Computing methodologies
RingX: Scalable Parallel Attention for Long-Context Learning on HPC
RingX significantly improves parallel attention for long-context AI models on HPC systems by optimizing workload partitioning and communication patterns, achieving up to 3.4x speedup over conventional ring attention. It demonstrates enhanced training efficiency for ViT and GPT applications, offering higher FLOPs utilization and better accuracy for long sequences.
3.4x
Speedup over Ring Attention
38%
Model FLOPs Utilization (Llama3 8B)
Deep Analysis & Enterprise Applications
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1.5x
End-to-End Training Speedup for ViT and GPT
Enterprise Process Flow
Chunk Q, K, V Tensors
→
Broadcast Q (Bi-directional)
→
Allgather K, V (Causal)
→
Local FlashAttention Compute
→
Reduce/Allreduce Local Results
→
Accumulate Gradients
| Method | Communication (forward) | Memory | Context |
|---|---|---|---|
| Megatron | all-gather(qkv) (6*b*h) | O(s*h*b) | limited |
| Ring/Stripe | send-recv(kv) (4*b*h) | O(h*b) | unlimited |
| RingX1/2 | broadcast(q), all-reduce(lse) (N*b*h) | O(h*b) | unlimited |
| RingX3 | all-gather(kv) (4*b*h) | O(s*h*b) | limited |
| RingX4 | broadcast(kv), reduce(dkdv) (4*b*h) | O(h*b) | unlimited |
RingX for Llama3 8B Training
RingX enabled training of the Llama3 8B model with context lengths up to 1 million tokens, achieving a 94% scaling efficiency on 4096 GPUs. It demonstrated a 1.5x speedup over stripe and ring methods, and a 38% MFU, one of the highest reported for long-context learning on HPC systems. This significantly improves language model capabilities by enabling much longer contextual understanding.
Calculate Your Potential AI ROI
By leveraging RingX's optimized parallel attention, enterprises can significantly reduce the computational costs and training times for large language models and high-resolution image/video generators. This enables faster iteration cycles, reduced infrastructure expenses, and the ability to deploy more capable AI models with unprecedented context lengths.
Annual Cost Savings
$0
Hours Reclaimed Annually
0
Your AI Implementation Roadmap
A clear, phased approach to integrating RingX and supercharging your AI infrastructure.
Phase 1: Performance Benchmarking
Identify current bottlenecks and establish baseline performance metrics on existing HPC infrastructure using RingX. Validate numerical consistency.
Phase 2: Integration & Optimization
Integrate RingX into existing Transformer-based models (ViT, GPT) and fine-tune for specific workloads. Leverage pipelining and collective communications.
Phase 3: Large-Scale Deployment
Deploy RingX-enabled models on production HPC clusters, scale to thousands of GPUs, and monitor end-to-end training efficiency for long-context learning.
Ready to Transform Your AI Capabilities?
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