Conservative Signed/Unsigned Type Inference for Binaries Using Minimum Cut
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Metaobject Protocols: Why We Want Them and What Else They Can Do
Metaobject protocols: Why we want them and what else they can do Gregor Kiczales, J.Michael Ashley, Luis Rodriguez, Amin Vahdat, and Daniel G. Bobrow Published in A. Paepcke, editor, Object-Oriented Programming: The CLOS Perspective, pages 101 ¾ 118. The MIT Press, Cambridge, MA, 1993. © Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. Metaob ject Proto cols WhyWeWant Them and What Else They Can Do App ears in Object OrientedProgramming: The CLOS Perspective c Copyright 1993 MIT Press Gregor Kiczales, J. Michael Ashley, Luis Ro driguez, Amin Vahdat and Daniel G. Bobrow Original ly conceivedasaneat idea that could help solve problems in the design and implementation of CLOS, the metaobject protocol framework now appears to have applicability to a wide range of problems that come up in high-level languages. This chapter sketches this wider potential, by drawing an analogy to ordinary language design, by presenting some early design principles, and by presenting an overview of three new metaobject protcols we have designed that, respectively, control the semantics of Scheme, the compilation of Scheme, and the static paral lelization of Scheme programs. Intro duction The CLOS Metaob ject Proto col MOP was motivated by the tension b etween, what at the time, seemed liketwo con icting desires. The rst was to have a relatively small but p owerful language for doing ob ject-oriented programming in Lisp. The second was to satisfy what seemed to b e a large numb er of user demands, including: compatibility with previous languages, p erformance compara- ble to or b etter than previous implementations and extensibility to allow further exp erimentation with ob ject-oriented concepts see Chapter 2 for examples of directions in which ob ject-oriented techniques might b e pushed. -
250P: Computer Systems Architecture Lecture 10: Caches
250P: Computer Systems Architecture Lecture 10: Caches Anton Burtsev April, 2021 The Cache Hierarchy Core L1 L2 L3 Off-chip memory 2 Accessing the Cache Byte address 101000 Offset 8-byte words 8 words: 3 index bits Direct-mapped cache: each address maps to a unique address Sets Data array 3 The Tag Array Byte address 101000 Tag 8-byte words Compare Direct-mapped cache: each address maps to a unique address Tag array Data array 4 Increasing Line Size Byte address A large cache line size smaller tag array, fewer misses because of spatial locality 10100000 32-byte cache Tag Offset line size or block size Tag array Data array 5 Associativity Byte address Set associativity fewer conflicts; wasted power because multiple data and tags are read 10100000 Tag Way-1 Way-2 Tag array Data array Compare 6 Example • 32 KB 4-way set-associative data cache array with 32 byte line sizes • How many sets? • How many index bits, offset bits, tag bits? • How large is the tag array? 7 Types of Cache Misses • Compulsory misses: happens the first time a memory word is accessed – the misses for an infinite cache • Capacity misses: happens because the program touched many other words before re-touching the same word – the misses for a fully-associative cache • Conflict misses: happens because two words map to the same location in the cache – the misses generated while moving from a fully-associative to a direct-mapped cache • Sidenote: can a fully-associative cache have more misses than a direct-mapped cache of the same size? 8 Reducing Miss Rate • Large block -
Functional Languages
Functional Programming Languages (FPL) 1. Definitions................................................................... 2 2. Applications ................................................................ 2 3. Examples..................................................................... 3 4. FPL Characteristics:.................................................... 3 5. Lambda calculus (LC)................................................. 4 6. Functions in FPLs ....................................................... 7 7. Modern functional languages...................................... 9 8. Scheme overview...................................................... 11 8.1. Get your own Scheme from MIT...................... 11 8.2. General overview.............................................. 11 8.3. Data Typing ...................................................... 12 8.4. Comments ......................................................... 12 8.5. Recursion Instead of Iteration........................... 13 8.6. Evaluation ......................................................... 14 8.7. Storing and using Scheme code ........................ 14 8.8. Variables ........................................................... 15 8.9. Data types.......................................................... 16 8.10. Arithmetic functions ......................................... 17 8.11. Selection functions............................................ 18 8.12. Iteration............................................................. 23 8.13. Defining functions ........................................... -
Chapter 2 Basics of Scanning And
Chapter 2 Basics of Scanning and Conventional Programming in Java In this chapter, we will introduce you to an initial set of Java features, the equivalent of which you should have seen in your CS-1 class; the separation of problem, representation, algorithm and program – four concepts you have probably seen in your CS-1 class; style rules with which you are probably familiar, and scanning - a general class of problems we see in both computer science and other fields. Each chapter is associated with an animating recorded PowerPoint presentation and a YouTube video created from the presentation. It is meant to be a transcript of the associated presentation that contains little graphics and thus can be read even on a small device. You should refer to the associated material if you feel the need for a different instruction medium. Also associated with each chapter is hyperlinked code examples presented here. References to previously presented code modules are links that can be traversed to remind you of the details. The resources for this chapter are: PowerPoint Presentation YouTube Video Code Examples Algorithms and Representation Four concepts we explicitly or implicitly encounter while programming are problems, representations, algorithms and programs. Programs, of course, are instructions executed by the computer. Problems are what we try to solve when we write programs. Usually we do not go directly from problems to programs. Two intermediate steps are creating algorithms and identifying representations. Algorithms are sequences of steps to solve problems. So are programs. Thus, all programs are algorithms but the reverse is not true. -
Integers in C: an Open Invitation to Security Attacks?
Integers In C: An Open Invitation To Security Attacks? Zack Coker Samir Hasan Jeffrey Overbey [email protected] [email protected] [email protected] Munawar Hafiz Christian Kästnery [email protected] Auburn University yCarnegie Mellon University Auburn, AL, USA Pittsburgh, PA, USA ABSTRACT integer type is assigned to a location with a smaller type, We performed an empirical study to explore how closely e.g., assigning an int value to a short variable. well-known, open source C programs follow the safe C stan- Secure coding standards, such as MISRA's Guidelines for dards for integer behavior, with the goal of understanding the use of the C language in critical systems [37] and CERT's how difficult it is to migrate legacy code to these stricter Secure Coding in C and C++ [44], identify these issues that standards. We performed an automated analysis on fifty-two are otherwise allowed (and sometimes partially defined) in releases of seven C programs (6 million lines of preprocessed the C standard and impose restrictions on the use of inte- C code), as well as releases of Busybox and Linux (nearly gers. This is because most of the integer issues are benign. one billion lines of partially-preprocessed C code). We found But, there are two cases where the issues may cause serious that integer issues, that are allowed by the C standard but problems. One is in safety-critical sections, where untrapped not by the safer C standards, are ubiquitous|one out of four integer issues can lead to unexpected runtime behavior. An- integers were inconsistently declared, and one out of eight other is in security-critical sections, where integer issues can integers were inconsistently used. -
BALANCING the Eulisp METAOBJECT PROTOCOL
LISP AND SYMBOLIC COMPUTATION An International Journal c Kluwer Academic Publishers Manufactured in The Netherlands Balancing the EuLisp Metaob ject Proto col y HARRY BRETTHAUER bretthauergmdde JURGEN KOPP koppgmdde German National Research Center for Computer Science GMD PO Box W Sankt Augustin FRG HARLEY DAVIS davisilogfr ILOG SA avenue Gal lieni Gentil ly France KEITH PLAYFORD kjpmathsbathacuk School of Mathematical Sciences University of Bath Bath BA AY UK Keywords Ob jectoriented Programming Language Design Abstract The challenge for the metaob ject proto col designer is to balance the con icting demands of eciency simplici ty and extensibil ity It is imp ossible to know all desired extensions in advance some of them will require greater functionality while oth ers require greater eciency In addition the proto col itself must b e suciently simple that it can b e fully do cumented and understo o d by those who need to use it This pap er presents the framework of a metaob ject proto col for EuLisp which provides expressiveness by a multileveled proto col and achieves eciency by static semantics for predened metaob jects and mo dularizin g their op erations The EuLisp mo dule system supp orts global optimizations of metaob ject applicati ons The metaob ject system itself is structured into mo dules taking into account the consequences for the compiler It provides introsp ective op erations as well as extension interfaces for various functionaliti es including new inheritance allo cation and slot access semantics While -
The Hexadecimal Number System and Memory Addressing
C5537_App C_1107_03/16/2005 APPENDIX C The Hexadecimal Number System and Memory Addressing nderstanding the number system and the coding system that computers use to U store data and communicate with each other is fundamental to understanding how computers work. Early attempts to invent an electronic computing device met with disappointing results as long as inventors tried to use the decimal number sys- tem, with the digits 0–9. Then John Atanasoff proposed using a coding system that expressed everything in terms of different sequences of only two numerals: one repre- sented by the presence of a charge and one represented by the absence of a charge. The numbering system that can be supported by the expression of only two numerals is called base 2, or binary; it was invented by Ada Lovelace many years before, using the numerals 0 and 1. Under Atanasoff’s design, all numbers and other characters would be converted to this binary number system, and all storage, comparisons, and arithmetic would be done using it. Even today, this is one of the basic principles of computers. Every character or number entered into a computer is first converted into a series of 0s and 1s. Many coding schemes and techniques have been invented to manipulate these 0s and 1s, called bits for binary digits. The most widespread binary coding scheme for microcomputers, which is recog- nized as the microcomputer standard, is called ASCII (American Standard Code for Information Interchange). (Appendix B lists the binary code for the basic 127- character set.) In ASCII, each character is assigned an 8-bit code called a byte. -
Abstract Data Types
Chapter 2 Abstract Data Types The second idea at the core of computer science, along with algorithms, is data. In a modern computer, data consists fundamentally of binary bits, but meaningful data is organized into primitive data types such as integer, real, and boolean and into more complex data structures such as arrays and binary trees. These data types and data structures always come along with associated operations that can be done on the data. For example, the 32-bit int data type is defined both by the fact that a value of type int consists of 32 binary bits but also by the fact that two int values can be added, subtracted, multiplied, compared, and so on. An array is defined both by the fact that it is a sequence of data items of the same basic type, but also by the fact that it is possible to directly access each of the positions in the list based on its numerical index. So the idea of a data type includes a specification of the possible values of that type together with the operations that can be performed on those values. An algorithm is an abstract idea, and a program is an implementation of an algorithm. Similarly, it is useful to be able to work with the abstract idea behind a data type or data structure, without getting bogged down in the implementation details. The abstraction in this case is called an \abstract data type." An abstract data type specifies the values of the type, but not how those values are represented as collections of bits, and it specifies operations on those values in terms of their inputs, outputs, and effects rather than as particular algorithms or program code. -
Understanding Integer Overflow in C/C++
Appeared in Proceedings of the 34th International Conference on Software Engineering (ICSE), Zurich, Switzerland, June 2012. Understanding Integer Overflow in C/C++ Will Dietz,∗ Peng Li,y John Regehr,y and Vikram Adve∗ ∗Department of Computer Science University of Illinois at Urbana-Champaign fwdietz2,[email protected] ySchool of Computing University of Utah fpeterlee,[email protected] Abstract—Integer overflow bugs in C and C++ programs Detecting integer overflows is relatively straightforward are difficult to track down and may lead to fatal errors or by using a modified compiler to insert runtime checks. exploitable vulnerabilities. Although a number of tools for However, reliable detection of overflow errors is surprisingly finding these bugs exist, the situation is complicated because not all overflows are bugs. Better tools need to be constructed— difficult because overflow behaviors are not always bugs. but a thorough understanding of the issues behind these errors The low-level nature of C and C++ means that bit- and does not yet exist. We developed IOC, a dynamic checking tool byte-level manipulation of objects is commonplace; the line for integer overflows, and used it to conduct the first detailed between mathematical and bit-level operations can often be empirical study of the prevalence and patterns of occurrence quite blurry. Wraparound behavior using unsigned integers of integer overflows in C and C++ code. Our results show that intentional uses of wraparound behaviors are more common is legal and well-defined, and there are code idioms that than is widely believed; for example, there are over 200 deliberately use it. -
Midterm-2020-Solution.Pdf
HONOR CODE Questions Sheet. A Lets C. [6 Points] 1. What type of address (heap,stack,static,code) does each value evaluate to Book1, Book1->name, Book1->author, &Book2? [4] 2. Will all of the print statements execute as expected? If NO, write print statement which will not execute as expected?[2] B. Mystery [8 Points] 3. When the above code executes, which line is modified? How many times? [2] 4. What is the value of register a6 at the end ? [2] 5. What is the value of register a4 at the end ? [2] 6. In one sentence what is this program calculating ? [2] C. C-to-RISC V Tree Search; Fill in the blanks below [12 points] D. RISCV - The MOD operation [8 points] 19. The data segment starts at address 0x10000000. What are the memory locations modified by this program and what are their values ? E Floating Point [8 points.] 20. What is the smallest nonzero positive value that can be represented? Write your answer as a numerical expression in the answer packet? [2] 21. Consider some positive normalized floating point number where p is represented as: What is the distance (i.e. the difference) between p and the next-largest number after p that can be represented? [2] 22. Now instead let p be a positive denormalized number described asp = 2y x 0.significand. What is the distance between p and the next largest number after p that can be represented? [2] 23. Sort the following minifloat numbers. [2] F. Numbers. [5] 24. What is the smallest number that this system can represent 6 digits (assume unsigned) ? [1] 25. -
2018-19 MAP 160 Byte File Layout Specifications
2018-19 MAP 160 Byte File Layout Specifications OVERVIEW: A) ISAC will provide an Eligibility Status File (ESF) record for each student to all schools listed as a college choice on the student’s Student Aid Report (SAR). The ESF records will be available daily as Record Type = 7. ESF records may be retrieved via the File Extraction option in MAP. B) Schools will transmit Payment Requests to ISAC via File Transfer Protocol (FTP) using the MAP 160byte layout and identify these with Record Type = 4. C) When payment requests are processed, ISAC will provide payment results to schools through the MAP system. The payment results records can be retrieved in the 160 byte format using the MAP Payment Results File Extraction Option. MAP results records have a Record Type = 5. The MAP Payment Results file contains some eligibility status data elements. Also, the same student record may appear on both the Payment Results and the Eligibility Status extract files. Schools may also use the Reports option in MAP to obtain payment results. D) To cancel Payment Requests, the school with the current Payment Results record on ISAC's Payment Database must transmit a matching record with MAP Payment Request Code = C, with the Requested Award Amount field equal to zero and the Enrollment Hours field equal to 0 along with other required data elements. These records must be transmitted to ISAC as Record Type = 4. E) Summary of Data Element Changes, revision (highlighted in grey) made to the 2018-19 layout. NONE 1 2018-19 MAP 160 Byte File Layout Specifications – 9/17 2018-19 MAP 160 Byte File Layout Specifications F) The following 160 byte record layout will be used for transmitting data between schools and ISAC. -
5. Data Types
IEEE FOR THE FUNCTIONAL VERIFICATION LANGUAGE e Std 1647-2011 5. Data types The e language has a number of predefined data types, including the integer and Boolean scalar types common to most programming languages. In addition, new scalar data types (enumerated types) that are appropriate for programming, modeling hardware, and interfacing with hardware simulators can be created. The e language also provides a powerful mechanism for defining OO hierarchical data structures (structs) and ordered collections of elements of the same type (lists). The following subclauses provide a basic explanation of e data types. 5.1 e data types Most e expressions have an explicit data type, as follows: — Scalar types — Scalar subtypes — Enumerated scalar types — Casting of enumerated types in comparisons — Struct types — Struct subtypes — Referencing fields in when constructs — List types — The set type — The string type — The real type — The external_pointer type — The “untyped” pseudo type Certain expressions, such as HDL objects, have no explicit data type. See 5.2 for information on how these expressions are handled. 5.1.1 Scalar types Scalar types in e are one of the following: numeric, Boolean, or enumerated. Table 17 shows the predefined numeric and Boolean types. Both signed and unsigned integers can be of any size and, thus, of any range. See 5.1.2 for information on how to specify the size and range of a scalar field or variable explicitly. See also Clause 4. 5.1.2 Scalar subtypes A scalar subtype can be named and created by using a scalar modifier to specify the range or bit width of a scalar type.