Search Results for author:
Found 6 papers, 4 papers with code
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers
We address these challenges with Adaptive SGD, an adaptive elastic model averaging stochastic gradient descent algorithm for heterogeneous multi-GPUs that is characterized by dynamic scheduling, adaptive batch size scaling, and normalized model merging.
Online Sketch-based Query Optimization
In COMPASS, query optimization and execution are intertwined.
Databases Data Structures and Algorithms H.2.4
DJEnsemble: On the Selection of a Disjoint Ensemble of Deep Learning Black-Box Spatio-Temporal Models
In the online part, we compute a DJEnsemble plan which minimizes a multivariate cost function based on estimates for the prediction error and the execution cost -- producing a model spatial allocation matrix -- and run the optimal ensemble plan.
Heterogeneous CPU+GPU Stochastic Gradient Descent Algorithms
In order to allow for a principled exploration of the design space, we first introduce a generic deep learning framework that exploits the difference in computational power and memory hierarchy between CPU and GPU through asynchronous message passing.
Progressive Data Science: Potential and Challenges
We discuss these challenges and outline first steps towards progressiveness, which, we argue, will ultimately help to significantly speed-up the overall data science process.
Stochastic Gradient Descent on Highly-Parallel Architectures
The choice between synchronous GPU and asynchronous CPU depends on the task and the characteristics of the data.
