LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft

GREAT: the SOFIA high-frequency heterodyne instrument

We describe the design and construction of GREAT, the German REceiver for Astronomy at Terahertz frequencies operated on the Stratospheric Observatory for Infrared Astronomy (SOFIA). GREAT is a modular dual-color heterodyne instrument for highresolution far-infrared (FIR) spectroscopy. Selected for SOFIA's Early Science demonstration, the instrument has successfully performed three Short and more than a dozen Basic Science flights since first light was recorded on its April 1, 2011 commissioning flight. We report on the in-flight performance and operation of the receiver that - in various flight configurations, with three different detector channels - observed in several science-defined frequency windows between 1.25 and 2.5 THz. The receiver optics was verified to be diffraction-limited as designed, with nominal efficiencies; receiver sensitivities are state-of-the-art, with excellent system stability. The modular design allows for the continuous integration of latest technologies; we briefly discuss additional channels under development and ongoing improvements for Cycle 1 observations. GREAT is a principal investigator instrument, developed by a consortium of four German research institutes, available to the SOFIA users on a collaborative basis

A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

Unsupervised Anomaly-based Malware Detection using Hardware Features

Recent works have shown promise in using microarchitectural execution patterns to detect malware programs. These detectors belong to a class of detectors known as signature-based detectors as they catch malware by comparing a program's execution pattern (signature) to execution patterns of known malware programs. In this work, we propose a new class of detectors - anomaly-based hardware malware detectors - that do not require signatures for malware detection, and thus can catch a wider range of malware including potentially novel ones. We use unsupervised machine learning to build profiles of normal program execution based on data from performance counters, and use these profiles to detect significant deviations in program behavior that occur as a result of malware exploitation. We show that real-world exploitation of popular programs such as IE and Adobe PDF Reader on a Windows/x86 platform can be detected with nearly perfect certainty. We also examine the limits and challenges in implementing this approach in face of a sophisticated adversary attempting to evade anomaly-based detection. The proposed detector is complementary to previously proposed signature-based detectors and can be used together to improve security.Comment: 1 page, Latex; added description for feature selection in Section 4, results unchange

Flow detectors having mechanical oscillators, and use thereof in flow characterization systems

An improved system (100), resonator flow detector (102) and method for characterizing a fluid sample that includes o injecting a fluid sample into a mobile phase of a flow characterization system (106), and detecting a property of the fluid sample > or of a component thereof with a flow detector (102) comprising a mechanical resonator (120), preferably one that is operated at a frequency less than about 1 MHz, such as tuning fork resonator