QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers

Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits that are also error prone. Quilt is a framework for performing multi-class classification task designed to work effectively on current error-prone quantum computers. Quilt is evaluated with real quantum machines as well as with projected noise levels as quantum machines become more noise-free. Quilt demonstrates up to 85% multi-class classification accuracy with the MNIST dataset on a five-qubit system

Measuring and Managing Answer Quality for Online Data-Intensive Services

Online data-intensive services parallelize query execution across distributed software components. Interactive response time is a priority, so online query executions return answers without waiting for slow running components to finish. However, data from these slow components could lead to better answers. We propose Ubora, an approach to measure the effect of slow running components on the quality of answers. Ubora randomly samples online queries and executes them twice. The first execution elides data from slow components and provides fast online answers; the second execution waits for all components to complete. Ubora uses memoization to speed up mature executions by replaying network messages exchanged between components. Our systems-level implementation works for a wide range of platforms, including Hadoop/Yarn, Apache Lucene, the EasyRec Recommendation Engine, and the OpenEphyra question answering system. Ubora computes answer quality much faster than competing approaches that do not use memoization. With Ubora, we show that answer quality can and should be used to guide online admission control. Our adaptive controller processed 37% more queries than a competing controller guided by the rate of timeouts.Comment: Technical Repor

Green Carbon Footprint for Model Inference Serving via Exploiting Mixed-Quality Models and GPU Partitioning

This paper presents a solution to the challenge of mitigating carbon emissions from large-scale high performance computing (HPC) systems and datacenters that host machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to datacenter compute cycles and carbon emissions. 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. Therefore, it is a promising solution toward achieving carbon neutrality in HPC systems and datacenters

MISO: Exploiting Multi-Instance GPU Capability on Multi-Tenant Systems for Machine Learning

GPU technology has been improving at an expedited pace in terms of size and performance, empowering HPC and AI/ML researchers to advance the scientific discovery process. However, this also leads to inefficient resource usage, as most GPU workloads, including complicated AI/ML models, are not able to utilize the GPU resources to their fullest extent -- encouraging support for GPU multi-tenancy. We propose MISO, a technique to exploit the Multi-Instance GPU (MIG) capability on the latest NVIDIA datacenter GPUs (e.g., A100, H100) to dynamically partition GPU resources among co-located jobs. MISO's key insight is to use the lightweight, more flexible Multi-Process Service (MPS) capability to predict the best MIG partition allocation for different jobs, without incurring the overhead of implementing them during exploration. Due to its ability to utilize GPU resources more efficiently, MISO achieves 49% and 16% lower average job completion time than the unpartitioned and optimal static GPU partition schemes, respectively

MosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computers

Quantum machine learning and vision have come to the fore recently, with hardware advances enabling rapid advancement in the capabilities of quantum machines. Recently, quantum image generation has been explored with many potential advantages over non-quantum techniques; however, previous techniques have suffered from poor quality and robustness. To address these problems, we introduce, MosaiQ, a high-quality quantum image generation GAN framework that can be executed on today's Near-term Intermediate Scale Quantum (NISQ) computers.Comment: Accepted to appear at ICCV'2

Is yoga an effective modality of stress reduction within medical population; a qualitative study within MBBS students of BRD medical college, Gorakhpur

Background: Stress is very common in medical professionals. Stress begins in the first year of medical school and increases with subsequent years of medical life. Stress decreases overall performance and had a multitude of health-related adverse effect. Yoga has been tried as a stress reduction technique in different populations. In present study yoga was performed in the 1st year MBBS students and impact on stress reduction was studied using PSS-10 stress scale.Methods: Study groups, yoga and control contained 26 and 27 subjects respectively. The yoga group practiced selected yogic asana, pranayama, and yoga nidra 1hour daily 6days a week for 3months. Control group kept in touch and allowed their usual activity as before. The PSS-10 scale used to measure the level of stress in both groups pre and post study.Results: There was a highly significant reduction in the PSS-10 Score (stress level) in the yoga group (P Value <0.0001) but there was no significant change in the PSS-10 Score of control group (P Value = 0.2930).Conclusions: Yoga is an effective modality of stress reduction technique in 1st year medical students. Therefore, yoga should be introduced as a part of the curricula in the first year of medical school. This may be taken as the 1st step in implantation of healthy lifestyle in future health care providers

Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models

The energy requirements of current natural language processing models continue to grow at a rapid, unsustainable pace. Recent works highlighting this problem conclude there is an urgent need for methods that reduce the energy needs of NLP and machine learning more broadly. In this article, we investigate techniques that can be used to reduce the energy consumption of common NLP applications. In particular, we focus on techniques to measure energy usage and different hardware and datacenter-oriented settings that can be tuned to reduce energy consumption for training and inference for language models. We characterize the impact of these settings on metrics such as computational performance and energy consumption through experiments conducted on a high performance computing system as well as popular cloud computing platforms. These techniques can lead to significant reduction in energy consumption when training language models or their use for inference. For example, power-capping, which limits the maximum power a GPU can consume, can enable a 15\% decrease in energy usage with marginal increase in overall computation time when training a transformer-based language model

Sustainable HPC: Modeling, Characterization, and Implications of Carbon Footprint in Modern HPC Systems

The rapid growth in demand for HPC systems has led to a rise in energy consumption and carbon emissions, which requires urgent intervention. In this work, we present a comprehensive framework for analyzing the carbon footprint of high-performance computing (HPC) systems, considering the carbon footprint during both the hardware production 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 an HPC system holistically

Idling emission at intersection and exploring suitable mitigation measures

Variety of road based transport modes catering to the transport demand ply in large number on the road system of urban India. As a result, the traffic and transportation problems are aggravating day by day. These problems manifest in the form of increased traffic congestion, increased air and noise pollution, accidents, delays etc. The consumption of fuel is on the increase due to enhanced trip lengths, shift of modal share towards personalized modes of travel and at signalized intersections due to idling of vehicles during stoppage phases. The results of a study conducted by CRRI in 2005 estimated that fuel worth of Indian Rupees. 1, 0000 Million is wasted every year in Delhi by vehicles idling at 600 signalized traffic signals and was around 15% of the total fuel consumed annually in Delhi. In addition, the time loss to the commuters is also associated with the delays. The running engines during idling also generate emissions, which are harmful both for human health and ecology. One of the ways to conserve fuels is to minimize its wastage. It is necessary to understand the amount of fuel loss and emissions generated at signalized intersections in the country. By applying various engineering / management measures the fuel wastage and associated emissions can be reduced. Presently there is no model that can give the amount of fuel loss and emissions in relation to the type of vehicle and delays. In this paper a review of the study for exploring suitable mitigation measures to improve the signalized intersections are presented