Nvidia corporation (20240211255). ROBUST, EFFICIENT MULTIPROCESSOR-COPROCESSOR INTERFACE simplified abstract

ROBUST, EFFICIENT MULTIPROCESSOR-COPROCESSOR INTERFACE

Organization Name

nvidia corporation

Inventor(s)

Ronald Charles Babich, Jr. of Murrysville PA (US)

John Burgess of Austin TX (US)

Jack Choquette of Palo Alto CA (US)

Tero Karras of Uusimaa (FI)

Samuli Laine of Uusimaa (FI)

Ignacio Llamas of Palo Alto CA (US)

Gregory Muthler of Austin TX (US)

William Parsons Newhall, Jr. of Woodside CA (US)

ROBUST, EFFICIENT MULTIPROCESSOR-COPROCESSOR INTERFACE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240211255 titled 'ROBUST, EFFICIENT MULTIPROCESSOR-COPROCESSOR INTERFACE

Simplified Explanation

The patent application describes systems and methods for an efficient interface between a streaming multiprocessor and an acceleration coprocessor in a GPU. This interface allows the coprocessor to accelerate specific operations by transferring data back and forth between the two processors.

• The multiprocessor writes input data for the operation to coprocessor-accessible storage locations.
• The multiprocessor then instructs the coprocessor to execute the operation.
• Finally, the multiprocessor reads the result data of the operation from coprocessor-accessible storage locations.

Key Features and Innovation

• Efficient and robust interface between a streaming multiprocessor and an acceleration coprocessor in a GPU.
• Acceleration of specific operations by transferring data between processors.
• Streamlined process of writing input data, executing operations, and reading result data.

Potential Applications

This technology can be applied in various GPU systems where acceleration of specific operations is required, such as in graphics rendering, machine learning, and scientific computing.

Problems Solved

This technology addresses the need for a seamless and efficient communication interface between a streaming multiprocessor and an acceleration coprocessor in a GPU to accelerate specific operations.

Benefits

• Improved performance and efficiency in GPU systems.
• Acceleration of specific operations without overburdening the main processor.
• Enhanced capabilities for graphics rendering, machine learning, and scientific computing applications.

Commercial Applications

• This technology can be utilized in high-performance computing systems, data centers, and AI applications to enhance processing speed and efficiency.
• Market implications include improved performance in GPU-intensive tasks and increased competitiveness in the technology sector.

Questions about the Technology

What are the potential applications of this technology beyond GPU systems?

This technology can also be applied in other parallel processing systems where acceleration of specific operations is required, such as in data centers and AI applications.

How does this technology improve the overall performance of GPU systems?

By offloading specific operations to an acceleration coprocessor, this technology reduces the workload on the main processor, leading to improved performance and efficiency in GPU systems.

Original Abstract Submitted

systems and methods for an efficient and robust multiprocessor-coprocessor interface that may be used between a streaming multiprocessor and an acceleration coprocessor in a gpu are provided. according to an example implementation, in order to perform an acceleration of a particular operation using the coprocessor, the multiprocessor: issues a series of write instructions to write input data for the operation into coprocessor-accessible storage locations, issues an operation instruction to cause the coprocessor to execute the particular operation; and then issues a series of read instructions to read result data of the operation from coprocessor-accessible storage locations to multiprocessor-accessible storage locations.