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AI Research
arXiv CS.AI·Jun 9·5 min read
Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads
TL;DR
As machine learning models grow larger, communication delays between GPUs during training have become a significant bottleneck. A new method was developed that allows computation and communication to occur simultaneously by managing shared memory and prioritizing communication tasks.
✦ Why It Matters
Engineers can implement this method to optimize multi-GPU training and reduce execution times in machine learning tasks.
Key Takeaways
- Utilize shared memory to optimize GPU resource allocation.
- Prioritize communication tasks to enhance throughput.
- Implement concurrent execution strategies in ML workloads.
- Achieve performance gains without modifying existing libraries.
Full Summary
The increasing complexity of machine learning (ML) models necessitates distributed training across multiple Graphics Processing Units (GPUs), but communication delays often hinder performance. To address this, a novel technique was introduced that enables concurrent execution of computation and communication tasks.
This method utilizes shared-memory-driven occupancy shaping to optimize the use of on-chip resources for computation kernels while ensuring that communication kernels receive higher scheduling priority. Experiments conducted on NVIDIA A40, A100, H100, and AMD MI250X GPUs demonstrated that this approach effectively overlaps computation and communication, leading to a reduction in total execution time by as much as 25.5%.
Importantly, this improvement was achieved without requiring modifications to vendor libraries or kernel implementations. These findings suggest that resource-aware strategies can significantly enhance the efficiency of multi-GPU ML workloads.