3 accepted papers
Emerging LLM workloads demand extreme mem- ory agility. However, state-of-the-art inference systems (e.g., vLLM) rely on software-defined paging, which sacrifices the contiguous tensor abstraction. This rigid interface exposes fragmen- tation complexity to developers, imposing a se- vere engineering…
Automating end-to-end GPU kernel generation with Large Language Models (LLMs) faces a critical tension between global performance and exploration efficiency. We present LEGO, a hierarchical framework that resolves this trade-off via a parallel multi-agent search over a recursive AND-OR FusionTree. L…
The rise of GPU-based high-performance computing (HPC) has driven the widespread adoption of parallel programming models such as CUDA. Yet, the inherent complexity of parallel programming creates a demand for the automated sequential-to-parallel approaches. However, data scarcity poses a significant…