Parallel Processor Packet Processing via Shared Memory Semaphores
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Solution Overview
Problem
Current parallel processing systems face inefficiencies in communication between compute nodes and graphics processing units (GPUs), particularly in processing packets in real-time, which hinders effective data processing and transmission.
Innovation Solution
The system employs one or more graphics processing units (GPUs) to detect and process packet data stored in memory, utilizing shared memory portions and semaphores for communication between CPUs, GPUs, and network interfaces to manage data processing and transmission efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If parallel processing systems use traditional communication techniques between compute nodes and GPUs, then system stability is maintained, but processing speed and real-time packet handling capability deteriorate
Solution Approach 1:
The patent introduces shared memory as an intermediary between CPU and GPU, allowing direct data exchange without traditional complex communication protocols. The semaphore mechanism acts as a simple mediator for synchronization, replacing elaborate inter-process communication systems and enabling faster packet processing.
Solution Approach 2:
The communication system is segmented into independent functional components: shared memory for data exchange, semaphores for synchronization, and event-driven notification mechanisms. This segmentation simplifies each component while maintaining overall system functionality and improving processing speed.
2Productivity
If GPUs process packets independently in real-time, then productivity is improved, but communication coordination between processors worsens
Solution Approach 1:
The GPU is empowered to independently detect packet availability in shared memory and process packets autonomously without continuous CPU intervention. The event-driven architecture allows the GPU to self-manage its processing queue, improving productivity while reducing coordination overhead through the simple semaphore mechanism.
Solution Approach 2:
The system implements event-driven feedback where the GPU notifies the CPU through semaphores when packets are processed or when shared memory needs replenishment. This feedback mechanism enables autonomous GPU operation while maintaining simple coordination through standardized notification protocols.
3Ease of operation
If shared memory and semaphores are used for communication, then ease of operation is improved, but device complexity increases
Solution Approach 1:
Shared memory serves multiple functions simultaneously: data storage, data exchange between CPU and GPU, and triggering event notifications. Semaphores provide universal synchronization and coordination across different processing operations. This multi-functionality simplifies operation by using standardized mechanisms for diverse communication needs.
Data Source
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AI summary
Apparatuses, systems, and techniques of using parallel processor(s), such as one or more graphics processing units, to process packets (e.g., in real time). In at least one embodiment, a processor (e.g., a parallel processing unit, a central processing unit, and/or the like) detects when packet data has been stored in a memory accessible by the parallel processing unit. Then, the parallel processing unit may process the packet data to produce output data.