Efficient model performance estimation via feature histories
An important step in the task of neural network design, such as hyper-parameter
optimization (HPO) or neural architecture search (NAS), is the evaluation of a candidate …
optimization (HPO) or neural architecture search (NAS), is the evaluation of a candidate …
Learning to sample: Exploiting similarities across environments to learn performance models for configurable systems
Most software systems provide options that allow users to tailor the system in terms of
functionality and qualities. The increased flexibility raises challenges for understanding the …
functionality and qualities. The increased flexibility raises challenges for understanding the …
Using bad learners to find good configurations
Finding the optimally performing configuration of a software system for a given setting is
often challenging. Recent approaches address this challenge by learning performance …
often challenging. Recent approaches address this challenge by learning performance …
Resource-guided configuration space reduction for deep learning models
Deep learning models, like traditional software systems, provide a large number of
configuration options. A deep learning model can be configured with different …
configuration options. A deep learning model can be configured with different …
Transfer learning with bellwethers to find good configurations
As software systems grow in complexity, the space of possible configurations grows
exponentially. Within this increasing complexity, developers, maintainers, and users cannot …
exponentially. Within this increasing complexity, developers, maintainers, and users cannot …
Reducing the tail latency of microservices applications via optimal configuration tuning
The microservice architecture is an architectural style for designing applications that
supports a collection of fine-grained and loosely-coupled services, called microservices …
supports a collection of fine-grained and loosely-coupled services, called microservices …
Evaluating machine learning models for disparate computer systems performance prediction
A Mankodi, A Bhatt, B Chaudhury… - 2020 IEEE …, 2020 - ieeexplore.ieee.org
Performance prediction is an active area of research due to its applicability in the
advancements of hardware-software co-development. Several empirical machine-learning …
advancements of hardware-software co-development. Several empirical machine-learning …
Predicting computer performance based on hardware configuration using multiple neural networks
L Lopez, M Guynn, M Lu - 2018 17th IEEE International …, 2018 - ieeexplore.ieee.org
How can we accurately predict the performance of a Personal Computer (PC) configuration
without time consuming simulation? In this work, we predict the performance of a computer …
without time consuming simulation? In this work, we predict the performance of a computer …
[PDF][PDF] Bayesian learning for hardware and software configuration co-optimization
Both hardware and software systems are increasingly configurable, which poses a
challenge to finding the highest performance configuration due to the tremendous search …
challenge to finding the highest performance configuration due to the tremendous search …
On The Fairness Impacts of Hardware Selection in Machine Learning
In the machine learning ecosystem, hardware selection is often regarded as a mere utility,
overshadowed by the spotlight on algorithms and data. This is especially relevant in …
overshadowed by the spotlight on algorithms and data. This is especially relevant in …