Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
ASReml-SA is a robust statistical software specifically tailored for mixed models that utilize Residual Maximum Likelihood (REML) for parameter estimation. Linear mixed-effects models serve as a versatile and comprehensive method for analyzing numerous datasets frequently encountered in fields such as animal, plant, and aquatic breeding, as well as in agriculture, environmental sciences, and medical research. The newest version, ASReml-SA 4.2, boasts three times the memory capacity of its predecessor 4.1, allowing for significantly larger analytical tasks to be performed. With enhancements in parallel processing and the capability to allocate memory for specific operations, the software has also seen improvements in speed; a comparison table is provided below to illustrate the speed enhancements realized across various analyses. ASReml-SA 4.2 not only accelerates processing but also offers users the potential for optimized performance tailored to their specific hardware and analytical needs. Ultimately, these advancements reflect ASReml-SA's commitment to facilitating efficient and effective data analysis.
Description
The RAPIDS software library suite, designed on CUDA-X AI, empowers users to run comprehensive data science and analytics workflows entirely on GPUs. It utilizes NVIDIA® CUDA® primitives for optimizing low-level computations while providing user-friendly Python interfaces that leverage GPU parallelism and high-speed memory access. Additionally, RAPIDS emphasizes essential data preparation processes tailored for analytics and data science, featuring a familiar DataFrame API that seamlessly integrates with various machine learning algorithms to enhance pipeline efficiency without incurring the usual serialization overhead. Moreover, it supports multi-node and multi-GPU setups, enabling significantly faster processing and training on considerably larger datasets. By incorporating RAPIDS, you can enhance your Python data science workflows with minimal code modifications and without the need to learn any new tools. This approach not only streamlines the model iteration process but also facilitates more frequent deployments, ultimately leading to improved machine learning model accuracy. As a result, RAPIDS significantly transforms the landscape of data science, making it more efficient and accessible.
API Access
Has API
API Access
Has API
Integrations
ActiveScale
Anaconda
Apache Spark
Capital One Spark Business Banking
Databricks
Domino Enterprise AI Platform
Gradient
HEAVY.AI
HPE Ezmeral Data Fabric
IBM Cloud
Integrations
ActiveScale
Anaconda
Apache Spark
Capital One Spark Business Banking
Databricks
Domino Enterprise AI Platform
Gradient
HEAVY.AI
HPE Ezmeral Data Fabric
IBM Cloud
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
VSN International
Founded
2000
Country
United Kingdom
Website
www.vsni.co.uk
Vendor Details
Company Name
NVIDIA
Founded
1993
Country
United States
Website
developer.nvidia.com/rapids
Product Features
Statistical Analysis
Analytics
Association Discovery
Compliance Tracking
File Management
File Storage
Forecasting
Multivariate Analysis
Regression Analysis
Statistical Process Control
Statistical Simulation
Survival Analysis
Time Series
Visualization
Product Features
Data Science
Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports