DOCSLIB.ORG

General, Scalable Path-Sensitive Fault Detection

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
General, Scalable Path-Sensitive Fault Detection General, Scalable Path-Sensitive Fault Detection Wei Le Mary Lou Soffa Department of Computer Science Department of Computer Science University of Virginia University of Virginia [email protected] [email protected] ABSTRACT software developers, and code is only allowed to check in Recent research has shown that program paths are impor- after it passes the inspection of those tools. Research has tant in static analysis for detecting and reporting faults. shown that program path information is essential in static However, developing a scalable path-sensitive analysis is chal- analysis for achieving precise fault detection, especially for lenging. Often the techniques only address one particular complex faults [5, 14, 20]. In path-sensitive analysis, identi- type of fault and require much manual effort to tune for the fied infeasible paths can be excluded, and program facts col- desirable scalability and precision. In this paper, we present lected along different paths for determining faults are never a novel framework that automatically generates scalable, in- merged, avoiding a loss of precision. Furthermore, because terprocedural, path-sensitive analyses to detect user speci- a fault often results from a sequence of executions, reporting fied faults. The framework consists of a specification tech- a fault in terms of path segments instead of program points nique for expressing program properties related to faults, allows the code inspector to follow the actual code sequences a scalable path-sensitive algorithm, and a generator that that lead to the fault. unifies the two. The generated analysis identifies not only Although useful for both detecting and reporting a fault, faults but also the path segments that are relevant to the developing a path-sensitive detector that is scalable for var- faults. The generality of the framework is accomplished for ious types of faults is challenging. In current path-sensitive both data and control centric faults, so that the detection detectors, scalability is mostly addressed for a certain type of of multiple types of faults can be unified, which enables the fault. For example, ESP [5] achieves a polynomial time ver- exploitation of fault interactions for diagnosis and efficiency. ification on identifying typestate violations. The scalability We implemented our framework and generated fault detec- is achieved by applying heuristics to select information rel- tors for identifying buffer overflow, integer truncation and evant to the fault. The approach is only shown effective for signedness errors, and null-pointer dereference. We exper- typestate errors because its underlying assumption is based imentally demonstrated that the generated analysis scales on how a programmer might program typestate transitions up to at least half a million lines of code, and its detection in practice. Tools such as Saturn [20] apply a modular, in- capability is comparable to manually produced analyses. In stead of whole program analysis. That is, parts of program our experiment, a total of 53 faults of the three types from are analyzed in isolation and then the results are composed 9 benchmarks are detected, among which 37 have not been at the whole program level. This summary based technique reported previously. The results show that we are able to is not effective for faults whose detection requires a large identify faults deeply embedded in the code, and the aver- amount of summary information or the tracking of global age length of faulty path segments is 1–4 procedures, which side effects. In fact, Saturn only demonstrates its effective- provides a focus for diagnosis. ness in detecting typestate violations. Besides heuristics and modular analysis, applying annotations is also a solution for scalability. ESPx [10] annotates procedural interfaces, aim- 1. INTRODUCTION ing to convert an interprocedural analysis to intraprocedu- The use of static analyses to check for properties in pro- ral. However, supporting multiple types of faults increases grams, such as protocol violations and security vulnerabil- the use of annotations while both writing and verifying an- ities, has been growing with the recognition of their ef- notations require much human effort. ESPx only supports fectiveness in improving software robustness and security. buffer related contracts. Our previous research developed a At Microsoft, static detectors report thousands of faults demand-driven analysis to achieve the scalability in detect- monthly [16]. Static detectors are deployed on desktops for ing only buffer overflow [14]. Considering the manual effort and challenges to build in- dividual fault detectors, techniques that address scalability and also are applicable to a variety of faults are desirable. Because of the generality, such a system can enable the de- Permission to make digital or hard copies of all or part of this work for tection of interactions of different types of faults, such as personal or classroom use is granted without fee provided that copies are showing an integer fault can lead to buffer overflow. not made or distributed for profit or commercial advantage and that copies In this paper, we present a novel framework which enables bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific the automatic generation of scalable path-sensitive analyses permission and/or a fee. that detect user specified faults. The framework consists of Copyright 200X ACM X-XXXXX-XX-X/XX/XX ...$10.00. 1 a general, scalable algorithm, a specification technique, and • a scalable algorithm that detects both control and data a generator that unifies the two. The key idea is to address centric faults and returns path segments of the faults, the scalability of path-sensitive fault detection in a general demand-driven algorithm, and automatically generate the • a specification language for user specified faults, and fault-specific part of the analysis from a specification. • implementation and experimentation that demonstrate The generated analysis is path-sensitive in that program the generality and scalability of the framework. facts collected along different paths are distinguished. Stat- ically identifiable infeasible paths are also excluded. Impor- In the rest of the paper, we present the three components tantly, only path segments that are relevant to the produc- of the framework: a general analysis in Section 2, specifica- tion of faults are reported, providing both the focus and nec- tion in Section 3, and the generator in Section 4. We show essary context for the code reviewer to diagnose the faults. the experimental results in Section 5, and compare the re- The scalability of path-sensitive analysis is achieved by a lated work in Section 6. The conclusion is given in Section 7. demand-driven algorithm and a set of optimizations, all of which are encapsulated in the general algorithm. 2. SCALABLE ANALYSIS The generality of the framework is accomplished for both data and control centric faults. Data centric faults require Demand-driven is a static analysis technique that deter- the tracking of variable values and ranges for detection, mines where to collect the information from the program e.g., buffer overflow and integer faults, while control cen- source, what information to collect, and when the analysis tric faults, such as typestate violations, mainly focus on the should terminate. A demand-driven analysis detects faults order of operations. Although different types of informa- by raising queries at the statements where faults potentially tion are required, there are commonalities in fault detec- occur, inquiring whether the constraints of correctness would tion. Our insight is that 1) many types of faults are only hold. The analysis performs a backward path traversal to de- observable at certain types of program statements, and 2) termine the occurrence of the fault. In this section, we first on the paths to such observable points, only certain types use an example to intuitively explain how a demand-driven of statements can contribute to the failure. By identifying analysis works to identify different types of faults. We then such observable points, we can construct a query at those define key components in a general analysis, and present a points regarding whether the fault can occur and propagate general algorithm developed based on the definitions. the query along the paths for resolution. Similarly, given contributing points, we know where to collect information 2.1 An Example for Demand-Driven Analysis to resolve the query and thus determine the fault. That is, In Figure 1, an integer signedness error occurs at node 11. by supplying observable/contributing points and the corre- We show how our analysis identifies this fault. In the first sponding actions at the points, we are able to guide a general step, the analysis performs a linear scan and identifies node 11 analysis to locate the desired faults. as a potentially faulty statement, because at node 11, a We develop a specification language for users to specify signedness conversion occurs for integer x to match the in- such information. The specification uses code signatures to terface of malloc. We raise a query [Value(x) ≥ 0] at identify observable and contributing points, and introduces node 11, indicating for integer safety, the value of x should the basic construct attributes to map the code to desired be non-negative along all paths before the signedness con- abstractions. For example, attribute Len(s) represents the version. The query is propagated backwards to determine length of the string s and Value(i) expresses the integer the satisfaction of the constraint. At node 10, the query is value stored in the variable i. Using attributes and a set of first updated to [Value(x)*8 ≥ 0] via a symbolic substitu- operations on the attributes, we specify the actions at ob- tion. Along branch h8, 7i, the query encounters a constant servable/contributing points. To generate an analysis, the assignment and is resolved to [1024 ≥ 0] as safe.
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Signedness-Agnostic Program Analysis: Precise Integer Bounds for Low-Level Code
    Signedness-Agnostic Program Analysis: Precise Integer Bounds for Low-Level Code Jorge A. Navas, Peter Schachte, Harald Søndergaard, and Peter J. Stuckey Department of Computing and Information Systems, The University of Melbourne, Victoria 3010, Australia Abstract. Many compilers target common back-ends, thereby avoid- ing the need to implement the same analyses for many different source languages. This has led to interest in static analysis of LLVM code. In LLVM (and similar languages) most signedness information associated with variables has been compiled away. Current analyses of LLVM code tend to assume that either all values are signed or all are unsigned (except where the code specifies the signedness). We show how program analysis can simultaneously consider each bit-string to be both signed and un- signed, thus improving precision, and we implement the idea for the spe- cific case of integer bounds analysis. Experimental evaluation shows that this provides higher precision at little extra cost. Our approach turns out to be beneficial even when all signedness information is available, such as when analysing C or Java code. 1 Introduction The “Low Level Virtual Machine” LLVM is rapidly gaining popularity as a target for compilers for a range of programming languages. As a result, the literature on static analysis of LLVM code is growing (for example, see [2, 7, 9, 11, 12]). LLVM IR (Intermediate Representation) carefully specifies the bit- width of all integer values, but in most cases does not specify whether values are signed or unsigned. This is because, for most operations, two’s complement arithmetic (treating the inputs as signed numbers) produces the same bit-vectors as unsigned arithmetic.
    [Show full text]
  • RICH: Automatically Protecting Against Integer-Based Vulnerabilities
    RICH: Automatically Protecting Against Integer-Based Vulnerabilities David Brumley, Tzi-cker Chiueh, Robert Johnson [email protected], [email protected], [email protected] Huijia Lin, Dawn Song [email protected], [email protected] Abstract against integer-based attacks. Integer bugs appear because programmers do not antici- We present the design and implementation of RICH pate the semantics of C operations. The C99 standard [16] (Run-time Integer CHecking), a tool for efficiently detecting defines about a dozen rules governinghow integer types can integer-based attacks against C programs at run time. C be cast or promoted. The standard allows several common integer bugs, a popular avenue of attack and frequent pro- cases, such as many types of down-casting, to be compiler- gramming error [1–15], occur when a variable value goes implementation specific. In addition, the written rules are out of the range of the machine word used to materialize it, not accompanied by an unambiguous set of formal rules, e.g. when assigning a large 32-bit int to a 16-bit short. making it difficult for a programmer to verify that he un- We show that safe and unsafe integer operations in C can derstands C99 correctly. Integer bugs are not exclusive to be captured by well-known sub-typing theory. The RICH C. Similar languages such as C++, and even type-safe lan- compiler extension compiles C programs to object code that guages such as Java and OCaml, do not raise exceptions on monitors its own execution to detect integer-based attacks.
    [Show full text]
  • C Language Features
    C Language Features 5.4 SUPPORTED DATA TYPES AND VARIABLES 5.4.1 Identifiers A C variable identifier (the following is also true for function identifiers) is a sequence of letters and digits, where the underscore character “_” counts as a letter. Identifiers cannot start with a digit. Although they can start with an underscore, such identifiers are reserved for the compiler’s use and should not be defined by your programs. Such is not the case for assembly domain identifiers, which often begin with an underscore, see Section 5.12.3.1 “Equivalent Assembly Symbols”. Identifiers are case sensitive, so main is different to Main. Not every character is significant in an identifier. The maximum number of significant characters can be set using an option, see Section 4.8.8 “-N: Identifier Length”. If two identifiers differ only after the maximum number of significant characters, then the compiler will consider them to be the same symbol. 5.4.2 Integer Data Types The MPLAB XC8 compiler supports integer data types with 1, 2, 3 and 4 byte sizes as well as a single bit type. Table 5-1 shows the data types and their corresponding size and arithmetic type. The default type for each type is underlined. TABLE 5-1: INTEGER DATA TYPES Type Size (bits) Arithmetic Type bit 1 Unsigned integer signed char 8 Signed integer unsigned char 8 Unsigned integer signed short 16 Signed integer unsigned short 16 Unsigned integer signed int 16 Signed integer unsigned int 16 Unsigned integer signed short long 24 Signed integer unsigned short long 24 Unsigned integer signed long 32 Signed integer unsigned long 32 Unsigned integer signed long long 32 Signed integer unsigned long long 32 Unsigned integer The bit and short long types are non-standard types available in this implementa- tion.
    [Show full text]
  • Practical Integer Overflow Prevention
    Practical Integer Overflow Prevention Paul Muntean, Jens Grossklags, and Claudia Eckert {paul.muntean, jens.grossklags, claudia.eckert}@in.tum.de Technical University of Munich Memory error vulnerabilities categorized 160 Abstract—Integer overflows in commodity software are a main Other Format source for software bugs, which can result in exploitable memory Pointer 140 Integer Heap corruption vulnerabilities and may eventually contribute to pow- Stack erful software based exploits, i.e., code reuse attacks (CRAs). In 120 this paper, we present INTGUARD, a symbolic execution based tool that can repair integer overflows with high-quality source 100 code repairs. Specifically, given the source code of a program, 80 INTGUARD first discovers the location of an integer overflow error by using static source code analysis and satisfiability modulo 60 theories (SMT) solving. INTGUARD then generates integer multi- precision code repairs based on modular manipulation of SMT 40 Number of VulnerabilitiesNumber of constraints as well as an extensible set of customizable code repair 20 patterns. We evaluated INTGUARD with 2052 C programs (≈1 0 Mil. LOC) available in the currently largest open-source test suite 2000 2002 2004 2006 2008 2010 2012 2014 2016 for C/C++ programs and with a benchmark containing large and Year complex programs. The evaluation results show that INTGUARD Fig. 1: Integer related vulnerabilities reported by U.S. NIST. can precisely (i.e., no false positives are accidentally repaired), with low computational and runtime overhead repair programs with very small binary and source code blow-up. In a controlled flows statically in order to avoid them during runtime. For experiment, we show that INTGUARD is more time-effective and example, Sift [35], TAP [56], SoupInt [63], AIC/CIT [74], and achieves a higher repair success rate than manually generated CIntFix [11] rely on a variety of techniques depicted in detail code repairs.
    [Show full text]
  • Multistage Algorithms in C++
    Multistage Algorithms in C++ Dissertation zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen FakultÄaten der Georg-August-UniversitÄat zu GÄottingen vorgelegt von Andreas Priesnitz aus GÄottingen GÄottingen 2005 D7 Referent: Prof. Dr. G. Lube Korreferent: Prof. Dr. R. Schaback Tag der mundlicÄ hen Prufung:Ä 2. November 2005 Andreas Priesnitz Multistage Algorithms in C++ in commemoration of my father, Kuno Priesnitz Contents 1 Frequently Asked Questions 1 1.1 Who is it for? . 1 1.1.1 Domain of interest . 1 1.1.2 Domain of knowledge . 2 1.2 What is it for? . 2 1.2.1 E±ciency . 2 1.2.2 Abstraction . 3 1.2.3 Situation . 4 1.2.4 Quest . 5 1.3 Where is it from? . 5 1.3.1 Related subjects . 6 1.3.2 Related information . 6 1.3.3 Related software . 6 1.4 How is it done? . 7 1.4.1 Structure . 7 1.4.2 Language . 7 1.4.3 Layout . 8 I Paradigms 9 2 Object-oriented programming 11 2.1 User-de¯ned types . 11 2.1.1 Enumeration types . 11 2.1.2 Class types . 12 2.1.3 Class object initialization . 13 2.1.4 Class interface . 14 2.1.5 Global functions . 14 2.1.6 Inheritance . 15 2.1.7 Performance impact . 18 2.2 Dynamic polymorphism . 19 2.2.1 Virtual functions . 19 2.2.2 Integration into the type system . 20 2.2.3 Performance impact . 22 I 3 Generic programming 25 3.1 Class templates . 25 3.1.1 Constant template parameters .
    [Show full text]
  • Bit Value for Instruction in Binary Format
    Bit Value For Instruction In Binary Format Aubert outbox refinedly as citable Dimitris slatting her barrier rewarm diligently. Banded and crumb Christian photolithograph her Bryant withstanding while Hanan snigging some story rhythmically. Plaguey and crossopterygian Gustave garrotted, but Demetrius mostly swigging her aigret. After these names need even for instruction for in binary format is This enhances the binary format is inverted at st instructions are given all available, but the processor, such that the immediate or to create executable file. These other in a header followed by a request that may be called binary number of stack pointer for each of looking at any decent assembler. Only target location not work on between these bits to a bit signed or to. The value available hardware running multiple operand instructions and overflow bit. The image of the macro definitions are rules require any time the universality of the c code instruction for in binary format is valid c compiler is not inherently make programs. Connect and may not likely to know which each time, vliw machine code itself provides a custom event on. Clearly hexadecimal value of bits? The binary back to all other than using linear memory locations for each other wires you made. Save its last. There is only one bit longer runs on many bits for binary! Since it was used either statically or logic controller for easy for even in. Structure of engineering stack can also chosen for each thread id number that value for in instruction format, it can be found here, are only one element vector processing.
    [Show full text]
  • Accurate ICP-Based Floating-Point Reasoning
    Accurate ICP-based Floating-Point Reasoning Albert-Ludwigs-Universität Freiburg Karsten Scheibler, Felix Neubauer, Ahmed Mahdi, Martin Fränzle, Tino Teige, Tom Bienmüller, Detlef Fehrer, Bernd Becker Chair of Computer Architecture FMCAD 2016 Context of this Work FMCAD 2016 Karsten Scheibler – Accurate ICP-based FP Reasoning 2 / 67 Context of this Work (1) Cooperation with Industrypartners (AVACS Transfer Project 1): “Accurate Dead Code Detection in Embedded C Code by Arithmetic Constraint Solving” University of Oldenburg: BTC-ES (Oldenburg): Ahmed Mahdi Tino Teige Martin Fränzle Tom Bienmüller University of Freiburg: SICK (Waldkirch): Felix Neubauer Detlef Fehrer Karsten Scheibler Bernd Becker FMCAD 2016 Karsten Scheibler – Accurate ICP-based FP Reasoning 3 / 67 Context of this Work (2) C SMI HYS BTC-Toolchain SMI2iSAT iSAT3 Scripts FMCAD 2016 Karsten Scheibler – Accurate ICP-based FP Reasoning 4 / 67 Context of this Work (3) C SMI HYS BTC-Toolchain SMI2iSAT iSAT3 annotate with coverage goal cone of influenceScripts reduction resolve loops and functions flatten data types static single assignment form BMC problem FMCAD 2016 Karsten Scheibler – Accurate ICP-based FP Reasoning 5 / 67 Context of this Work (4) C SMI HYS BTC-Toolchain SMI2iSAT iSAT3 This presentation: Scripts accurate reasoning for floating-point arithmetic support for bitwise integer operations FMCAD 2016 Karsten Scheibler – Accurate ICP-based FP Reasoning 6 / 67 How does iSAT3 Work FMCAD 2016 Karsten Scheibler – Accurate ICP-based FP Reasoning 7 / 67 iSAT3 = CDCL + ICP CDCL:
    [Show full text]
  • High-Level Synthesis and Arithmetic Optimizations Yohann Uguen
    High-level synthesis and arithmetic optimizations Yohann Uguen To cite this version: Yohann Uguen. High-level synthesis and arithmetic optimizations. Mechanics [physics.med-ph]. Uni- versité de Lyon, 2019. English. NNT : 2019LYSEI099. tel-02420901v2 HAL Id: tel-02420901 https://hal.archives-ouvertes.fr/tel-02420901v2 Submitted on 17 Jul 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. N°d'ordre NNT : 2019LYSEI099 THESE` de DOCTORAT DE L'UNIVERSITE´ DE LYON Op´er´eeau sein de : (INSA de Lyon, CITI lab) Ecole Doctorale InfoMaths EDA 512 (Informatique Math´ematique) Sp´ecialit´ede doctorat : Informatique Soutenue publiquement le 13/11/2019, par : Yohann Uguen High-level synthesis and arithmetic optimizations Devant le jury compos´ede : Fr´ed´ericP´etrot Pr´esident Professeur des Universit´es, TIMA, Grenoble, France Philippe Coussy Rapporteur Professeur des Universit´es, UBS, Lorient, France Olivier Sentieys Rapporteur Professeur des Universit´es, Univ. Rennes, Inria, IRISA, Rennes Laure Gonnord Examinatrice Ma^ıtrede conf´erence, Universit´eLyon 1, France Martin Kumm Examinateur Professeur des Universit´es, Universit´ede Fulda, Allemagne Florent de Dinechin Directeur de th`ese Professeur des Universit´es, INSA Lyon, France Cette thèse est accessible à l'adresse : http://theses.insa-lyon.fr/publication/2019LYSEI099/these.pdf © [Y.
    [Show full text]
  • University of Warwick Institutional Repository
    CORE Metadata, citation and similar papers at core.ac.uk Provided by Warwick Research Archives Portal Repository University of Warwick institutional repository: http://go.warwick.ac.uk/wrap A Thesis Submitted for the Degree of PhD at the University of Warwick http://go.warwick.ac.uk/wrap/3713 This thesis is made available online and is protected by original copyright. Please scroll down to view the document itself. Please refer to the repository record for this item for information to help you to cite it. Our policy information is available from the repository home page. Addressing Concerns in Performance Prediction: The Impact of Data Dependencies and Denormal Arithmetic in Scientific Codes by Brian Patrick Foley A thesis submitted to the University of Warwick in partial fulfilment of the requirements for admission to the degree of Doctor of Philosophy Department of Computer Science September 2009 Abstract To meet the increasing computational requirements of the scientific community, the use of parallel programming has become commonplace, and in recent years distributed applications running on clusters of computers have become the norm. Both parallel and distributed applications face the problem of predictive uncertainty and variations in runtime. Modern scientific applications have varying I/O, cache, and memory profiles that have significant and difficult to predict effects on their runtimes. Data-dependent sensitivities such as the costs of denormal floating point calculations introduce more variations in runtime, further hindering predictability. Applications with unpredictable performance or which have highly variable run- times can cause several problems. If the runtime of an application is unknown or varies widely, workflow schedulers cannot efficiently allocate them to compute nodes, leading to the under-utilisation of expensive resources.
    [Show full text]