Ensuring Code Safety Without Runtime Checks for Real-Time Control Systems
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Automatic Detection of Uninitialized Variables
Automatic Detection of Uninitialized Variables Thi Viet Nga Nguyen, Fran¸cois Irigoin, Corinne Ancourt, and Fabien Coelho Ecole des Mines de Paris, 77305 Fontainebleau, France {nguyen,irigoin,ancourt,coelho}@cri.ensmp.fr Abstract. One of the most common programming errors is the use of a variable before its definition. This undefined value may produce incorrect results, memory violations, unpredictable behaviors and program failure. To detect this kind of error, two approaches can be used: compile-time analysis and run-time checking. However, compile-time analysis is far from perfect because of complicated data and control flows as well as arrays with non-linear, indirection subscripts, etc. On the other hand, dynamic checking, although supported by hardware and compiler tech- niques, is costly due to heavy code instrumentation while information available at compile-time is not taken into account. This paper presents a combination of an efficient compile-time analysis and a source code instrumentation for run-time checking. All kinds of variables are checked by PIPS, a Fortran research compiler for program analyses, transformation, parallelization and verification. Uninitialized array elements are detected by using imported array region, an efficient inter-procedural array data flow analysis. If exact array regions cannot be computed and compile-time information is not sufficient, array elements are initialized to a special value and their utilization is accompanied by a value test to assert the legality of the access. In comparison to the dynamic instrumentation, our method greatly reduces the number of variables to be initialized and to be checked. Code instrumentation is only needed for some array sections, not for the whole array. -
A Status Update of BEAMJIT, the Just-In-Time Compiling Abstract Machine
A Status Update of BEAMJIT, the Just-in-Time Compiling Abstract Machine Frej Drejhammar and Lars Rasmusson <ffrej,[email protected]> 140609 Who am I? Senior researcher at the Swedish Institute of Computer Science (SICS) working on programming tools and distributed systems. Acknowledgments Project funded by Ericsson AB. Joint work with Lars Rasmusson <[email protected]>. What this talk is About An introduction to how BEAMJIT works and a detailed look at some subtle details of its implementation. Outline Background BEAMJIT from 10000m BEAMJIT-aware Optimization Compiler-supported Profiling Future Work Questions Just-In-Time (JIT) Compilation Decide at runtime to compile \hot" parts to native code. Fairly common implementation technique. McCarthy's Lisp (1969) Python (Psyco, PyPy) Smalltalk (Cog) Java (HotSpot) JavaScript (SquirrelFish Extreme, SpiderMonkey, J¨agerMonkey, IonMonkey, V8) Motivation A JIT compiler increases flexibility. Tracing does not require switching to full emulation. Cross-module optimization. Compiled BEAM modules are platform independent: No need for cross compilation. Binaries not strongly coupled to a particular build of the emulator. Integrates naturally with code upgrade. Project Goals Do as little manual work as possible. Preserve the semantics of plain BEAM. Automatically stay in sync with the plain BEAM, i.e. if bugs are fixed in the interpreter the JIT should not have to be modified manually. Have a native code generator which is state-of-the-art. Eventually be better than HiPE (steady-state). Plan Use automated tools to transform and extend the BEAM. Use an off-the-shelf optimizer and code generator. Implement a tracing JIT compiler. BEAM: Specification & Implementation BEAM is the name of the Erlang VM. -
Advanced Practical Programming for Scientists
Advanced practical Programming for Scientists Thorsten Koch Zuse Institute Berlin TU Berlin SS2017 The Zen of Python, by Tim Peters (part 1) ▶︎ Beautiful is better than ugly. ▶︎ Explicit is better than implicit. ▶︎ Simple is better than complex. ▶︎ Complex is better than complicated. ▶︎ Flat is better than nested. ▶︎ Sparse is better than dense. ▶︎ Readability counts. ▶︎ Special cases aren't special enough to break the rules. ▶︎ Although practicality beats purity. ▶︎ Errors should never pass silently. ▶︎ Unless explicitly silenced. ▶︎ In the face of ambiguity, refuse the temptation to guess. Advanced Programming 78 Ex1 again • Remember: store the data and compute the geometric mean on this stored data. • If it is not obvious how to compile your program, add a REAME file or a comment at the beginning • It should run as ex1 filenname • If you need to start something (python, python3, ...) provide an executable script named ex1 which calls your program, e.g. #/bin/bash python3 ex1.py $1 • Compare the number of valid values. If you have a lower number, you are missing something. If you have a higher number, send me the wrong line I am missing. File: ex1-100.dat with 100001235 lines Valid values Loc0: 50004466 with GeoMean: 36.781736 Valid values Loc1: 49994581 with GeoMean: 36.782583 Advanced Programming 79 Exercise 1: File Format (more detail) Each line should consists of • a sequence-number, • a location (1 or 2), and • a floating point value > 0. Empty lines are allowed. Comments can start a ”#”. Anything including and after “#” on a line should be ignored. -
Patterns in Dynamic Slices to Assist in Automated Debugging
Patterns in Dynamic Slices to Assist in Automated Debugging A thesis submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of Master of Science in the Department of Computer Science of the College of Engineering and Applied Science by Joshua W. Burbrink B.S. (Computer Science) University of Cincinnati, Cincinnati, Ohio, USA Thesis Advisor and Committee Chair: Dr. John Franco Abstract The process of manually debugging programs can be difficult and time consuming. The goal of this thesis is to aid developers in debugging their crashed programs by identifying common programming mistakes using information collected during the execution of the crashed program. Specifically, the solution proposed by this thesis calculates a dynamic slice with respect to conditions at the time of the crash and inspects the dynamic slice for characteristics that are only present when certain types of programming errors occur. Common programing mistakes explored by this thesis include: uninitialized heap variables, uninitialized stack variables, dangling pointers, and stack overflow. A prototype, based on the GNU Debugger, was developed to implement the concepts of this thesis and was used to identify common programming mistakes made in a set of example C programs. Experimental results showed that the solutions presented in this thesis performed very well. The prototype was able to identify all occurrences of uninitialized stack variables, uninitialized heap variables, and stack overflow, and only a few instances of dangling pointers were missed. Upon identifying a common programming mistake in the dynamic slice, the prototype was able to produce a high level debug report indicating source code lines relevant to the coding mistake. -
Using JIT Compilation and Configurable Runtime Systems for Efficient Deployment of Java Programs on Ubiquitous Devices
Using JIT Compilation and Configurable Runtime Systems for Efficient Deployment of Java Programs on Ubiquitous Devices Radu Teodorescu and Raju Pandey Parallel and Distributed Computing Laboratory Computer Science Department University of California, Davis {teodores,pandey}@cs.ucdavis.edu Abstract. As the proliferation of ubiquitous devices moves computation away from the conventional desktop computer boundaries, distributed systems design is being exposed to new challenges. A distributed system supporting a ubiquitous computing application must deal with a wider spectrum of hardware architectures featuring structural and functional differences, and resources limitations. Due to its architecture independent infrastructure and object-oriented programming model, the Java programming environment can support flexible solutions for addressing the diversity among these devices. Unfortunately, Java solutions are often associated with high costs in terms of resource consumption, which limits the range of devices that can benefit from this approach. In this paper, we present an architecture that deals with the cost and complexity of running Java programs by partitioning the process of Java program execution between system nodes and remote devices. The system nodes prepare a Java application for execution on a remote device by generating device-specific native code and application-specific runtime system on the fly. The resulting infrastructure provides the flexibility of a high-level programming model and the architecture independence specific to Java. At the same time the amount of resources consumed by an application on the targeted device are comparable to that of a native implementation. 1 Introduction As a result of the proliferation of computation into the physical world, ubiquitous computing [1] has become an important research topic. -
CDC Build System Guide
CDC Build System Guide Java™ Platform, Micro Edition Connected Device Configuration, Version 1.1.2 Foundation Profile, Version 1.1.2 Optimized Implementation Sun Microsystems, Inc. www.sun.com December 2008 Copyright © 2008 Sun Microsystems, Inc., 4150 Network Circle, Santa Clara, California 95054, U.S.A. All rights reserved. Sun Microsystems, Inc. has intellectual property rights relating to technology embodied in the product that is described in this document. In particular, and without limitation, these intellectual property rights may include one or more of the U.S. patents listed at http://www.sun.com/patents and one or more additional patents or pending patent applications in the U.S. and in other countries. U.S. Government Rights - Commercial software. Government users are subject to the Sun Microsystems, Inc. standard license agreement and applicable provisions of the FAR and its supplements. This distribution may include materials developed by third parties. Parts of the product may be derived from Berkeley BSD systems, licensed from the University of California. UNIX is a registered trademark in the U.S. and in other countries, exclusively licensed through X/Open Company, Ltd. Sun, Sun Microsystems, the Sun logo, Java, Solaris and HotSpot are trademarks or registered trademarks of Sun Microsystems, Inc. or its subsidiaries in the United States and other countries. The Adobe logo is a registered trademark of Adobe Systems, Incorporated. Products covered by and information contained in this service manual are controlled by U.S. Export Control laws and may be subject to the export or import laws in other countries. Nuclear, missile, chemical biological weapons or nuclear maritime end uses or end users, whether direct or indirect, are strictly prohibited. -
EXE: Automatically Generating Inputs of Death
EXE: Automatically Generating Inputs of Death Cristian Cadar, Vijay Ganesh, Peter M. Pawlowski, David L. Dill, Dawson R. Engler Computer Systems Laboratory Stanford University Stanford, CA 94305, U.S.A {cristic, vganesh, piotrek, dill, engler} @cs.stanford.edu ABSTRACT 1. INTRODUCTION This paper presents EXE, an effective bug-finding tool that Attacker-exposed code is often a tangled mess of deeply- automatically generates inputs that crash real code. Instead nested conditionals, labyrinthine call chains, huge amounts of running code on manually or randomly constructed input, of code, and frequent, abusive use of casting and pointer EXE runs it on symbolic input initially allowed to be “any- operations. For safety, this code must exhaustively vet in- thing.” As checked code runs, EXE tracks the constraints put received directly from potential attackers (such as sys- on each symbolic (i.e., input-derived) memory location. If a tem call parameters, network packets, even data from USB statement uses a symbolic value, EXE does not run it, but sticks). However, attempting to guard against all possible instead adds it as an input-constraint; all other statements attacks adds significant code complexity and requires aware- run as usual. If code conditionally checks a symbolic ex- ness of subtle issues such as arithmetic and buffer overflow pression, EXE forks execution, constraining the expression conditions, which the historical record unequivocally shows to be true on the true branch and false on the other. Be- programmers reason about poorly. cause EXE reasons about all possible values on a path, it Currently, programmers check for such errors using a com- has much more power than a traditional runtime tool: (1) bination of code review, manual and random testing, dy- it can force execution down any feasible program path and namic tools, and static analysis. -
An Opencl Runtime System for a Heterogeneous Many-Core Virtual Platform Kuan-Chung Chen and Chung-Ho Chen Inst
An OpenCL Runtime System for a Heterogeneous Many-Core Virtual Platform Kuan-Chung Chen and Chung-Ho Chen Inst. of Computer & Communication Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C. [email protected], [email protected] Abstract—We present a many-core full system is executed on a Linux PC while the kernel code execution is simulation platform and its OpenCL runtime system. The simulated on an ESL many-core system. OpenCL runtime system includes an on-the-fly compiler and resource manager for the ARM-based many-core The rest of this paper consists of the following sections. In platform. Using this platform, we evaluate approaches of Section 2, we briefly introduce the OpenCL execution model work-item scheduling and memory management in and our heterogeneous full system simulation platform. Section OpenCL memory hierarchy. Our experimental results 3 discusses the OpenCL runtime implementation in detail and show that scheduling work-items on a many-core system its enhancements. Section 4 presents the evaluation system and using general purpose RISC CPU should avoid per work- results. We conclude this paper in Section 5. item context switching. Data deployment and work-item coalescing are the two keys for significant speedup. II. OPENCL MODEL AND VIRTUAL PLATFORM Keywords—OpenCL; runtime system; work-item coalescing; In this section, we first introduce the OpenCL programming full system simulation; heterogeneous integration; model and followed by our SystemC-based virtual platform architecture. I. INTRODUCTION A. OpenCL Platform Overview OpenCL is a data parallel programming model introduced Fig. 1 shows a conceptual OpenCL platform which has two for heterogeneous system architecture [1] which may include execution components: a host processor and the compute CPUs, GPUs, or other accelerator devices. -
Linking: from the Object File to the Executable
Linking: from the object file to the executable An overview of static and dynamic linking Alessandro Di Federico Politecnico di Milano April 11, 2018 Index ELF format overview Static linking Dynamic linking Advanced linking features main.c parse.c Preprocessor Preprocessor C code C code Compiler Compiler Assembly Assembly Assembler Assembler libm.a Object file Object file Linker Object files Executable program We’ll focus on the ELF format [2] The object file • An object file is the final form of a translation unit • It uses the ELF format • It’s divided in several sections: .text The actual executable code .data Global, initialized, writeable data .bss Global, non-initialized, writeable data .rodata Global read-only data .symtab The symbol table .strtab String table for the symbol table .rela.section The relocation table for section .shstrtab String table for the section names An example #include<stdint.h> uint32_t uninitialized; uint32_t zero_intialized = 0; const uint32_t constant = 0x41424344; const uint32_t *initialized = &constant; uint32_t a_function() { return 42 + uninitialized; } An example $ gcc -c example.c -o example.o -fno-PIC -fno-stack-protector .text $ objdump -d -j .text example.o Disassembly of section .text: 0000000000000000 <a_function>: 0:55 push rbp 1:4889e5 mov rbp,rsp 4: 8b 05 00 00 00 00 mov eax, [rip+0x0] a:83c02a add eax,0x2a d: 5d pop rbp e: c3 ret The sections $ readelf -S example.o Nr Name Type Off Size ES Flg Inf 0 NULL 00000000 0 1.text PROGBITS 040 00f 00 AX 0 2.rela.text RELA 298 018 18 I 1 3.data PROGBITS 050 008 00 WA 0 4.rela.data RELA 2b0 018 18 I 3 5.bss NOBITS 058004 00 WA 0 6 .rodata PROGBITS 05c 004 00 A 0 .. -
In Using the GNU Compiler Collection (GCC)
Using the GNU Compiler Collection For gcc version 6.1.0 (GCC) Richard M. Stallman and the GCC Developer Community Published by: GNU Press Website: http://www.gnupress.org a division of the General: [email protected] Free Software Foundation Orders: [email protected] 51 Franklin Street, Fifth Floor Tel 617-542-5942 Boston, MA 02110-1301 USA Fax 617-542-2652 Last printed October 2003 for GCC 3.3.1. Printed copies are available for $45 each. Copyright c 1988-2016 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with the Invariant Sections being \Funding Free Software", the Front-Cover Texts being (a) (see below), and with the Back-Cover Texts being (b) (see below). A copy of the license is included in the section entitled \GNU Free Documentation License". (a) The FSF's Front-Cover Text is: A GNU Manual (b) The FSF's Back-Cover Text is: You have freedom to copy and modify this GNU Manual, like GNU software. Copies published by the Free Software Foundation raise funds for GNU development. i Short Contents Introduction ::::::::::::::::::::::::::::::::::::::::::::: 1 1 Programming Languages Supported by GCC ::::::::::::::: 3 2 Language Standards Supported by GCC :::::::::::::::::: 5 3 GCC Command Options ::::::::::::::::::::::::::::::: 9 4 C Implementation-Defined Behavior :::::::::::::::::::: 373 5 C++ Implementation-Defined Behavior ::::::::::::::::: 381 6 Extensions to -
Vinculum II Memory Management Application Note an 157
Future Technology Devices International Ltd. Vinculum II Memory Management Application Note AN_157 Document Reference No.: FT_000352 Version 1.2 Issue Date: 2010-11-26 This application note discusses methods for efficient use of RAM in Vinculum-II (VNC2) applications. Future Technology Devices International Ltd (FTDI) Unit 1, 2 Seaward Place, Centurion Business Park, Glasgow, G41 1HH, United Kingdom Tel.: +44 (0) 141 429 2777 Fax: + 44 (0) 141 429 2758 E-Mail (Support): [email protected] Web: http://www.ftdichip.com Use of FTDI devices in life support and/or safety applications is entirely at the user‟s risk, and the user agrees to defend, indemnify and hold harmless FTDI from any and all damages, claims, suits or expense resulting from such use. Copyright © 2010 Future Technology Devices International Limited Document Reference No.: FT_000352 AN_157 Vinculum II Memory Management Version 1.3 Clearance No.: FTDI# 178 ` 1 Introduction The FTDI Vinculum-II (part number VNC2) is a dual channel USB 2.0 Device/Host, microcontroller based IC targeted at USB applications. The device has 256kB of ROM storage and 16kB of user RAM. Memory for storing global and static variables in RAM is allocated by the Vinculum-II Toolchain which is used to develop user application firmware. This Toolchain is available free of charge at the following website http://www.ftdichip.com/Firmware/VNC2tools.htm#VNC2Toolchain. The Vinculum Operating System (VOS) kernel allows multiple threads to have a separate and configurable stack space and also maintains a global heap for dynamic memory allocation within VNC2. This application note discusses how to efficiently allocate the VNC2 memory between threads and dynamic allocation. -
A Source to Source Translator
SML2Java: A Source to Source Translator Justin Koser, Haakon Larsen, and Jeffrey A. Vaughan Cornell University Abstract. Java code is unsafe in several respects. Explicit null refer- ences and object downcasting can cause unexpected runtime errors. Java also lacks powerful language features such as pattern matching and first- class functions. However, due to its widespread use, cross-platform com- patibility, and comprehensive library support, Java remains a popular language. This paper discusses SML2Java, a source to source translator. SML2Java operates on type checked SML code and, to the greatest extent possible, produces functionally equivalent Java source. SML2Java allows program- mers to combine existing SML code and Java applications. While direct translation of SML primitive types to Java primitive types is not possible, the Java class system provides a powerful framework for emulating SML value semantics. Function translations are based on a substantial similarity between Java’s first-class objects and SML’s first- class functions. 1 Introduction SML2Java is a source-to-source translator from Standard ML (SML), a statically typed functional language [8], to Java, an object-oriented imperative language. A successful translator must emulate distinctive features of one language in the other. For instance, SML’s first-class functions are mapped to Java’s first-class objects, and an SML let expression could conceivably be translated to a Java interface containing an ’in’ function, where every let expression in SML would produce an anonymous instantiation of the let interface in Java. Similarly, many other functional features of SML are translated to take advantage of Java’s object-oriented style.