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

High-level services for networks-on-chip

Future technology trends envision that next-generation Multiprocessors Systems-on- Chip (MPSoCs) will be composed of a combination of a large number of processing and storage elements interconnected by complex communication architectures. Communication and interconnection between these basic blocks play a role of crucial importance when the number of these elements increases. Enabling reliable communication channels between cores becomes therefore a challenge for system designers. Networks-on-Chip (NoCs) appeared as a strategy for connecting and managing the communication between several design elements and IP blocks, as required in complex Systems-on-Chip (SoCs). The topic can be considered as a multidisciplinary synthesis of multiprocessing, parallel computing, networking, and on- chip communication domains. Networks-on-Chip, in addition to standard communication services, can be employed for providing support for the implementation of system-level services. This dissertation will demonstrate how high-level services can be added to an MPSoC platform by embedding appropriate hardware/software support in the network interfaces (NIs) of the NoC. In this dissertation, the implementation of innovative modules acting in parallel with protocol translation and data transmission in NIs is proposed and evaluated. The modules can support the execution of the high-level services in the NoC at a relatively low cost in terms of area and energy consumption. Three types of services will be addressed and discussed: security, monitoring, and fault tolerance. With respect to the security aspect, this dissertation will discuss the implementation of an innovative data protection mechanism for detecting and preventing illegal accesses to protected memory blocks and/or memory mapped peripherals. The second aspect will be addressed by proposing the implementation of a monitoring system based on programmable multipurpose monitoring probes aimed at detecting NoC internal events and run-time characteristics. As last topic, new architectural solutions for the design of fault tolerant network interfaces will be presented and discussed

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

Smoking behaviour, involuntary smoking, attitudes towards smoke-free legislations, and tobacco control activities in the European Union

The six most important cost-effective policies on tobacco control can be measured by the Tobacco Control Scale (TCS). The objective of our study was to describe the correlation between the TCS and smoking prevalence, self-reported exposure to secondhand smoke (SHS) and attitudes towards smoking restrictions in the 27 countries of the European Union (EU27)

Biological effect monitoring in peripheral blood lymphocytes from subjects occupationally exposed to antineoplastic drugs: assessment of micronuclei frequency.

Biological Effect Monitoring in Peripheral Blood Lymphocytes from Subjects Occupationally Exposed to Antineoplastic Drugs: Assessment of Micronuclei Frequency: Milena VILLARINI, et al. Department of Medical‐Surgical Specialties and Public Health (Section of Public Health), University of Perugia, Italy—ObjectivesAntineoplastic drugs (ANPDs) are widely used in the treatment of cancer and some nonneoplastic diseases. However, most if not all of these chemical agents are generally nonselective and, along with tumor cells, normal cells may undergo cytotoxic/genotoxic damage. Italian pharmacists and nurses occupationally exposed to ANPDs during their normal work routines were monitored to evaluate biological effects (i.e., cytogenetic damage) eventually associated with exposure. The subjects were also monitored for primary, oxidative and excision repaired DNA damage as evaluated by comet assay (published data). In the present paper, we present the results obtained with the cytokinesis‐block micronucleus (CBMN) test.MethodsThe CBMN test in peripheral blood lymphocytes was applied because of its ability to detect both clastogenic and aneugenic effects, and because it has recently been reported that micronuclei (MNs) are predictive of cancer risk in human populations. In this study, the evaluation of MN frequency was carried out using the CBMN test in the absence or in the presence of the DNA repair inhibitor Ara‐C (cytosine arabinoside).ResultsNo significant difference was observed for MN frequency comparing nurses handling ANPDs (exposed subjects) and controls; no correlations were found between job seniority, age, smoking habits and MN rates.ConclusionsConcerning the aim of this study to evaluate the genotoxic risk arising from occupational exposure to ANPDs, statistically significant differences in MN rates in the subjects under study could not be determined