On local Fourier analysis of multigrid methods for PDEs with jumping and random coefficients

In this paper, we propose a novel non-standard Local Fourier Analysis (LFA) variant for accurately predicting the multigrid convergence of problems with random and jumping coefficients. This LFA method is based on a specific basis of the Fourier space rather than the commonly used Fourier modes. To show the utility of this analysis, we consider, as an example, a simple cell-centered multigrid method for solving a steady-state single phase flow problem in a random porous medium. We successfully demonstrate the prediction capability of the proposed LFA using a number of challenging benchmark problems. The information provided by this analysis helps us to estimate a-priori the time needed for solving certain uncertainty quantification problems by means of a multigrid multilevel Monte Carlo method

Architectural Techniques to Enable Reliable and Scalable Memory Systems

High capacity and scalable memory systems play a vital role in enabling our desktops, smartphones, and pervasive technologies like Internet of Things (IoT). Unfortunately, memory systems are becoming increasingly prone to faults. This is because we rely on technology scaling to improve memory density, and at small feature sizes, memory cells tend to break easily. Today, memory reliability is seen as the key impediment towards using high-density devices, adopting new technologies, and even building the next Exascale supercomputer. To ensure even a bare-minimum level of reliability, present-day solutions tend to have high performance, power and area overheads. Ideally, we would like memory systems to remain robust, scalable, and implementable while keeping the overheads to a minimum. This dissertation describes how simple cross-layer architectural techniques can provide orders of magnitude higher reliability and enable seamless scalability for memory systems while incurring negligible overheads.Comment: PhD thesis, Georgia Institute of Technology (May 2017

Efficient Striping Techniques for Variable Bit Rate Continuous Media File Servers

The performance of striped disk arrays is governed by two parameters: the stripe unit size and the degree of striping. In this paper, we describe techniques for determining the stripe unit size and degree of striping for disk arrays storing variable bit rate continuous media data. We present an analytical model that uses the server configuration and the workload characteristics to predict the load on the most heavily loaded disk in redundant and non-redundant arrays. We then use the model to determine the optimal stripe unit size for different workloads. We also use the model to study the effect of various system parameters on the optimal stripe unit size. To determine the degree of striping, we first demonstrate that striping a continuous media stream across all disks in the array causes the number of clients supported to increase sub-linearly with increase in the number of disks. To maximize the number of clients supported in large arrays, we propose a technique that partitions a disk array and stripes each media stream across a single partition. Since load imbalance can occur in such partitioned arrays, we present an analytical model to compute the imbalance across partitions in the array. We then use the model to determine a partition size that minimizes the load imbalance, and hence, maximizes the number of clients supported by the array

The Dirty Secret of SSDs: Embodied Carbon

Scalable Solid-State Drives (SSDs) have revolutionized the way we store and access our data across datacenters and handheld devices. Unfortunately, scaling technology can have a significant environmental impact. Across the globe, most semiconductor manufacturing use electricity that is generated from coal and natural gas. For instance, manufacturing a Gigabyte of Flash emits 0.16 Kg CO$_2$ and is a significant fraction of the total carbon emission in the system. We estimate that manufacturing storage devices has resulted in 20 million metric tonnes of CO$_2$ emissions in 2021 alone. To better understand this concern, this paper compares the sustainability trade-offs between Hard Disk Drives (HDDs) and SSDs and recommends methodologies to estimate the embodied carbon costs of the storage system. In this paper, we outline four possible strategies to make storage systems sustainable. First, this paper recommends directions that help select the right medium of storage (SSD vs HDD). Second, this paper proposes lifetime extension techniques for SSDs. Third, this paper advocates for effective and efficient recycling and reuse of high-density multi-level cell-based SSDs. Fourth, specifically for hand-held devices, this paper recommends leveraging elasticity in cloud storage.Comment: In the proceedings of the 1st Workshop on Sustainable Computer Systems Design and Implementation (HotCarbon 2022

Cross-Lingual Classification of Crisis Data

Many citizens nowadays flock to social media during crises to share or acquire the latest information about the event. Due to the sheer volume of data typically circulated during such events, it is necessary to be able to efficiently filter out irrelevant posts, thus focusing attention on the posts that are truly relevant to the crisis. Current methods for classifying the relevance of posts to a crisis or set of crises typically struggle to deal with posts in different languages, and it is not viable during rapidly evolving crisis situations to train new models for each language. In this paper we test statistical and semantic classification approaches on cross-lingual datasets from 30 crisis events, consisting of posts written mainly in English, Spanish, and Italian. We experiment with scenarios where the model is trained on one language and tested on another, and where the data is translated to a single language. We show that the addition of semantic features extracted from external knowledge bases improve accuracy over a purely statistical model