Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads

📰 ArXiv cs.AI

Optimize multi-GPU ML workloads by overlapping computation and communication to reduce bottlenecks

advanced Published 9 Jun 2026

Action Steps

Identify communication bottlenecks in multi-GPU ML workloads using tools like NVLink or NCCL
Overlap computation and collective communication using techniques like pipelining or parallelization
Configure multi-GPU systems to optimize resource allocation and minimize overhead
Test and evaluate the performance of overlapped computation and communication using benchmarks like HPL-AI or MLPerf
Apply resource-aware optimization strategies to further improve training efficiency

Who Needs to Know This

ML engineers and researchers working on large-scale distributed training can benefit from this technique to improve training efficiency

Key Insight

💡 Overlapping computation and communication can significantly reduce bottlenecks in multi-GPU ML workloads

Key Takeaways

Optimize multi-GPU ML workloads by overlapping computation and communication to reduce bottlenecks

Full Article

Title: Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads

Abstract:
arXiv:2606.09200v1 Announce Type: cross Abstract: The rapid growth of large-scale machine learning (ML) has made distributed training across multiple GPUs a fundamental component of modern ML systems. As model sizes and computational throughput continue to increase, communication overhead has become a dominant bottleneck in multi-GPU training, particularly when computation and communication are executed sequentially. This work explores concurrent execution of computation and collective communica

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