4,020 research outputs found
Organizing sustainable development
The role and meaning of sustainable development have been recognized in the scientific literature for decades. However, there has recently been a dynamic increase in interest in the subject, which results in numerous, in-depth scientific research and publications with an interdisciplinary dimension. This edited volume is a compendium of theoretical knowledge on sustainable development. The context analysed in the publication includes a multi-level and multi-aspect analysis starting from the historical and legal conditions, through elements of the macro level and the micro level, inside the organization. Organizing Sustainable Development offers a systematic and comprehensive theoretical analysis of sustainable development supplemented with practical examples, which will allow obtaining comprehensive knowledge about the meaning and its multi-context application in practice. It shows the latest state of knowledge on the topic and will be of interest to students at an advanced level, academics and reflective practitioners in the fields of sustainable development, management studies, organizational studies and corporate social responsibility
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
DRLCap: Runtime GPU Frequency Capping with Deep Reinforcement Learning
Power and energy consumption is the limiting factor of modern computing systems. As the GPU becomes a mainstream computing device, power management for GPUs becomes increasingly important. Current works focus on GPU kernel-level power management, with challenges in portability due to architecture-specific considerations. We present DRLCap , a general runtime power management framework intended to support power management across various GPU architectures. It periodically monitors system-level information to dynamically detect program phase changes and model the workload and GPU system behavior. This elimination from kernel-specific constraints enhances adaptability and responsiveness. The framework leverages dynamic GPU frequency capping, which is the most widely used power knob, to control the power consumption. DRLCap employs deep reinforcement learning (DRL) to adapt to the changing of program phases by automatically adjusting its power policy through online learning, aiming to reduce the GPU power consumption without significantly compromising the application performance. We evaluate DRLCap on three NVIDIA and one AMD GPU architectures. Experimental results show that DRLCap improves prior GPU power optimization strategies by a large margin. On average, it reduces the GPU energy consumption by 22% with less than 3% performance slowdown on NVIDIA GPUs. This translates to a 20% improvement in the energy efficiency measured by the energy-delay product (EDP) over the NVIDIA default GPU power management strategy. For the AMD GPU architecture, DRLCap saves energy consumption by 10%, on average, with a 4% percentage loss, and improves energy efficiency by 8%
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics
We study a game between autobidding algorithms that compete in an online
advertising platform. Each autobidder is tasked with maximizing its
advertiser's total value over multiple rounds of a repeated auction, subject to
budget and/or return-on-investment constraints. We propose a gradient-based
learning algorithm that is guaranteed to satisfy all constraints and achieves
vanishing individual regret. Our algorithm uses only bandit feedback and can be
used with the first- or second-price auction, as well as with any
"intermediate" auction format. Our main result is that when these autobidders
play against each other, the resulting expected liquid welfare over all rounds
is at least half of the expected optimal liquid welfare achieved by any
allocation. This holds whether or not the bidding dynamics converges to an
equilibrium and regardless of the correlation structure between advertiser
valuations
- …