Search Results for "parallel programming frameworkc++"

Apache Beam is an open source, unified programming model to define both batch and streaming data-parallel processing pipelines, as well as certain language-specific SDKs for constructing pipelines and Runners. These pipelines are executed on one of Beam’s supported distributed processing back-ends, which include Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow.

...The Actor Model provides a higher level of abstraction for writing concurrent and distributed systems. It alleviates the developer from having to deal with explicit locking and thread management, making it easier to write correct concurrent and parallel systems. Grain abstraction, which provides a straightforward approach to building distributed interactive applications, without the need to learn complex programming patterns for handling concurrency, fault tolerance, and resource management. This allows Proto.Actor to leverage in-process performance for realtime stream processing.

...It was created by Microsoft Research and introduced the Virtual Actor Model as a novel approach to building a new generation of distributed systems for the Cloud era. The core contribution of Orleans is its programming model which tames the complexity inherent to highly-parallel distributed systems without restricting capabilities or imposing onerous constraints.

OpenHCPP is a high-level directive-based parallel programming framework.

With the help of the ProgramNet the user can develop plug-ins(called units) which can be connected with other units forming a net. The net has a form of a flow diagram. The resulted net can be executed in the ProgramNet for testing purposes or independently in the target platform as a final release. In order to execute a net the ProgramNet uses the kernel. The kernel executes automatically each unit in a separate thread and manages the data exchange between the units.

The giServer is an easy-to-use integration server for process automation and event-driven or scheduled execution of batch jobs. Instead of using complex XML configuration files an elaborate GUI for batch job management is included. Some possible usage scenarios are: - Automatic processing of incoming data files - Big Data applications - Process automation - Data Mining/Aggregation applications - Automatic Reporting - Processing and analysis of database records

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. Each microthread is conceptually similar to a task in Ada and it is much lighter weight than an operating system thread. SpikeOS was designed to handle millions of microthreads, for example in a neural network hosting millions of spiking model neurons.

Python bindings for OpenCL, the open standard for parallel programming of heterogeneous systems

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.

Python Integrated Parallel Programming EnviRonment (PIPPER), Python pre-parser that is designed to manage a pipeline, written in Python. It enables automated parallelization of loops. Think of it like OpenMP for Python, but it works in a computer cluster

PyGWA is a GPGPU library for Python. It contains Python bindings for AMD CAL and PyGWA.DP - a toy data-parallel programming API.

FastPara is a C++ programming framework for writing data-parallel applications in cluster environments. It builds the code framework for the data-parallel applications based on their data distribution declaration.