DynaFlow: Transparent and Flexible Intra-Device Parallelism via Programmable Operator Scheduling
Intra-device parallelism addresses resource under-utilization in ML inference and training by overlapping the execution of operators with different resource usage. However, its wide adoption is hindered by a fundamental conflict with the static, sequential programming model of existing frameworks. Integrating these strategies requires invasive, model-specific code overhauls, representing an intractable engineering cost. This is further amplified by the high sensitivity of strategies to execution contexts (e.g., workload, model architecture, hardware), forcing developers to implement and maintain multiple specialized solutions. To address this, we propose DynaFlow, a framework that enables...
| Authors | Yi Pan, Yile Gu, Jinbin Luo et al. |
|---|---|
| Primary category | cs.DC |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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