Nvidia corporation (20240193017). OPTIMIZING INTERMEDIATE OUTPUT ACCUMULATION OF PARALLEL PROCESSING OPERATIONS IN STREAMING AND LATENCY-SENSITIVE APPLICATIONS simplified abstract
Contents
- 1 OPTIMIZING INTERMEDIATE OUTPUT ACCUMULATION OF PARALLEL PROCESSING OPERATIONS IN STREAMING AND LATENCY-SENSITIVE APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 OPTIMIZING INTERMEDIATE OUTPUT ACCUMULATION OF PARALLEL PROCESSING OPERATIONS IN STREAMING AND LATENCY-SENSITIVE APPLICATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Efficient Parallel Execution of Multiple Processes
- 1.13 Original Abstract Submitted
OPTIMIZING INTERMEDIATE OUTPUT ACCUMULATION OF PARALLEL PROCESSING OPERATIONS IN STREAMING AND LATENCY-SENSITIVE APPLICATIONS
Organization Name
Inventor(s)
Dominik Wachowicz of Gdansk (PL)
OPTIMIZING INTERMEDIATE OUTPUT ACCUMULATION OF PARALLEL PROCESSING OPERATIONS IN STREAMING AND LATENCY-SENSITIVE APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240193017 titled 'OPTIMIZING INTERMEDIATE OUTPUT ACCUMULATION OF PARALLEL PROCESSING OPERATIONS IN STREAMING AND LATENCY-SENSITIVE APPLICATIONS
Simplified Explanation
The patent application describes techniques for efficiently executing multiple processes in real-time streaming and latency-sensitive applications by running processing threads in parallel, storing their output in accumulation buffers, and aggregating the data.
Key Features and Innovation
- Efficient parallel execution of multiple processes
- Real-time streaming and latency-sensitive applications
- Parallel processing threads
- Accumulation buffers for storing data output
- Aggregation function for generating aggregated data
Potential Applications
The technology can be applied in various fields such as:
- Financial trading platforms
- Video streaming services
- Online gaming systems
- Industrial automation processes
Problems Solved
The technology addresses the following issues:
- Improving performance in real-time applications
- Reducing latency in data processing
- Enhancing scalability for handling multiple processes simultaneously
Benefits
The benefits of this technology include:
- Faster processing of data streams
- Improved responsiveness in real-time applications
- Enhanced efficiency in handling multiple processes concurrently
Commercial Applications
- The technology can be utilized in high-frequency trading systems to process market data in real-time.
- Video streaming platforms can benefit from the improved performance in delivering content to users.
- Industrial automation companies can enhance their production processes by efficiently handling multiple tasks simultaneously.
Prior Art
Readers interested in prior art related to this technology can explore research papers on parallel processing, real-time data streaming, and latency-sensitive applications.
Frequently Updated Research
Stay updated on the latest advancements in parallel processing techniques, real-time data streaming technologies, and latency optimization strategies to enhance the efficiency of multiple process execution.
Questions about Efficient Parallel Execution of Multiple Processes
How does the technology improve performance in real-time streaming applications?
The technology enhances performance by executing processing threads in parallel, reducing latency in data processing, and aggregating data efficiently.
What are the potential commercial applications of this technology?
The technology can be applied in financial trading platforms, video streaming services, and industrial automation processes to improve efficiency and scalability.
Original Abstract Submitted
disclosed are apparatuses, systems, and techniques for efficient parallel execution of multiple processes in real-time streaming and latency-sensitive applications. the techniques include but are not limited to executing in parallel multiple processing threads, storing data output by the multiple processing threads in respective accumulation buffers, and applying an aggregation function to the stored data to generate an aggregated data.
