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

Systematic innovation and the underlying principles behind TRIZ and TOC

Innovative developments in the design of product and manufacturing systems are often marked by simplicity, at least in retrospect, that has previously been shrouded by restrictive mental models or limited knowledge transfer. These innovative developments are often associated with the breaking of long established trade-off compromises, as in the paradigm shift associated with JIT & TQM, or the resolution of design contradictions, as in the case of the dual cyclone vacuum cleaner. The rate of change in technology and the commercial environment suggests the opportunity for innovative developments is accelerating, but what systematic support is there to guide this innovation process. This paper brings together two parallel, but independent theories on inventive problem solving; one in mechanical engineering, namely the Russian Theory of Inventive Problem Solving (TRIZ) and the other originating in manufacturing management as the Theory of Constraints (TOC). The term systematic innovation is used to describe the use of common underlying principles within these two approaches. The paper focuses on the significance of trade-off contradictions to innovation in these two fields and explores their relationship with manufacturing strategy development

Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond

In this and a set of companion whitepapers, the USQCD Collaboration lays out a program of science and computing for lattice gauge theory. These whitepapers describe how calculation using lattice QCD (and other gauge theories) can aid the interpretation of ongoing and upcoming experiments in particle and nuclear physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers

A Prior Study of Split Compilation and Approximate Floating-Point Computations

From a future perspective of heterogeneous multicore processors, we studied several optimization processes specified for floating-point numbers in this internship. To adjust this new generation processors, we believe {¥it split compilation} has an important role to receive the maximum benefits from these processors. A purpose of our internship is to know a potential of tradeoff between accuracy and speedup of floating-point computations as a prior-study to promote the notion of split compilation. In many case, we can more successfully optimize a target program if we are able to timely apply appropriate optimizations in dynamic. However, an online compiler cannot always apply aggressive optimizations because of its constraints like memory resources or compilation time, and the online compiler has to respect these constraints. To overcome these constraints, the split compilation can combine statically analyzed information and dynamic timely information. For this reason, in the bibliographic part, we mainly studied the several compilation method and floating-point numbers to smoothly move to our internship

Strategies for protecting intellectual property when using CUDA applications on graphics processing units

Recent advances in the massively parallel computational abilities of graphical processing units (GPUs) have increased their use for general purpose computation, as companies look to take advantage of big data processing techniques. This has given rise to the potential for malicious software targeting GPUs, which is of interest to forensic investigators examining the operation of software. The ability to carry out reverse-engineering of software is of great importance within the security and forensics elds, particularly when investigating malicious software or carrying out forensic analysis following a successful security breach. Due to the complexity of the Nvidia CUDA (Compute Uni ed Device Architecture) framework, it is not clear how best to approach the reverse engineering of a piece of CUDA software. We carry out a review of the di erent binary output formats which may be encountered from the CUDA compiler, and their implications on reverse engineering. We then demonstrate the process of carrying out disassembly of an example CUDA application, to establish the various techniques available to forensic investigators carrying out black-box disassembly and reverse engineering of CUDA binaries. We show that the Nvidia compiler, using default settings, leaks useful information. Finally, we demonstrate techniques to better protect intellectual property in CUDA algorithm implementations from reverse engineering