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I'm currently thinking about how to write performant and numercially stable code on very large GPU clusters.
I think any engineering endeavor at sufficient scale is a social problem as it involves >10 people talking to each other. In this case,
we try to establish trust between the hardware (built and maintained by our vendors), the workload (constructed by our framework), and the engineer (our recipe colleagues).
A consequence is that the software we write reflect the communicative and collaborative structures we form amongst ourselves.
Ultimately, it's hard to be didactic as requirements constantly evolve, but a few ideas I've found helpful:
> loose coupling in general
> low-overhead FFI layer for externally tuned kernels
> mapping of logical parallelism to physical device topology
> FIFO queue to schedule computations and communications
I've also worked on a few peripheral topics:
> machine learning compilation and SPMD partitioning
> running neural networks on edge devices
> mobile operating systems (AOSP)
> automation and synthetic content
> long-running/remote/cloud agents
Services
- Artifact Evaluation: MLSys, CGO
- Journal Review: IEEE TPAMI, IEEE TKDE
- Conference Review: NeurIPS, LoG, AISTATS, ICML, ICLR, ACL
A small caveat
- I publish under both Liao Peiyuan and Peiyuan Liao, so I may appear as both (Peiyuan, 2026) and (Liao, 2026).