The rapid growth in demand for HPC systems has led to a rise in carbon footprint, which requires urgent intervention. In this work, we present a comprehensive analysis of the carbon footprint of highperformance computing (HPC) systems, considering the carbon footprint during both the hardware manufacturing and system operational stages. Our work employs HPC hardware component carbon footprint modeling, regional carbon intensity analysis, and experimental characterization of the system life cycle to highlight the importance of quantifying the carbon footprint of HPC systems.

Serverless computing has grown rapidly as a new cloud computing paradigm that promises ease-of-management, cost-efficiency, and auto-scaling by shipping functions via self-contained virtualized containers. Unfortunately, serverless computing suffers from severe cold-start problems---starting containers incurs non-trivial latency. Full container caching is widely applied to mitigate cold-starts, yet has recently been outperformed by two lines of research: partial container caching and container sharing. However, either partial container caching or container sharing techniques exhibit their drawbacks. Partial container caching effectively deals with burstiness while leaving cold-start mitigation halfway; container sharing reduces cold-starts by enabling containers to serve multiple functions while suffering from excessive memory waste due to over-packed containers. This paper proposes RainbowCake, a layer-wise container pre-warming and keep-alive technique that effectively mitigates cold-starts with sharing awareness at minimal waste of memory. With structured container layers and sharing-aware modeling, RainbowCake is robust and tolerant to invocation bursts. We seize the opportunity of container sharing behind the startup process of standard container techniques. RainbowCake breaks the container startup process of a container into three stages and manages different container layers individually. We develop a sharing-aware algorithm that makes event-driven layer-wise caching decisions in real-time. Experiments on OpenWhisk clusters with real-world workloads show that RainbowCake reduces 68% function startup latency and 77% memory waste compared to state-of-the-art solutions.

This paper presents a solution to the challenge of mitigating carbon emissions from hosting large-scale machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to carbon footprint. We introduce, Clover, a carbon-friendly ML inference service runtime system that balances performance, accuracy, and carbon emissions through mixed-quality models and GPU resource partitioning. Our experimental results demonstrate that Clover is effective in substantially reducing carbon emissions while maintaining high accuracy and meeting service level agreement (SLA) targets.

As research and deployment of AI grows, the computational burden to support and sustain its progress inevitably does too. To train or fine-tune state-of-the-art models in NLP, computer vision, etc., some form of AI hardware acceleration is virtually a requirement. Recent large language models require considerable resources to train and deploy, resulting in significant energy usage, potential carbon emissions, and massive demand for GPUs and other hardware accelerators. However, this surge carries large implications for energy sustainability at the HPC/datacenter level. In this paper, we study the effects of power-capping GPUs at a research supercomputing center on GPU temperature and power draw; we show significant decreases in both temperature and power draw, reducing power consumption and potentially improving hardware life-span, with minimal impact on job performance. To our knowledge, our work is the first to conduct and make available a detailed analysis of the effects of GPU power-capping at the supercomputing scale. We hope our work will inspire HPCs/datacenters to further explore, evaluate, and communicate the impact of power-capping AI hardware accelerators for more sustainable AI.

A large variety of sound sources in the ocean, including biological, geophysical, and man-made, can be simultaneously monitored over instantaneous continental-shelf scale regions via the passive ocean acoustic waveguide remote sensing (POAWRS) technique by employing a large-aperture densely-populated coherent hydrophone array system. Millions of acoustic signals received on the POAWRS system per day can make it challenging to identify individual sound sources. An automated classification system is necessary to enable sound sources to be recognized. Here, the objectives are to (i) gather a large training and test data set of fin whale vocalization and other acoustic signal detections; (ii) build multiple fin whale vocalization classifiers, including a logistic regression, support vector machine (SVM), decision tree, convolutional neural network (CNN), and long short-term memory (LSTM) network; (iii) evaluate and compare performance of these classifiers using multiple metrics including accuracy, precision, recall and F1-score; and (iv) integrate one of the classifiers into the existing POAWRS array and signal processing software. The findings presented here will (1) provide an automatic classifier for near real-time fin whale vocalization detection and recognition, useful in marine mammal monitoring applications; and (2) lay the foundation for building an automatic classifier applied for near real-time detection and recognition of a wide variety of biological, geophysical, and man-made sound sources typically detected by the POAWRS system in the ocean.

Dr. Prachi Deval/Dr. Jyoti Tembhurne/Dr. Arti Gangurde/Dr. Manish Chauhan/Dr. Niraja Jaiswal/Dr. Devesh Kumar Tiwari: Purpose: To evaluate and compare the wear of natural enamel against a metal-ceramic and a monolithic zirconia crown, with the null hypothesis that there is no difference in the wear of enamel between antagonist metal-ceramic and monolithic zirconia crowns. Materials and Methods: In 30 subjects (irrespective of sex and within the age range of 18 to 40 years), two bilaterally opposing molars (maxillary/mandibular) were prepared to receive monolithic zirconia or metal-ceramic crowns with feldspathic porcelain veneer. A polyvinyl siloxane impression of the opposing arch was taken at the time of cementation and 1 year after cementation. Casts were poured in type III gypsum and scanned, and the images were superimposed on each other. AutoCAD was used to calculate the difference between two images, which corresponded to the linear wear of the antagonist teeth. Statistical analysis of the data was done using one-way analysis of variance (ANOVA) and post hoc Tukey honest significant difference test for intergroup comparison. The P value obtained by one-way ANOVA was 1.1102e^-16 (< .05), and by post hoc Tukey test was .001 (< .01). Results: The mean wear of enamel against enamel was 14.8 ? 1.3 μm, enamel against metal-ceramic was 87.1 ? 18.3 μm, and enamel against monolithic zirconia was 59.4 ? 13.6 μm. The P values obtained; ie, 1.1102e^-16 (one-way ANOVA) and 0.001 (post hoc Tukey), indicated that the difference in wear of the antagonist tooth between monolithic zirconia and feldspathic porcelain was significant. Conclusion: It can be concluded that monolithic zirconia causes less wear of the antagonist tooth than feldspathic porcelain.