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Chapt.2 Machine Architecture Impact of languages –Support – faster, more secure Primitive Operations –e.g. nested subroutine calls »Subroutines implemented.

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Presentation on theme: "Chapt.2 Machine Architecture Impact of languages –Support – faster, more secure Primitive Operations –e.g. nested subroutine calls »Subroutines implemented."— Presentation transcript:

1 Chapt.2 Machine Architecture Impact of languages –Support – faster, more secure Primitive Operations –e.g. nested subroutine calls »Subroutines implemented with stack (ex: PL/1- recursion) IBM704 – index registers Security –e.g., data and code kept separate –O.S. kept separate –Limitations Space Time –Networks, multiprocessors, etc.

2 Hardware or actual computer Data / storage CELL –Main memory, cache and registers –Fixed-length words determines range of values of data Limitation on range and precision of numbers –Strictly speaking, built-in types are determined by operations Operations on data –Primitive operations manipulate data Integer, character, bit string, (1-dimensional) arrays Possibly floating-point, character string –Data operations are valid on all data cells

3 Hardware Sequence control (control operations) –Sequence (von Neumann architecture) Program address register (PC) incremented automatically –Jump or branch –Branch conditionally –Multiprocessing as alternative control mechanism Data access –Operation (move, load, etc.) –Operand – typically address of cell RISC (fewer hardware ops) vs CISC

4 Hardware Effect of current access times –Register access – 5-10 nsec –Memory access – nsec –I/O access – msec. Modular programs can make efficient use of cache and virtual memory (hits) Cost of tasking In-line modules Multi-dimensional arrays

5 Computer states State transitions for representing virtual machine State transition diagrams for proving correctness

6 Role of firmware Instructions in microprogram Emulation to create virtual computer FORTRAN hardware machine –Statements in hardware/firmware Translator is interpreter Cost in terms of flexibility, monetary, speed –Slower for running spreadsheet, for example

7 Translators Translators allow for the creation of virtual machines A program language defines its own machine –May restrict data operations e.g., pure LISP does not have use floating point operations –May restrict control operations Typically does not allow use of primitive i/o operations –Adds its own data and control structures

8 Translators Interpreter – decodes and executes each hardware machine instruction or higher level statement (initially Basic, Snobol, LISP, Perl, Smalltalk, Java) –Does not create object code –Each statement (even in a loop) must be repeatedly translated Assembler – specific to hardware; translates almost 1-1 to hardware machine instruction

9 Translators Virtual machine is portable, user friendly(?) Compiler creates object code in assembler or hardware machine language –Quick and dirty –Optimizing Cross compiler – translates to machine language of another machine (simulation) –Useful for designing code for small chips Load or link editor - source code is typically relocatable and object code is a single executable program with external references resolved Preprocessor or macroprocessor –Source and object code both in high level language –Initial pass for expanding macros, constants, C++, etc.

10 Hierarchies of virtual machines Hardware machine- gates, switches Augmented by microcode Operating System virtual machine –Denies some functions –Adds some capabilities e.g., i/o, semaphores C virtual computer –Hides/ adds capabilities –C library routines Web virtual machine –Browser executing HTML, XML pages

11 Binding Times Binding of data: association with cell(s) Binding of operation: association with hardware primitive operation(s) Typically several intermediate steps Flexibility versus efficiency/ reliability –Late binding provides greater flexibility, less efficiency, typically less reliability

12 Binding Times –During program execution Module entry- semi-dynamic Arbitrary run times – dynamic –During compile/ translation time Bindings chosen by the programmer –Ex: types and their operations –Array and record size Bindings chosen by the translator –Ex: position of data on the stack Bindings chosen by the loader, linker –Ex: storage location or displacement

13 Binding Times During language implementation for specific hardware –possibly number implementation Cs int Issue of portability During language definition type –The ability to define more operations for a type –The ability to define new data types

14 Binding Times: example Statement X = X + 10 –Association of Type with X Language definition determines possible range of types (int, short int, float, complex, user defined, etc.) Xs type may be chosen statically (C), or dynamically (SNOBOL) –Association of Value with X Language implementation may determine range and representation of value Xs value is determined at arbitrary pts of execution –Association of + Language definition determines what + can be (or sum, etc.) Binding of + to hardware or software op –Compile time in FORTRAN, C –At arbitrary pts of execution in C++ – polymorphism

15 Binding time: example static int X = 10; –In C Xs range of types, meaning of = are determined at language definition X is assigned 10 only upon first execution X retains value between calls

16 Do we need to know about the hardware? High speed, large memories shield us from many of the problems that earlier programmers had Speed of translating and executing Java is small in relation to network transmission, user response time Can careful language design shield us from hardware? – not entirely


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