programming as well as a set of visually-oriented interfaces.
