RCCLX: Innovating GPU Communications on AMD Platforms

This post introduces RCCLX, an open-sourced enhanced version of RCCL for AMD platforms, integrated with Torchcomms. It details two new features: Direct Data Access (DDA) for lightweight intra-node collectives and Low Precision Collectives (LP). DDA algorithms (flat and tree) improve AllReduce latency by enabling direct memory access between ranks, showing 10-50% performance improvements on AMD MI300X GPUs for decoding and 10-30% for prefill. LP collectives utilize FP8 quantization for up to 4:1 compression, reducing communication overhead for large message sizes (≥16MB) and yielding ~7% increase in throughput and ~9-10% decrease in latency for E2E inference workloads. The post also mentions the integration of CTran to AMD platforms for GPU-resident collectives and highlights the ease of adaptation for AI models through the Torchcomms API.