{"total":15,"rows":[{"id":"2204.09656","title":"A Fast Post-Training Pruning Framework for Transformers","created":"2022-03-29","cited_by_count":162,"area":null},{"id":"2203.12533","title":"Pathways: Asynchronous Distributed Dataflow for ML","created":"2022-03-23","cited_by_count":82,"area":null},{"id":"2109.01611","title":"Multi-model Machine Learning Inference Serving with GPU Spatial Partitioning","created":"2021-09-01","cited_by_count":4,"area":null},{"id":"2301.00391","title":"PiPAD: Pipelined and Parallel Dynamic GNN Training on GPUs","created":"2023-01-01","cited_by_count":4,"area":null},{"id":"2110.00459","title":"Characterizing Concurrency Mechanisms for NVIDIA GPUs under Deep Learning Workloads","created":"2021-10-01","cited_by_count":2,"area":null},{"id":"2301.08695","title":"Baechi: Fast Device Placement of Machine Learning Graphs","created":"2023-01-20","cited_by_count":2,"area":null},{"id":"2201.09210","title":"Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs","created":"2022-01-23","cited_by_count":1,"area":null},{"id":"2202.12417","title":"Optimal channel selection with discrete QCQP","created":"2022-02-24","cited_by_count":1,"area":null},{"id":"2302.03851","title":"ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines","created":"2023-02-08","cited_by_count":1,"area":null},{"id":"2309.13077","title":"A Differentiable Framework for End-to-End Learning of Hybrid Structured Compression","created":"2023-09-21","cited_by_count":1,"area":null},{"id":"2110.00929","title":"Scheduling Optimization Techniques for Neural Network Training","created":"2021-10-03","cited_by_count":0,"area":null},{"id":"2312.10351","title":"Opara: Exploiting Operator Parallelism for Expediting DNN Inference on GPUs","created":"2023-12-16","cited_by_count":0,"area":null},{"id":"2402.17652","title":"Compass: A Decentralized Scheduler for Latency-Sensitive ML Workflows","created":"2024-02-27","cited_by_count":0,"area":null},{"id":"2607.07862","title":"CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2609.35115","title":"DuplexCadence: Exact State and Execution from a Speech Model's Declared Timelines","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}