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Found 10 papers, 3 papers with code
CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs
Considering the large space of DNN models and devices that impede direct profiling of all combinations, recent efforts focus on building a predictor to model the performance of DNN models on different devices.
ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs
In this paper, we present ByteTransformer, a high-performance transformer boosted for variable-length inputs.
ByteComp: Revisiting Gradient Compression in Distributed Training
It first designs a decision tree abstraction to express all the compression strategies and develops empirical models to timeline tensor computation, communication, and compression to enable ByteComp to derive the intricate interactions among tensors.
dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training
Distributed training using multiple devices (e. g., GPUs) has been widely adopted for learning DNN models over large datasets.
Aryl: An Elastic Cluster Scheduler for Deep Learning
We introduce Aryl, a new cluster scheduler to address these problems.
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
Extensive experiments on various GNN models and large graph datasets show that BGL significantly outperforms existing GNN training systems by 20. 68x on average.
Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance
Today's auto-tuners (e. g., AutoTVM, Ansor) generate efficient tensor programs by navigating a large search space to identify effective implementations, but they do so with opaque hardware details.
Serving DNN Models with Multi-Instance GPUs: A Case of the Reconfigurable Machine Scheduling Problem
With MIG, A100 can be the most cost-efficient GPU ever for serving Deep Neural Networks (DNNs).
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
This mutual-training process between BO and the loss-prediction model allows us to limit the training steps invested in the BO search.
High performance integrated graphene electro-optic modulator at cryogenic temperature
High performance integrated electro-optic modulators operating at low temperature are critical for optical interconnects in cryogenic applications.
Applied Physics Optics
