Mao Yang
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| title | conference | date |
|---|---|---|
| PipeThreader: Software-Defined Pipelining for Efficient DNN Execution | OSDI '25 | 2025-07-07 |
A compiler shifts pipeline scheduling from hardware to software, exposing tile-level tasks and heterogeneous units to generate efficient DNN pipelines. It… | ||
| Ladder: Enabling Efficient Low-Precision Deep Learning Computing through... | 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI '24) | 2024-07-10 |
Ladder is a compiler that represents evolving low‑bit formats as a first‑class tType and exposes four concrete tensor transforms (slice/map/convert/pad) to… | ||
| nnScaler: Constraint-Guided Parallelization Plan Generation for Deep Learning Training | 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI '24) | 2024-07-10 |
A plan-generation framework implements three primitives op-trans, op-assign, op-order plus constraint filters so experts compose custom parallelization spaces… | ||
| LitePred: Transferable and Scalable Latency Prediction for Hardware-Aware Neural... | 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI '24) | 2024-04-16 |
LitePred claims it predicts DNN inference latency on new edge platforms by transferring existing predictors rather than rebuilding them. It uses a VAE data… | ||
| VBASE: Unifying Online Vector Similarity Search and Relational Queries via Relaxed... | OSDI 2023 | 2023-07-10 |
VBase identifies a property called relaxed monotonicity that lets approximate nearest-neighbor indices serve as termination checks for SQL-style queries… | ||
| Cocktailer: Analyzing and Optimizing Dynamic Control Flow in Deep Learning | OSDI 2023 | 2023-07-10 |
OSDI '23 - Cocktailer: Analyzing and Optimizing Dynamic Control Flow in Deep Learning
Chen Zhang, Tsinghua University, Lingxiao Ma, Microsoft Research, Jilong… | ||
| On Modular Learning of Distributed Systems for Predicting End-to-End Latency | NSDI 2023 | 2023-04-17 |
Fluxion builds a modular performance model by packaging per-service ML models into learning assignments and wiring them into an inference graph so changes only… | ||
| AutoSys: The Design and Operation of Learning-Augmented Systems | USENIX ATC '20 | 2020-07-15 |
AutoSys is a unified framework that runs Hyperband, TPE, SMAC tuners and trial benchmarks to auto-tune five Web Search engines (selection, ranking, KV stores)… | ||
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