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What is SYCL?

SYCL is a programming standard created by the Khronos Group that is open and free of royalties, designed to support heterogeneous and offload computing within modern ISO C++, providing a cohesive abstraction layer where host and device code coexist in a single C++ source file, and accommodating a variety of devices including CPUs, GPUs, FPGAs, and additional accelerators. Acting as a C++ API, SYCL improves the effectiveness and cross-platform compatibility of heterogeneous computing by utilizing standard programming constructs such as templates, inheritance, and lambda expressions, which empower developers to efficiently handle data and execution across multiple hardware platforms without relying on proprietary languages or extensions. Moreover, SYCL builds on the foundational ideas of acceleration backends like OpenCL, facilitating effortless integration with other technologies and ensuring a unified language framework, APIs, and ecosystem that streamline the tasks of identifying devices, managing data, and executing kernels effectively. This flexibility and compatibility make SYCL an attractive option for developers who are looking for a robust solution in the rapidly changing environment of heterogeneous computing. Its ability to provide a seamless programming experience while targeting diverse hardware platforms further enhances its appeal in the tech community.

What is Bright Cluster Manager?

Bright Cluster Manager provides a diverse array of machine learning frameworks, such as Torch and TensorFlow, to streamline your deep learning endeavors. In addition to these frameworks, Bright features some of the most widely used machine learning libraries, which facilitate dataset access, including MLPython, NVIDIA's cuDNN, the Deep Learning GPU Training System (DIGITS), and CaffeOnSpark, a Spark package designed for deep learning applications. The platform simplifies the process of locating, configuring, and deploying essential components required to operate these libraries and frameworks effectively. With over 400MB of Python modules available, users can easily implement various machine learning packages. Moreover, Bright ensures that all necessary NVIDIA hardware drivers, as well as CUDA (a parallel computing platform API), CUB (CUDA building blocks), and NCCL (a library for collective communication routines), are included to support optimal performance. This comprehensive setup not only enhances usability but also allows for seamless integration with advanced computational resources.

Media

Media

Integrations Supported

C++

Integrations Supported

C++

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

The Khronos Group

Date Founded

2000

Company Location

United States

Company Website

www.khronos.org

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/bright-cluster-manager

Categories and Features

Application Development

Access Controls/Permissions
Code Assistance
Code Refactoring
Collaboration Tools
Compatibility Testing
Data Modeling
Debugging
Deployment Management
Graphical User Interface
Mobile Development
No-Code
Reporting/Analytics
Software Development
Source Control
Testing Management
Version Control
Web App Development

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

HPC

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