27 citations · 31 across the 3 of their papers we have counts for
5 papers
Towards Scalable Dataframe Systems
Devin Petersohn, Stephen Macke, Doris Xin +7
Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in Rand Python, dataframes face performance issu…
Pay Attention to Convolution Filters: Towards Fast and Accurate Fine-Grained Transfer Learning
Xiangxi Mo, Ruizhe Cheng, Tianyi Fang
We propose an efficient transfer learning method for adapting ImageNet pre-trained Convolutional Neural Network (CNN) to fine-grained image classification task. Conventional transf…
The OoO VLIW JIT Compiler for GPU Inference
Paras Jain, Xiangxi Mo, Ajay Jain +3
Current trends in Machine Learning~(ML) inference on hardware accelerated devices (e.g., GPUs, TPUs) point to alarmingly low utilization. As ML inference is increasingly time-bound…
Dynamic Space-Time Scheduling for GPU Inference
Paras Jain, Xiangxi Mo, Ajay Jain +5
Serving deep neural networks in latency critical interactive settings often requires GPU acceleration. However, the small batch sizes typical in online inference results in poor GP…
InferLine: ML Prediction Pipeline Provisioning and Management for Tight Latency Objectives
Daniel Crankshaw, Gur-Eyal Sela, Corey Zumar +4
Serving ML prediction pipelines spanning multiple models and hardware accelerators is a key challenge in production machine learning. Optimally configuring these pipelines to meet…