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![\"NVIDIA](\"https:\/\/image.blockchain.news:443\/features\/D8E08E86F8EDBDDCD68414CF49BDD8B1401B11A69515DFF98E6B2B03EE9CF9D7.jpg\")
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NVIDIA has unveiled new capabilities for its DOCA GPUNetIO library, enabling GPU-accelerated Remote Direct Memory Access (RDMA) for real-time inline GPU packet processing. This enhancement leverages technologies such as GPUDirect RDMA and GPUDirect Async, allowing a CUDA kernel to directly communicate with the network interface card (NIC), bypassing the CPU. This update aims to improve GPU-centric applications by reducing latency and CPU utilization, according to the NVIDIA Technical Blog<\/a>.<\/p>\n Previously, DOCA GPUNetIO, along with DOCA Ethernet and DOCA Flow, was used for packet transmissions over the Ethernet transport layer. The latest update, DOCA 2.7, introduces a new set of APIs that enable RDMA communications directly from a GPU CUDA kernel using RoCE or InfiniBand transport layers. This development allows for high-throughput, low-latency data transfers by enabling the GPU to control the data path of the RDMA application.<\/p>\n RDMA allows direct access between the main memory of two hosts without involving the operating system, cache, or storage. This is achieved by registering and sharing a local memory area with the remote host, enabling high-throughput and low-latency data transfers. The process involves three fundamental steps: local configuration, exchange of information, and data path execution.<\/p>\n With the new GPUNetIO RDMA functions, the application can manage the data path of the RDMA application on the GPU, executing the data path step with a CUDA kernel instead of the CPU. This reduces latency and frees up CPU cycles, allowing the GPU to be the main controller of the application.<\/p>\n NVIDIA has conducted performance comparisons between GPUNetIO RDMA functions and IB Verbs RDMA functions using the perftest microbenchmark suite. The tests were executed on a Dell R750 machine with an NVIDIA H100 GPU and a ConnectX-7 network card. The results show that DOCA GPUNetIO RDMA performance is comparable to IB Verbs perftest, with both methods achieving similar peak bandwidth and elapsed times.<\/p>\n For the performance tests, parameters were set to 1 RDMA queue, 2,048 iterations, and 512 RDMA writes per iteration, with message sizes ranging from 64 to 4,096 bytes. Both implementations reached up to 16 GB\/s in peak bandwidth when increased to four queues, demonstrating the scalability and efficiency of the new GPUNetIO RDMA functions.<\/p>\n The architectural choice of offloading RDMA data path control to the GPU offers several benefits:<\/p>\n This update is particularly beneficial for network applications where data processing occurs on the GPU, enabling more efficient and scalable solutions. For more details, visit the NVIDIA Technical Blog<\/a>.<\/p>\nEnhanced RDMA Functionality<\/h2>\n
RDMA GPU Data Path<\/h2>\n
Performance Comparison<\/h2>\n
Benefits and Applications<\/h2>\n
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