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Description
OpenCL, or Open Computing Language, is a free and open standard designed for parallel programming across various platforms, enabling developers to enhance computation tasks by utilizing a variety of processors like CPUs, GPUs, DSPs, and FPGAs on supercomputers, cloud infrastructures, personal computers, mobile gadgets, and embedded systems. It establishes a programming framework that comprises a C-like language for crafting compute kernels alongside a runtime API that facilitates device control, memory management, and execution of parallel code, thereby providing a portable and efficient means to access heterogeneous hardware resources. By enabling the delegation of compute-heavy tasks to specialized processors, OpenCL significantly accelerates performance and responsiveness across numerous applications, such as creative software, scientific research tools, medical applications, vision processing, and the training and inference of neural networks. This versatility makes it an invaluable asset in the evolving landscape of computing technology.
Description
Recent breakthroughs in natural language processing, comprehension, and generation have been greatly influenced by the development of large language models. This research presents a system that employs Ascend 910 AI processors and the MindSpore framework to train a language model exceeding one trillion parameters, specifically 1.085 trillion, referred to as PanGu-{\Sigma}. This model enhances the groundwork established by PanGu-{\alpha} by converting the conventional dense Transformer model into a sparse format through a method known as Random Routed Experts (RRE). Utilizing a substantial dataset of 329 billion tokens, the model was effectively trained using a strategy called Expert Computation and Storage Separation (ECSS), which resulted in a remarkable 6.3-fold improvement in training throughput through the use of heterogeneous computing. Through various experiments, it was found that PanGu-{\Sigma} achieves a new benchmark in zero-shot learning across multiple downstream tasks in Chinese NLP, showcasing its potential in advancing the field. This advancement signifies a major leap forward in the capabilities of language models, illustrating the impact of innovative training techniques and architectural modifications.
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Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
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Types of Training
Training Docs
Yes
Webinars
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Live Training (Online)
No
In Person
No
Vendor Details
Company Name
The Khronos Group
Founded
2000
Country
United States
Website
www.khronos.org
Vendor Details
Company Name
Huawei
Founded
1987
Country
China
Website
huawei.com