Automatically Achieving Elasticity in the Implementation of Programming Languages Michael Lee Horowitz
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Featuring Independent Software Vendors
Software Series 32000 Catalog Featuring Independent Software Vendors Software Series 32000 Catalog Featuring Independent Software Vendors .. - .. a National Semiconductor Corporation GENIX, Series 32000, ISE, SYS32, 32016 and 32032 are trademarks of National Semiconductor Corp. IBM is a trademark of International Business Machines Corp. VAX 11/730, 750, 780, PDP-II Series, LSl-11 Series, RSX-11 M, RSX-11 M PLUS, DEC PRO, MICRO VAX, VMS, and RSTS are trademarks of Digital Equipment Corp. 8080, 8086, 8088, and 80186 are trademarks of Intel Corp. Z80, Z80A, Z8000, and Z80000 are trademarks of Zilog Corp. UNIX is a trademark of AT&T Bell Laboratories. MS-DOS, XENIX, and XENIX-32 are trademarks of Microsoft Corp. NOVA, ECLIPSE, and AoS are trademarks of Data General Corp. CP/M 2.2, CP/M-80, CP/M-86 are registered trademarks of Digital Research, Inc. Concurrent DOS is a trademark of Digital Research, Inc. PRIMOS is a trademark of Prime Computer Corp. UNITY is a trademark of Human Computing Resources Corp. Introduction Welcome to the exciting world of As a result, the Independent software products for National's Software Vendor (ISV) can provide advanced 32-bit Microprocessor solutions to customer software family, Series 32000. We have problems by offering the same recently renamed our 32-bit software package independent of microprocessor products from the the particular Series 32000 CPU. NS16000 family to Series 32000. This software catalog was This program was effective created to organize, maintain, and immediately following the signing disseminate Series 32000 software of Texas Instruments, Inc. as our information. It includes the software second source for the Series vendor, contact information, and 32000. -
Solving the Funarg Problem with Static Types IFL ’21, September 01–03, 2021, Online
Solving the Funarg Problem with Static Types Caleb Helbling Fırat Aksoy [email protected] fi[email protected] Purdue University Istanbul University West Lafayette, Indiana, USA Istanbul, Turkey ABSTRACT downwards. The upwards funarg problem refers to the manage- The difficulty associated with storing closures in a stack-based en- ment of the stack when a function returns another function (that vironment is known as the funarg problem. The funarg problem may potentially have a non-empty closure), whereas the down- was first identified with the development of Lisp in the 1970s and wards funarg problem refers to the management of the stack when hasn’t received much attention since then. The modern solution a function is passed to another function (that may also have a non- taken by most languages is to allocate closures on the heap, or to empty closure). apply static analysis to determine when closures can be stack allo- Solving the upwards funarg problem is considered to be much cated. This is not a problem for most computing systems as there is more difficult than the downwards funarg problem. The downwards an abundance of memory. However, embedded systems often have funarg problem is easily solved by copying the closure’s lexical limited memory resources where heap allocation may cause mem- scope (captured variables) to the top of the program’s stack. For ory fragmentation. We present a simple extension to the prenex the upwards funarg problem, an issue arises with deallocation. A fragment of System F that allows closures to be stack-allocated. returning function may capture local variables, making the size of We demonstrate a concrete implementation of this system in the the closure dynamic. -
Dynamic Variables
Dynamic Variables David R. Hanson and Todd A. Proebsting Microsoft Research [email protected] [email protected] November 2000 Technical Report MSR-TR-2000-109 Abstract Most programming languages use static scope rules for associating uses of identifiers with their declarations. Static scope helps catch errors at compile time, and it can be implemented efficiently. Some popular languages—Perl, Tcl, TeX, and Postscript—use dynamic scope, because dynamic scope works well for variables that “customize” the execution environment, for example. Programmers must simulate dynamic scope to implement this kind of usage in statically scoped languages. This paper describes the design and implementation of imperative language constructs for introducing and referencing dynamically scoped variables—dynamic variables for short. The design is a minimalist one, because dynamic variables are best used sparingly, much like exceptions. The facility does, however, cater to the typical uses for dynamic scope, and it provides a cleaner mechanism for so-called thread-local variables. A particularly simple implementation suffices for languages without exception handling. For languages with exception handling, a more efficient implementation builds on existing compiler infrastructure. Exception handling can be viewed as a control construct with dynamic scope. Likewise, dynamic variables are a data construct with dynamic scope. Microsoft Research Microsoft Corporation One Microsoft Way Redmond, WA 98052 http://www.research.microsoft.com/ Dynamic Variables Introduction Nearly all modern programming languages use static (or lexical) scope rules for determining variable bindings. Static scope can be implemented very efficiently and makes programs easier to understand. Dynamic scope is usu- ally associated with “older” languages; notable examples include Lisp, SNOBOL4, and APL. -
On the Expressive Power of Programming Languages for Generative Design
On the Expressive Power of Programming Languages for Generative Design The Case of Higher-Order Functions António Leitão1, Sara Proença2 1,2INESC-ID, Instituto Superior Técnico, Universidade de Lisboa 1,2{antonio.menezes.leitao|sara.proenca}@ist.utl.pt The expressive power of a language measures the breadth of ideas that can be described in that language and is strongly dependent on the constructs provided by the language. In the programming language area, one of the constructs that increases the expressive power is the concept of higher-order function (HOF). A HOF is a function that accepts functions as arguments and/or returns functions as results. HOF can drastically simplify the programming activity, reducing the development effort, and allowing for more adaptable programs. In this paper we explain the concept of HOFs and its use for Generative Design. We then compare the support for HOF in the most used programming languages in the GD field and we discuss the pedagogy of HOFs. Keywords: Generative Design, Higher-Order Functions, Programming Languages INTRODUCTION ming language is Turing-complete, meaning that Generative Design (GD) involves the use of algo- almost all programming languages have the same rithms that compute designs (McCormack 2004, Ter- computational power. In what regards their expres- dizis 2003). In order to take advantage of comput- sive power, however, they can be very different. A ers, these algorithms must be implemented in a pro- given programming language can be textual, visual, gramming language. There are two important con- or both but, in any case, it must be expressive enough cepts concerning programming languages: compu- to allow the description of a large range of algo- tational power, which measures the complexity of rithms. -
Closure Joinpoints: Block Joinpoints Without Surprises
Closure Joinpoints: Block Joinpoints without Surprises Eric Bodden Software Technology Group, Technische Universität Darmstadt Center for Advanced Security Research Darmstadt (CASED) [email protected] ABSTRACT 1. INTRODUCTION Block joinpoints allow programmers to explicitly mark re- In this work, we introduce Closure Joinpoints for As- gions of base code as \to be advised", thus avoiding the need pectJ [6], a mechanism that allows programmers to explicitly to extract the block into a method just for the sake of cre- mark any Java expression or sequence of statement as \to ating a joinpoint. Block joinpoints appear simple to define be advised". Closure Joinpoints are explicit joinpoints that and implement. After all, regular block statements in Java- resemble labeled, instantly called closures, i.e., anonymous like languages are constructs well-known to the programmer inner functions with access to their declaring lexical scope. and have simple control-flow and data-flow semantics. As an example, consider the code in Figure1, adopted Our major insight is, however, that by exposing a block from Hoffman's work on explicit joinpoints [24]. The pro- of code as a joinpoint, the code is no longer only called in grammer marked the statements of lines4{8, with a closure its declaring static context but also from within aspect code. to be \exhibited" as a joinpoint Transaction so that these The block effectively becomes a closure, i.e., an anonymous statements can be advised through aspects. Closure Join- function that may capture values from the enclosing lexical points are no first-class objects, i.e., they cannot be assigned scope. -
CONS Should Not CONS Its Arguments, Or, a Lazy Alloc Is a Smart Alloc Henry G
CONS Should not CONS its Arguments, or, a Lazy Alloc is a Smart Alloc Henry G. Baker Nimble Computer Corporation, 16231 Meadow Ridge Way, Encino, CA 91436, (818) 501-4956 (818) 986-1360 (FAX) November, 1988; revised April and December, 1990, November, 1991. This work was supported in part by the U.S. Department of Energy Contract No. DE-AC03-88ER80663 Abstract semantics, and could profitably utilize stack allocation. One would Lazy allocation is a model for allocating objects on the execution therefore like to provide stack allocation for these objects, while stack of a high-level language which does not create dangling reserving the more expensive heap allocation for those objects that references. Our model provides safe transportation into the heap for require it. In other words, one would like an implementation which objects that may survive the deallocation of the surrounding stack retains the efficiency of stack allocation for most objects, and frame. Space for objects that do not survive the deallocation of the therefore does not saddle every program with the cost for the surrounding stack frame is reclaimed without additional effort when increased flexibility--whether the program uses that flexibility or the stack is popped. Lazy allocation thus performs a first-level not. garbage collection, and if the language supports garbage collection The problem in providing such an implementation is in determining of the heap, then our model can reduce the amortized cost of which objects obey LIFO allocation semantics and which do not. allocation in such a heap by filtering out the short-lived objects that Much research has been done on determining these objects at can be more efficiently managed in LIFO order. -
IDL (Interface Description Language). Background and Status
!-MOO 922 IDL (INTERFRCE DESCRIPTION LANGUAGE) BACKGROUND AM IVA STATUS(U) CARNEGIE-NELLON UNIV PITTSBURGH PA SOFTINE ENGINEERING INST.. D L STONE ET AL. DEC 87 UNCLRSSIFIEDleaeeaaaaaaEI CNAAI-97-TR-47 ESD-TR-07-219 F/G 12/5 NL 11111w11l I~~hI~r MSe 11111 2115 W_ *EI5E 1*4 TechnicalCMU/SEI-87-TR.-47 Report i' ESO-TR-87-210 %. Carneg ie-Melon Universt, .ri Software Engineering Institute 4\4 'F 1P IDL: Background and Status I, Donald L. Stone John R. Nestor N December 1987 t"IC N, FESLECTE 0 a s-W COII D tMUIN STAMWE ,fOr Publi ___ %- .- * 58 01 113 Ire. - -'. Technical Report CMU/SEI-87-TR-47 ESD-TR-87-210 December 1987 IDL: Background and Status Donald L. Stone John Nestor Accesion For NTIS CRA&I ' DTIC TAB " Unanno,;: ced 0 Justif,catcn . By ....... .......... Di~t ," Approed for public release. Distribution unlimited. Software Engineering institute Carnegie Mellon University Pittsburgh, Pennsylvania 15213 This technical report was prepared for the SEt Joint Program Office ESD/XRS Hanscom AFB. MA 01731 The ideas and findings in this report should not be construed as an off icial DoD position. It is published inthe Interest of scientific and technical information exchange. Review and Approval This report has been reviewed and is approved for publication. FOR THE COMMANDER Karl Shinglerr SEl Joint Program Office ,J This work was sponsored by the U.S. Department of Defense. This document is avafthls through 1he Defense Tchnical Information Cente. DTIC provides access io and transfer of scientific and lischnical information for DoD personinel. -
Functional Programming Is Free
Functional Programming is Free. Francis Sergeraert January 2015 Abstract A paper [5] has recently been published. This paper is faulty: 1) The standard requirements about the definition of an algorithm are not respected, 2) The main point in the complexity study, namely the functional programming component, is absent. The Editorial Board of the SIAM JC had been warned a confirmed publication would be openly commented, it is the role of this text1. 1 History of the subject. I started the subject of Effective Homology in 1984, trying first to implement the Edgar Brown’s “algorithm” [4] computing the homotopy groups of the finite simply connected simplicial sets. This idea was quickly understood as ridiculous; thirty years later, there remains difficult to hope for an actual implementation of this algorithm, much too complex. Other methods were soon imagined to overcome the essential difficulty, con- sisting in using the strange power of the functional programming. Julio Rubio’s contribution was essential to make the project succeed: he understood in 1989 that the best theoretical tool to organize the work is the so-called Basic Perturbation Lemma [9, 3], more and more often called now the Homological Perturbation The- orem, you may question Google(Galway perturbation) to see numerous examples. Using Julio’s idea, a program called EAT (Effective Algebraic Topology) was soon obtained in 1990, mainly devoted to the computation of the homology groups of iterated loop spaces, already allowing us to produce homology groups so far arXiv:1504.00527v1 [cs.PL] 2 Apr 2015 unreachable by the “standard” methods. In the years 1995-8, Xavier Dousson’s thesis was devoted to the calculation of homotopy groups; the same tools, functional programming combined with the homological perturbation theorem, were successfully applied to the so-called White- head and Postnikov towers, the most “elementary” methods to compute the ho- motopy groups. -
CSE583: Programming Languages Functional Programming
Functional programming: two weeks G Scheme – Gives a strong, language-based foundation CSE583: for functional programming Programming Languages – May be mostly review for some of you G Some theory David Notkin – Theoretical foundations and issues in 11 January 2000 functional programming [email protected] G ML http://www.cs.washington.edu/education/courses/583 – A modern basis for discussing key issues in functional programming University of Washington • CSE583 • D. Notkin © 2000 2 Scheme: a Lisp descendant Statically scoped (review) G A statically scoped and properly tail-recursive G A free variable in a proc fork(x,y:int){ function definition is proc snork(a:int){ dialect of the Lisp programming language proc cork() { bound to the variable developed by Steele and Sussman in the mid a := x + y; of the same name in 1970s } the closest enclosing x : int; – Embodies an executable version of the lamda block a := x + y; } calculus G Dynamically scoped: } G Intended to have an exceptionally clear and free variable bound to simple semantics with few different ways to nearest match on the form expressions call stack University of Washington • CSE583 • D. Notkin © 2000 3 University of Washington • CSE583 • D. Notkin © 2000 4 Tail recursive Scheme G The top level call (define member(e l) G Dynamically typed, strongly typed (cond returns a value G identical to that of (null l) #f Expression-oriented, largely side-effect- the bottom level (equal e (car l)) #t free call (member e (cdr l)) – Functional languages are expression, not G Who cares? )) statement-oriented, since the expression – Performance! G Once an element is a member, define the function computation it’s always a member – Scheme requires G List-oriented, garbage-collected heap- G tail recursion to be Once you start returning true, based implemented as you keep on returning true until efficiently as you unwind the whole recursive G Good compilers exist iteration call stack University of Washington • CSE583 • D. -
Some History of Functional Programming Languages
Some History of Functional Programming Languages D. A. Turner University of Kent & Middlesex University Abstract. We study a series of milestones leading to the emergence of lazy, higher order, polymorphically typed, purely functional program- ming languages. An invited lecture given at TFP12, St Andrews Univer- sity, 12 June 2012. Introduction A comprehensive history of functional programming languages covering all the major streams of development would require a much longer treatment than falls within the scope of a talk at TFP, it would probably need to be book length. In what follows I have, firstly, focussed on the developments leading to lazy, higher order, polymorphically typed, purely functional programming languages of which Haskell is the best known current example. Secondly, rather than trying to include every important contribution within this stream I focus on a series of snapshots at significant stages. We will examine a series of milestones: 1. Lambda Calculus (Church & Rosser 1936) 2. LISP (McCarthy 1960) 3. Algol 60 (Naur et al. 1963) 4. ISWIM (Landin 1966) 5. PAL (Evans 1968) 6. SASL (1973{83) 7. Edinburgh (1969{80) | NPL, early ML, HOPE 8. Miranda (1986) 9. Haskell (1992 . ) 1 The Lambda Calculus The lambda calculus (Church & Rosser 1936; Church 1941) is a typeless theory of functions. In the brief account here we use lower case letters for variables: a; b; c ··· and upper case letters for terms: A; B; C ···. A term of the calculus is a variable, e.g. x, or an application AB, or an abstraction λx.A for some variable x. In the last case λx. -
Register Reassociation in PA-RISC Compilers, by Vatsa Santhanam
H E W L E T-PA C K A R D JOURNAL June 1992 N/sc HEWLETT PACKARD © Copr. 1949-1998 Hewlett-Packard Co. HEWLETT-PACKARD JOURNAL June 1992 Volume 43 • Number 3 Articles ¡ HP-UX Karen System Kernel Support for the HP 9000 Series 700 Workstations, by Karen Kerschen and Jeffrey R. G/asson I An Example of the FTEST Instruction Providing HP-UX Kernel Functionality on a New PA-RISC Architecture, by Donald E. Bollinger, Frank P. Lemmon, and Dawn L. Yamine ^ New Optimizations for PA-RISC Compilers, by Robert C. Hansen // Link-Time Optimizations 7 A HP 9000 Series 700 FORTRAN Optimizing Preprocessor, by Roben A. Gottlieb, DanielJ. Magenheimer, Sue A. Meloy, and Alan C. Meyer Vector Library Q Q Register Reassociation in PA-RISC Compilers, by Vatsa Santhanam ^< Q Software Pipelining in PA-RISC Compilers, by Sridhar Ramakrishnan At ¡ Shared Libraries for HP-UX, by CaryA. Coutant and Michelle A. Ruscetta Deferred Binding, Relocation, and Initialization of Shared Library Data Editor, Richard R Dolan • Associate Editor, Charles L Leath • Publication Production Manager, Susan E. Wright • Illustration, Renée D. Pighini Typography/Layout, Rita C Smith • Test and Measurement Organization Liaison. J Michael Gospe Advisory Harry William W. Brown, Integrated Circuit Business Division, Santa Clara. California • Harry Chou, Microwave Technology Division, Santa Rosa, California • Rajesh Gordon. Waltham, Systems Division, Cupertino, California Gary Gordon. HP Laboratories, Palo Alto. California* Jim Grady, Waltham Division. Waltham, Massachusetts • Man J. Marline, Systems Technology Division. Roseville, California • Roger L Jungerman, Microwave Technology Division. -
An Extension of Lambda-Calculus for Functional Programming*
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector J. LOGIC PROGRAMMING 1984:3:241-251 241 AN EXTENSION OF LAMBDA-CALCULUS FOR FUNCTIONAL PROGRAMMING* GYORGY REVESZ D An implementation oriented modification of lambda-calculus is presented together with some additional conversion rules for list manipulations. The resulting set of axioms is very simple and provides for a theoretical foundation of the semantics of functional programming. At the same time it can be used directly for the implementation of a powerful graph-reduction algorithm. <i INTRODUCTION Here we assume familiarity with the basics of lambda-calculus and with the main ideas of functional programming as promulgated by Backus. [2] The efforts to implement functional languages have come up with various ideas and approaches. There are basically two different groups of efforts: The first group is trying to solve the problem by using lambda-calculus reduction or combinatory reduction for function evaluation. (See, for instance, [4] and 171.1 The second group is trying to use more specialized techniques tailored to the given functional language. (See, for instance, [5].) In both cases the function to be evaluated is usually represented as a graph (sometimes a tree) and thus, the function evaluation process can be seen as a graph manipulation. Each step of the function evaluation will be reflected by a correspond- ing change in the graph and the entire graph should eventually collapse to a single node if the function evaluates to a constant. Now, the question is how best to represent a function via a graph and then how to make each step in a reasonably efficient manner.