This paper configured multiple experiments to explore the rules of the GPU kernel-level scheduling.

The authors proposed BRP-NAS, an efficient hardware-aware NAS enabled by an accurate performance (latency and accuracy) predictor based on graph convolutional network (GCN). The BRP-NAS uses the binary relations of models and an iterative data selection strategy to improve the sample selection. In addition, they also released LatBench - a latency dataset of NAS-Bench-201 models running on abroad range of devices.

This paper introduces InferLine, a system for provisioning and managing each stage of prediction pipelines to meet end-to-end tail latency constraints while minimizing cost.

Introduction and learning of Docker containers and images basics and usage

Introduction

This paper won the Best Paper Award at MobiSys 2021. The paper proposes nn-Meter, a model inference time prediction system that can efficiently and accurately predict the inference latency of DNN models on different edge devices. The key idea is to divide the entire model into kernels (execution units on the device), and then perform kernel-level prediction.

Background

As more and more deep neural networks emerge, systems for predicting network inference performance need to be generalizable to adapt.

CODE & VIDEO

Abstract

Habitat is a Python library that can predict performance on GPUs with the help of a GPU that the user already has.

Current Apporaches and their Limitation

The approaches for the DL performance analysis today include

1. Directly measuring the training job on the GPU
2. Using the benchmark. However, there are the limitations of these approaches:
   1. You need to have the GPU in the first place
   2. They are not as helpful in a custom DNN on a specific GPU. Another approach is to use heuristics, which assumes that DNN training workload exhaust all the computational resources on a GPU, which is not true in general.

Observations

Hence, habitat based on the observations: