8 citations · 15 across the 5 of their papers we have counts for
5 papers
CAMEO: A Causal Transfer Learning Approach for Performance Optimization of Configurable Computer Systems
Md Shahriar Iqbal, Ziyuan Zhong, Iftakhar Ahmad +2
Modern computer systems are highly configurable, with hundreds of configuration options that interact, resulting in an enormous configuration space. As a result, optimizing perform…
Unicorn: Reasoning about Configurable System Performance through the lens of Causality
Md Shahriar Iqbal, Rahul Krishna, Mohammad Ali Javidian +2
Modern computer systems are highly configurable, with the total variability space sometimes larger than the number of atoms in the universe. Understanding and reasoning about the p…
CADET: Debugging and Fixing Misconfigurations using Counterfactual Reasoning
Rahul Krishna, Md Shahriar Iqbal, Mohammad Ali Javidian +2
Modern computing platforms are highly-configurable with thousands of interacting configurations. However, configuring these systems is challenging. Erroneous configurations can cau…
FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks
Md Shahriar Iqbal, Jianhai Su, Lars Kotthoff +1
The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs). Typica…
Transfer Learning for Performance Modeling of Deep Neural Network Systems
Md Shahriar Iqbal, Lars Kotthoff, Pooyan Jamshidi
Modern deep neural network (DNN) systems are highly configurable with large a number of options that significantly affect their non-functional behavior, for example inference time…