GPU-Native Packet I/O via Commodity Ethernet
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Solution Overview
Problem
In data centers and cloud environments, the use of multiple GPUs connected through a network leads to network bottlenecks due to high networking overhead, with existing solutions requiring dedicated hardware or complex kernel-bypass methods that are inefficient and difficult to implement.
Innovation Solution
A GPU internal packet input/output method and device based on commodity Ethernet devices, utilizing peer-to-peer direct memory access (P2P-DMA) and persistent kernels to directly transfer network packets to GPU memory, eliminating the need for CPU intervention and memory copying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dedicated hardware such as RDMA HCA or SmartNIC is used for network I/O optimization, then network performance is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses commodity Ethernet NICs instead of expensive dedicated hardware like RDMA HCA or SmartNIC. The approach leverages existing standard network interfaces that are already deployed in data centers, avoiding the need for costly specialized hardware while achieving similar network I/O optimization benefits through software-based kernel-bypass mechanisms
Solution Approach 2:
The patent implements a virtualized network interface that copies network packet processing functionality to the GPU. By using virtualization and software emulation, the system replicates the performance benefits of dedicated hardware without requiring the actual expensive hardware components, thus reducing device complexity and cost
2Loss of time
If kernel-bypass I/O method is used to reduce network processing delay, then network latency is reduced, but implementation complexity and difficulty increase
Solution Approach 1:
The patent introduces a virtualized network interface as an intermediary layer between the network stack and the GPU. This virtual interface simplifies the kernel-bypass implementation by providing a standardized abstraction that handles packet processing without requiring direct manipulation of complex network protocols in the kernel space
Solution Approach 2:
The patent extracts network packet processing functionality from the traditional CPU-based kernel path and relocates it to the GPU. By separating these functions, the system achieves kernel-bypass performance while simplifying the implementation through clear functional decomposition and dedicated GPU-based processing units
3Ease of operation
If packets are copied one by one from host memory to GPU memory, then data transfer is simple, but processing time increases significantly
Solution Approach 1:
The patent implements packet batching where multiple packets are collected and prepared in advance before being transferred to the GPU. This preliminary gathering of packets in batches allows for more efficient single-shot transfers, reducing the total number of transfer operations and minimizing cumulative processing time while maintaining operational simplicity
Solution Approach 2:
The patent merges multiple individual packet copy operations into a single batched memory transfer operation. By combining multiple small transfers into one larger transfer, the system reduces overhead and improves throughput, achieving faster processing time without complicating the data transfer mechanism
4Productivity
If pipelining technique is used to process packets while gathering, then processing efficiency is improved, but tuning complexity and implementation effort increase
Solution Approach 1:
The patent implements dynamic packet batching where the batch size is automatically adjusted based on current system conditions and workload characteristics. This dynamic adaptation eliminates the need for manual tuning of pipelining parameters, as the system self-optimizes batch sizes to maximize processing efficiency under varying conditions
Solution Approach 2:
The patent changes the batch size parameter dynamically based on system state rather than using fixed values. By monitoring factors like packet arrival rates and GPU processing speed, the system adjusts batch parameters in real-time to optimize the balance between gathering overhead and processing throughput, eliminating complex manual tuning requirements
Data Source
AI summary
The disclosure relates to a method and device for inputting and outputting packets inside a GPU based on a commodity Ethernet device. According to embodiments of the disclosure, a method for commodity Ethernet device-based graphic processing unit (GPU) internal packet input/output performed by a GPU internal packet input/output device comprises being allocated an available packet buffer from a memory pool inside a GPU, after packets received from a network interface controller (NIC) are directly transferred to the allocated packet buffer, processing the directly transferred packets through a reception (Rx) kernel, transmitting a transmission packet to a network through the NIC according to an operation of a transmission (Tx) kernel, and returning the allocated packet buffer.