Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • IONOS Cloud GPU Servers Reviews & Ratings
    4.6 out of 5 stars 45,816 Ratings
    Company Website for IONOS Cloud GPU Servers
  • SurveyJS Reviews & Ratings
    4.8 out of 5 stars 64 Ratings
    Company Website for SurveyJS
  • JS7 JobScheduler Reviews & Ratings
    5.0 out of 5 stars 1 Rating
    Company Website for JS7 JobScheduler
  • Dragonfly Reviews & Ratings
    4.9 out of 5 stars 16 Ratings
    Company Website for Dragonfly
  • QA Wolf Reviews & Ratings
    4.8 out of 5 stars 284 Ratings
    Company Website for QA Wolf
  • DialerAI Reviews & Ratings
    4.8 out of 5 stars 5 Ratings
    Company Website for DialerAI
  • Proton Pass Reviews & Ratings
    4.8 out of 5 stars 31,996 Ratings
    Company Website for Proton Pass
  • Vantage Reviews & Ratings
    4.4 out of 5 stars 16,475 Ratings
    Company Website for Vantage
  • AlsoThere Reviews & Ratings
    5.0 out of 5 stars 1 Rating
    Company Website for AlsoThere
  • LTX Reviews & Ratings
    4.4 out of 5 stars 182 Ratings
    Company Website for LTX

What is MPI for Python (mpi4py)?

In recent times, high-performance computing has become increasingly available to a larger pool of researchers in the scientific field than it ever has been before. The effective synergy of high-quality open-source software and reasonably priced hardware has played a crucial role in the widespread utilization of Beowulf class clusters and workstation clusters. Among the various approaches to parallel computation, message-passing has stood out as a notably efficient model. This approach is particularly advantageous for distributed memory systems and is heavily relied upon in today’s most challenging scientific and engineering tasks related to modeling, simulation, design, and signal processing. However, the environment for portable message-passing parallel programming used to be complicated, as developers had to navigate a multitude of incompatible choices. Fortunately, this scenario has vastly improved since the MPI Forum established its standard specification, which has simplified the development process considerably. Consequently, researchers are now able to dedicate more of their efforts to advancing their scientific research instead of dealing with the intricacies of programming. This shift not only enhances productivity but also fosters innovation across various disciplines.

What is Graph Engine?

Graph Engine (GE) is an advanced distributed in-memory data processing platform that utilizes a strongly-typed RAM storage system combined with a flexible distributed computation engine. This RAM storage operates as a high-performance key-value store, which can be accessed throughout a cluster of machines, enabling efficient data retrieval. By harnessing the power of this RAM store, GE allows for quick random data access across vast distributed datasets, making it particularly effective for handling large graphs. Its capacity to conduct fast data exploration and perform distributed parallel computations makes GE a prime choice for processing extensive datasets, specifically those with billions of nodes. The engine adeptly supports both low-latency online query processing and high-throughput offline analytics, showcasing its versatility in dealing with massive graph structures. The significance of schema in efficient data processing is highlighted by the necessity of strongly-typed data models, which are crucial for optimizing storage and accelerating data retrieval while maintaining clear data semantics. GE stands out in managing billions of runtime objects, irrespective of their sizes, and it operates with exceptional efficiency. Even slight fluctuations in the number of objects can greatly affect performance, emphasizing that every byte matters. Furthermore, GE excels in rapid memory allocation and reallocation, leading to impressive memory utilization ratios that significantly bolster its performance. This combination of capabilities positions GE as an essential asset for developers and data scientists who are navigating the complexities of large-scale data environments, enabling them to derive valuable insights from their data with ease.

Media

Media

Integrations Supported

C
C++
Fortran
NumPy
Python

Integrations Supported

API Availability

API Availability

Has API

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided

Supported Platforms

Windows
Mac
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

MPI for Python

Company Website

mpi4py.readthedocs.io/en/stable/

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

www.graphengine.io

Categories and Features

Component Libraries

Not specified

Categories and Features

Graph Databases

Not specified

Popular Alternatives

GASP Reviews & Ratings

GASP

AeroSoft

Popular Alternatives

Sparksee Reviews & Ratings

Sparksee

Sparsity Technologies
Dgraph Reviews & Ratings

Dgraph

Hypermode
HyperGraphDB Reviews & Ratings

HyperGraphDB

Kobrix Software