Show HN: Libefaxx – A Microbenchmark for AWS EFA GPU/CPU RDMA Communication (github.com)

A new benchmarking tool called Libefaxx has been introduced, specifically tailored for evaluating the performance of inter-node communication over AWS Elastic Fabric Adapter (EFA) during large-language-model (LLM) training. Unlike existing tools that primarily measure collective communication, Libefaxx allows for a deeper analysis of EFA's raw performance metrics, including latency, bandwidth, and GPU-Initiated Networking (GIN) behaviors. This is crucial as it supports engineers and researchers in optimizing distributed training pipelines, maximizing the efficiency of computations on AWS infrastructure. The repository also facilitates smoother development by offering a coroutine-based scheduler built on C++20, easing the complexities associated with asynchronous RDMA APIs. This enhancement allows developers to implement custom algorithms over EFA without getting bogged down in callback management. Initial benchmarks show promising results, with effective bandwidths reaching up to 380 Gbps when utilizing multiple EFA devices. The tool’s ability to scale effectively with device memory offers significant implications for the AI/ML community, particularly as the demand for high-performance cloud computing continues to rise for training sophisticated AI models.