GPUs as Storage System Accelerators

Massively multicore processors, such as Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems and to explore new ways to engineer them to recalibrate the cost-to-performance relation. This project explores the feasibility of harnessing GPUs' computational power to improve the performance, reliability, or security of distributed storage systems. In this context, we present the design of a storage system prototype that uses GPU offloading to accelerate a number of computationally intensive primitives based on hashing, and introduce techniques to efficiently leverage the processing power of GPUs. We evaluate the performance of this prototype under two configurations: as a content addressable storage system that facilitates online similarity detection between successive versions of the same file and as a traditional system that uses hashing to preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing applications' performance. Our results show that this technique can bring tangible performance gains without negatively impacting the performance of concurrently running applications.Comment: IEEE Transactions on Parallel and Distributed Systems, 201

PerfWeb: How to Violate Web Privacy with Hardware Performance Events

The browser history reveals highly sensitive information about users, such as financial status, health conditions, or political views. Private browsing modes and anonymity networks are consequently important tools to preserve the privacy not only of regular users but in particular of whistleblowers and dissidents. Yet, in this work we show how a malicious application can infer opened websites from Google Chrome in Incognito mode and from Tor Browser by exploiting hardware performance events (HPEs). In particular, we analyze the browsers' microarchitectural footprint with the help of advanced Machine Learning techniques: k-th Nearest Neighbors, Decision Trees, Support Vector Machines, and in contrast to previous literature also Convolutional Neural Networks. We profile 40 different websites, 30 of the top Alexa sites and 10 whistleblowing portals, on two machines featuring an Intel and an ARM processor. By monitoring retired instructions, cache accesses, and bus cycles for at most 5 seconds, we manage to classify the selected websites with a success rate of up to 86.3%. The results show that hardware performance events can clearly undermine the privacy of web users. We therefore propose mitigation strategies that impede our attacks and still allow legitimate use of HPEs

Towards a Distributed Quantum Computing Ecosystem

The Quantum Internet, by enabling quantum communications among remote quantum nodes, is a network capable of supporting functionalities with no direct counterpart in the classical world. Indeed, with the network and communications functionalities provided by the Quantum Internet, remote quantum devices can communicate and cooperate for solving challenging computational tasks by adopting a distributed computing approach. The aim of this paper is to provide the reader with an overview about the main challenges and open problems arising with the design of a Distributed Quantum Computing ecosystem. For this, we provide a survey, following a bottom-up approach, from a communications engineering perspective. We start by introducing the Quantum Internet as the fundamental underlying infrastructure of the Distributed Quantum Computing ecosystem. Then we go further, by elaborating on a high-level system abstraction of the Distributed Quantum Computing ecosystem. Such an abstraction is described through a set of logical layers. Thereby, we clarify dependencies among the aforementioned layers and, at the same time, a road-map emerges

CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates

A guide to the ATM and debit card industry - 2006 update

It has been three years since we published A Guide to the ATM and Debit Card Industry. Those three years represent a very dynamic time in the industry with a number of important developments. Some trends and patterns have persisted or accelerated, while others have peaked or reversed. Still others have emerged for the first time. The purpose of this 2006 Update is to document these trends and patterns by updating the data we presented in the original book and to discuss their implications for the current and future state of the industry. ; The most important development is that the two segments of the industry, ATM and debit, are in some sense going in opposite directions. The ATM industry has matured and is relatively stagnant, with major players jockeying for position, searching for and adopting different business strategies, and adjusting to the maturation of the industry. The debit card industry, in contrast, is expanding rapidly, with new players, new partnerships, new products, and new markets. The challenge in the debit card industry is not how to cope with a maturing industry but, rather, how to preserve and enhance position and not be left behind. ; In the first part of this Update, we highlight and discuss some of the most important changes in the ATM and debit card industry. For both the ATM and debit sides of the industry, we recap and analyze changes in activity levels, industry structure, and industry pricing. We then offer some thoughts on what might lie ahead, including a discussion of fraud and data security. In the second part of this Update, we present updated versions of the 23 charts and 11 tables from the original book, adding the three or four years of additional data that have since become available.Automated tellers ; Debit cards ; Point-of-sale-systems ; Payment systems