Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads
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 communication using two portable runtime controls: shared-memory-driven occupancy shaping for computation kernels and elevated scheduling priority for communication kernels. Our approach regulates computation-kernel residency through per-block shared-memor...
| Authors | Minyu Cui, Miquel Pericas |
|---|---|
| Primary category | cs.DC |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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