Ring Attention With Blockwise Transformers for Distributed Long-Context Training: Scaling to 1 Million Token Sequences With Linear Communication Overhead

This study investigates ring attention architecture for distributed transformer training on sequences up to 1 million tokens with linear inter-device communication overhead and near-linear memory scaling across GPU nodes within the context of systems machine learning and distributed computing, an area of growing scientific importance given its implications for long-document LLM training, genomic sequence modeling, and video/multimodal context window scaling. Using ring attention implementation in JAX with all-gather communication overlapping with local attention computation, profiling of memory, throughput (tokens/sec), and communication overhead across sequence lengths 8k-1M on 4/8/16/32/64/128 A100 nodes, we examine ring topology distributing query, key, value partitions across GPUs with blockwise computation enabling each device to compute attention against one KV block at a time while receiving next block via ring all-gather, achieving O(N/P) memory per device and O(N) communication (vs. O(N^2) all-gather) in benchmark suite of 24 configurations: 6 sequence lengths (8k, 32k, 128k, 256k, 512k, 1M) x 4 GPU counts (16/32/64/128 A100s), each measured over 100 training steps with 3 repetitions drawn from Pacific Computing Institute A100 GPU cluster (NVLink+InfiniBand HDR 200 Gb/s interconnect) and Google TPU v4 pod for cross-hardware validation. Results indicate that ring attention achieves 1M token training on 128 A100s with 78 GB/GPU peak memory (vs. OOM for flash attention) at 0.84x throughput efficiency vs. theoretical linear scaling, with communication overhead stable at 8.4% across 8k-1M sequence lengths (p < 0.001), with 1M token on 128 A100s; 78 GB/GPU; 8.4% communication overhead stable across sequence lengths as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to systems machine learning and distributed computing and carry actionable implications for the design of programs and policies targeting long-document LLM training, genomic sequence modeling, and video/multimodal context window scaling.