Search Results for "parallel programming frameworkc++"
Sort By:
Showing 15 open source projects for "parallel programming frameworkc++"
View related business solutions-
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
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.
-
1
Apache Beam
Unified programming model for Batch and Streaming
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. -
2
Earthly
The effortless CI/CD framework that runs anywhere
...Earthly uses Earthfiles, which combine concepts from Dockerfiles and Makefiles into a syntax intended to be easy for developers to read and maintain. It works with existing compilers, programming languages, frameworks, and build tools, allowing teams to wrap their existing build processes rather than replace them. Earthly automatically creates build dependency graphs, runs independent steps in parallel, and caches results to improve build performance. It can be used across monorepos and multiple repositories and integrates with CI/CD platforms such as GitHub Actions, GitLab CI/CD, Jenkins, and CircleCI. -
3
Orleans
Orleans is a cross-platform framework for building distributed apps
...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. -
4
protoactor-go
Proto Actor - Ultra fast distributed actors for Go, C# and Java/Kotlin
...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. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
5
OpenHCPP
A Directive-Based Heterogeneous Parallel Programming Framework
OpenHCPP is a high-level directive-based parallel programming framework. -
6
ProgramNet Designer
C++ cross plattform functional block programming
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. -
7
giServer
giServer the easy to use and extensible batch and integration server
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 -
8
Streams for IBM i
Batch performance boosting and Big Data framework for IBM i
Streamd for IBM i is a suite of tools for IBM i (previously known as AS/400 and iSeries) that can significantly improve performance characteristics of batch processes. Due to extensive use of parallel programming techniques Streams for IBM i delivers significant performance improvements for single streamed batch jobs. Streams for IBM i can split an existing batch process into a number of concurrent streams, completely eliminate backup-related delays, introduce new robust recovery policies and even modify the program logic of existing applications - all without any code modifications. ... -
9
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.
-
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
10
-
11
MORP (Module Orientated Robotic Programming) is a framework bringing base implementations in order to easily connect modules such as a NXT or Bioloid Robot. It provides its own scripting languages called 'morp' dedicated to robot programming.
-
12
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.
-
13
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
-
14
PyGWA is a GPGPU library for Python. It contains Python bindings for AMD CAL and PyGWA.DP - a toy data-parallel programming API.
-
15
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.
- Previous
- You're on page 1
- Next
