Supposedly Forth programs can be "compiled" but I don't see how that is true if they have words that are only evaluated at runtime. For example, there is the word DOES> which stores words for evaluation at runtime. If those words include an EVALUATE or INTERPRET word then there will be a runtime need for the dictionary.
To support such statements it would mean the entire word list (dictionary) would have to be embedded inside the program, essentially what interpreted programs (not compiled programs) do.
This would seem to prevent you from compiling small programs using Forth because the entire dictionary would have to be embedded in the program, even if you used only a fraction of the words in the dictionary.
Is this correct, or is there some way to compile Forth programs without embedding the dictionary? (maybe by not using runtime words at all ??)
Forth programs can be compiled with or without word headers. The headers include the word names (called "name space").
In the scenario you describe, where the program may include run-time evalutation calls such as EVALUATE, the headers will be needed.
The dictionary can be divided into three logically distinct parts: name space, code space, and data space. Code and data are needed for program execution, names are usually not.
A normal Forth program will usually not do any runtime evaluation. So in most cases, the names aren't needed in a compiled program.
The code after DOES> is compiled, so it's not evaluated at run time. It's executed at run time.
Even though names are included, they usually don't add much to program size.
Many Forths do have a way to leave out the names from a program. Some have a switch to remove word headers (the names). Other have cross compilers which keep the names in the host system during compile time, but generate target code without names.
No, the entire dictionary need not be embedded, nor compiled. All that need remain is just the list of words used, and their parent words, (& grandparents, etc.). And the even names of the words aren't necessary, the word locations are enough. Forth code compiled by such methods can be about as compact as it gets, rivaling or even surpassing assembly language in executable size.
Proof by example: Tom Almy's ForthCMP, an '80s-'90s MSDOS compiler that shrunk executable code way down. Its README says:
. Compiles Forth into machine code -- not interpreted.
. ForthCMP is written in Forth so that Forth code can be executed
during compilation, as is customary in Forth applications.
. Very fast -- ForthCMP compiles Forth code into an executable file
in a single pass.
. Generated code is extremely compact. Over 110 Forth "primitives"
are compiled in-line. ForthCMP performs constant expression
folding, strength reduction, register optimization, DO...LOOP
optimization, tail recursion, and various "peephole"
optimizations.
. Built-in assembler.
4C.COM runs under emulators like dosemu or dosbox.
A "Hello World" compiles into a 117 byte .COM file, a wc program compiles to a 3K .COM file (from 5K of source code). No dictionary or external libraries, (aside from standard MSDOS calls, i.e. the OS it runs on).
Forth can be a bear to get your head around from the outside because there is NO standard implementation of the language. Much of what people see are from the early days of Forth when the author (Charles Moore) was still massaging his own thoughts. Or worse, homemade systems that people call Forth because it has a stack but are really not Forth.
So is Forth Interpreted or Compiled?
Short answer: both
Early years:
Forth had a text interpreter facing the programmer. So Interpreted: Check
But... The ':' character enabled the compiler which "compiled" the addresses of the words in the language so it was "compiled" but not as native machine code. It was lists of addresses where the code was in memory. The clever part was that those addresses could be run with a list "interpreter" that was only 2 or 3 instructions on most machines and a few more on an old 8 bit CPU. That meant it was still pretty fast and quite space efficient.
These systems are more of an image system so yes the system goes along with your program but some of those system kernels were 8K bytes for the entire run-time including the compiler and interpreter. Not heavy lifting.
This is what most people think of as Forth. See JonesForth for a literate example. (This was called "threaded code" at the time, not to be confused with multi-threading)
1990ish
Forth gurus and Chuck Moore began to realize that a Forth language primitive could be as little as one machine instruction on modern machines so why not just compile the instruction rather than the address. This became very useful with 32bit machines since the address was sometimes bigger than the instruction. They could then replace the little 3 instruction interpreter with the native CALL/Return instructions of the processor. This was called sub-routine threading. The front end interpreter did not disappear. It simply kicked off native code sub-routines
Today
Commercial Forth systems generate native code, inline many/most primitives and do many of the other optimization tricks you see in modern compilers.
They still have an interpreter facing the programmer. :-)
You can also buy (or build) Forth cross-compilers that create standalone executables for different CPUs that include multi-tasking, TCP/IP stacks and guess what, that text interpreter can be compiled into the executable as an option for remote debugging and configuration if you want it.
So is Forth Interpreted or Compiled? Still both.
You are right that a program that executes INTERPRET (EVALUATE, LOAD, INCLUDE etc.) is obliged to have a dictionary. That is hardly a disadvantage because even a 64 bit executable is merely a 50 K for Linux or MS-Windows. Modern single board computer like the MSP430 can have the whole dictionary in flash memory. See ciforth and noforth respectively. Then there is scripting. If you use Forth as a scripting language, it is similar to perl or python The script is small, and doesn't contain the whole language. It requires though that the language is installed on your computer.
In case of really small computers you can resort to cross compiling or using an umbellical Forth where the dictionary is positioned on a host computer and communicates and programs via a serial line. These are special techniques that are normally not needed. You can't use INTERPRETing code in those cases on the sbc, because obviously there is no dictionary there.
Note: mentioning the DOES> instruction doesn't serve to make the question clearer. I recommend that you edit this out.
Related
If so, why different programs written in different languages have different execution speeds?
Simple answer: they don't produce the same machine code. They might produce different machine code which still produces the same side effects (same end result), but via different machine instructions.
Imagine you have two interpreters (let's say male and female just to distinguish them) to translate what you say into some other language. Both of them may translate what you say properly into the desired language, but they won't necessarily be equally efficient. One of them might feel the need to explain more of what you meant, one might be very terse and translate what you say in a very short and sweet way.
Performance doesn't just vary between languages. They vary between compilers for the same programming language.
For example, with C, the performance difference between GCC and Tiny-C can be about 2 to 3x, with Tiny-C being roughly 2-3 times slower.
And it's because even within the same programming language (C), GCC and Tiny-C don't produce identical machine instructions. In the case of Tiny-C, it was optimized to compile quickly, not to produce code that runs as quickly. For example, it doesn't make the best use of the fastest form of memory available to the machine (registers) and spills more data into the stack (which uses anything from L1 to DRAM depending on the access patterns). Because it doesn't bother to get so fancy with register allocation, Tiny-C can compile code quite quickly, but the resulting code isn't as efficient.
If you want a more in-depth answer, then you should study compiler design starting with the Dragon Book.
Though programs written in different languages are converted into machine code at the end of the day, different languages have different implementation to say same thing.
You can take analogy from human languages e.g the English statement I am coming home. is translated to Chinese as 我未来的家。, as you can see the Chinese one is more concise though it is not always true; same concept applies to programming languages.
So in the case of programming languages a machine code X can be written in programming language A as 2X-X, programming language B as X/2 + X/2...but executing machine code X and 2X-X will result same result though their performance wont same ( this is hypothetical example but hope it makes sense.)
Basically it is not guaranteed that a program with same output written in different programming languages results in same machine code, but is converted into a machine code that gives same output, that where the difference comes.
But this will give you thorough info
Because 1) the compilers are written by different people so the machine code they generate is not the same, and 2) they make use of preexisting run-time libraries of routines to do math, input-output, memory management, and more, and those libraries are also not the same, for the same reason.
Some compilers do not generate machine code, because then the resulting code would not be portable to different machines, so instead they generate code for a fictitious general computer.
Then on any particular machine that code is either interpreted directly by an interpreter program, or it is translated into that machine's code, or a combination of these (look up just-in-time(JIT) compiler).
i have a question concerning computer programming. Let's say i have only one computer with no OS running. And would like to start to "develop" an OS. basically what i have is a blank sheet an a pen to do so. an a couple of electronic devices. how do i put my instruction into that computer?
because today we use interpreter of compiler that "turn" programming language into what they call "machine code". But my question could be how to generate machine code from nowhere.
Thank you for your replies, a link to learn how to do that will be must welcome.
The first computers where programmed making the "machine code" directly. Just punching one's an zeros into cards (well, in fact they punched octal digits).
This was done that way until somebody thought it would be a nice idea to have an assembler which translated the machine code instructions into that ones and zeros.
After that, another guy thought that it can be very nice idea to have a programming language, who will translate "top level" instructions to machine code.
And after that, or probably at the same time, some "internal procedures" where created to ease the programming: to open a file, to close a file the only thing you have to do is to call an internal subroutine in the machine instead of programming all the open file and close file subroutines by yourself: the seed for the operating systems was planted.
The cross compiling issue that is commented here is the way to create an operating system for a new computer nowadays: you use a working computer as a "lever" to create an operating system for a new computer.
it depends on how far back you want to go. the earliest ones "programming" was moving wires from one essentially analog alu to another.
The woman/women programming at this point were called computers and used use pencil and paper.
later you use a pencil and paper and the datasheet/documentation for the instruction set. assemble by hand basically, there are no compilers or even the concept of a programming language at this point, this has to evolve still. you wrote down the ones and zeros in whatever form you preferred (binary or octal).
one way to enter code at this point is with switches. certainly computers predated it but look for a picture of the front panel of a pdp8 or the altair, etc. you set the switches for the data value and address, and you manually strobe a write. you load the bootstrap in this way and/or the whole program. set the start address and switch to run mode.
over time they developed card and tape readers for which you loaded the bootstrap in by hand (switches) then you could use a reader to load larger programs easier. cards could be punched on a typewriter type thing, literally a keyboard but instead of striking through a ribbon onto paper, it cut slots in a card.
oses and programming languages started to evolve at this point. until you bootstrapped your compiler you had to write the first compiler for a new language in some other language (no different than today). so the first assembler had to be in machine code, then from assembler you could create some other language and so on.
If you wanted to repeat something like this today you would have to build a computer with some sort of manual input. you could certainly but you would have to design it that way, like then you need the debouncing out but you could for example have a processor with an external flash, be it parallel or serial, mux the lines to the switches (a switch controls the mux) and either address/data/write your program, or for fun could use a spi flash and serially load the program into the flash. much better to just use one of the pdp or altair, etc online simulators to get a feel for the experience.
there is no magic here, there is no chicken and egg problem at all. humans had to do it by hand before the computer could do it. a smaller/simpler program had to generate more complicated programs, and so on. this long, slow, evolution is well documented all over the internet and in books in libraries everywhere.
Computers are based on a physical processor which was designed to accept instructions (eg. in assembly code) that only allowed primitive instructions like shift, move, copy, add. This processor decided how it spoke (eg. how big were the words (8-bit) and and other specs (speed/standards etc). Using some type of storage, we could store the instructions (punch cards, disk) and execute huge streams of these instructions.
If instructions were repeated over and over, you could move to an address and execute what was at that location and create loops and other constructs (branches, context switches, recursion).
Since you would have peripherals, you would have some kind of way to interface with it (draw, print dots), and you could create routines to build up on that to build letters, fonts, boxes, lines. Then you could run a subroutine to print the letter 'a' on screen..
An OS is basically a high-level implementation of all those lower level instructions. It is really a collection of all the instructions to interface with different areas (i/o, computations etc). Unix is a perfect example of different folks working on different areas and plugging them all into a single OS.
I ve written a code in C for ATmega128 and
I d like to know how the changes that I do in the code influence the Program Memory.
To be more specific, let's consider that the code is similar to that one:
d=fun1(a,b);
c=fun2(c,d);
the change that I do in the code is that I call the same functions more times e.g.:
d=fun1(a,b);
c=fun2(c,d);
h=fun1(k,l);
n=fun2(p,m);
etc...
I build the solution at the AtmelStudio 6.1 and I see the changes in the Program Memory.
Is there anyway to foresee, without builiding the solution, how the chages in the code will affect the program memory?
Thanks!!
Generally speaking this is next to impossible using C/C++ (that means the effort does not pay off).
In your simple case (the number of calls increase), you can determine the number of instructions for each call, and multiply by the number. This will only be correct, if the compiler does not inline in all cases, and does not apply optimzations at a higher level.
These calculations might be wrong, if you upgrade to a newer gcc version.
So normally you only get exact numbers when you compare two builds (same compiler version, same optimisations). avr-size and avr-nm gives you all information, for example to compare functions by size. You can automate this task (by converting the output into .csv files), and use a spreadsheet or diff to look for changes.
This method normally only pays off, if you have to squeeze a program into a smaller device (from 4k flash into 2k for example - you already have 128k flash, that's quite a lot).
This process is frustrating, because if you apply the same design pattern in C with small differences, it can lead to different sizes: So from C/C++, you cannot really predict what's going to happen.
Isn't every language compiled into low-level computer language?
If so, shouldn't all languages have the same performance?
Just wondering...
As pointed out by others, not every language is translated into machine language; some are translated into some form (bytecode, reverse Polish, AST) that is interpreted.
But even among languages that are translated to machine code,
Some translators are better than others
Some language features are easier to translate to high-performance code than others
An example of a translator that is better than some others is the GCC C compiler. It has had many years' work invested in producing good code, and its translations outperform those of the simpler compilers lcc and tcc, for example.
An example of a feature that is hard to translate to high-performance code is C's ability to do pointer arithmetic and to dereference pointers: when a program stores through a pointer, it is very difficult for the compiler to know what memory locations are affected. Similarly, when an unknown function is called, the compiler must make very pessimistic assumptions about what might happen to the contents of objects allocated on the heap. In a language like Java, the compiler can do a better job translating because the type system enforces greater separation between pointers of different types. In a language like ML or Haskell, the compiler can do better still, because in these languages, most data allocated in memory cannot be changed by a function call. But of course object-oriented languages and functional languages present their own translation challenges.
Finally, translation of a Turing-complete language is itself a hard problem: in general, finding the best translation of a program is an NP-hard problem, which means that the only solutions known potentially take time exponential in the size of the program. This would be unacceptable in a compiler (can't wait forever to compile a mere few thousand lines), and so compilers use heuristics. There is always room for improvement in these heuristics.
It is easier and more efficient to map some languages into machine language than others. There is no easy analogy that I can think of for this. The closest I can come to is translating Italian to Spanish vs. translating a Khoisan language into Hawaiian.
Another analogy is saying "Well, the laws of physics are what govern how every animal moves, so why do some animals move so much faster than others? Shouldn't they all just move at the same speed?".
No, some languages are simply interpreted. They never actually get turned into machine code. So those languages will generally run slower than low-level languages like C.
Even for the languages which are compiled into machine code, sometimes what comes out of the compiler is not the most efficient possible way to write that given program. So it's often possible to write programs in, say, assembly language that run faster than their C equivalents, and C programs that run faster than their JIT-compiled Java equivalents, etc. (Modern compilers are pretty good, though, so that's not so much of an issue these days)
Yes, all programs get eventually translated into machine code. BUT:
Some programs get translated during compilation, while others are translated on-the-fly by an interpreter (e.g. Perl) or a virtual machine (e.g. original Java)
Obviously, the latter is MUCH slower as you spend time on translation during running.
Different languages can be translated into DIFFERENT machine code. Even when the same programming task is done. So that machine code might be faster or slower depending on the language.
You should understand the difference between compiling (which is translating) and interpreting (which is simulating). You should also understand the concept of a universal basis for computation.
A language or instruction set is universal if it can be used to write an interpreter (or simulator) for any other language or instruction set. Most computers are electronic, but they can be made in many other ways, such as by fluidics, or mechanical parts, or even by people following directions. A good teaching exercise is to write a small program in BASIC and then have a classroom of students "execute" the program by following its steps. Since BASIC is universal (to a first approximation) you can use it to write a program that simulates the instruction set for any other computer.
So you could take a program in your favorite language, compile (translate) it into machine language for your favorite machine, have an interpreter for that machine written in BASIC, and then (in principle) have a class full of students "execute" it. In this way, it is first being reduced to an instruction set for a "fast" machine, and then being executed by a very very very slow "computer". It will still get the same answer, only about a trillion times slower.
Point being, the concept of universality makes all computers equivalent to each other, even though some are very fast and others are very slow.
No, some languages are run by a 'software interpreter' as byte code.
Also, it depends on what the language does in the background as well, so 2 identically functioning programs in different languages may have different mechanics behind the scenes and hence be actually running different instructions resulting in differing performance.
I was reading about the pros and cons of interpreted languages, and one of the most common cons is the slowness, but why are programs in interpreted languages slow?
Native programs runs using instructions written for the processor they run on.
Interpreted languages are just that, "interpreted". Some other form of instruction is read, and interpreted, by a runtime, which in turn executes native machine instructions.
Think of it this way. If you can talk in your native language to someone, that would generally work faster than having an interpreter having to translate your language into some other language for the listener to understand.
Note that what I am describing above is for when a language is running in an interpreter. There are interpreters for many languages that there is also native linkers for that build native machine instructions. The speed reduction (however the size of that might be) only applies to the interpreted context.
So, it is slightly incorrect to say that the language is slow, rather it is the context in which it is running that is slow.
C# is not an interpreted language, even though it employs an intermediate language (IL), this is JITted to native instructions before being executed, so it has some of the same speed reduction, but not all of it, but I'd bet that if you built a fully fledged interpreter for C# or C++, it would run slower as well.
And just to be clear, when I say "slow", that is of course a relative term.
All answers seem to miss the real important point here. It's the detail how "interpreted" code is implemented.
Interpreted script languages are slower because their method, object and global variable space model is dynamic. In my opinion this is the real definition of of script language not the fact that it is interpreted. This requires many extra hash-table lookups on each access to a variable or method call. And its the main reason why they are all terrible at multithreading and using a GIL (Global Interpreter Lock). This lookups is where most of the time is spent. It is a painful random memory lookup, which really hurts when you get a L1/L2 cache-miss.
Google's Javascript Core8 is so fast and targeting almost C speed for a simple optimization: they take the object data model as fixed and create internal code to access it like the data structure of a native compiled program. When a new variable or method is added or removed then the whole compiled code is discarded and compiled again.
The technique is well explained in the Deutsch/Schiffman paper "Efficient Implementation of the Smalltalk-80 System".
The question why php, python and ruby aren't doing this is pretty simple to answer: the technique is extremely complicated to implement.
And only Google has the money to pay for JavaScript because a fast browser-based JavaScript interpreter is their fundamental need of their billion dollar business model.
Think of the interpeter as an emulator for a machine you don't happen to have
The short answer is that the compiled languages are executed by machine instructions whereas the interpreted ones are executed by a program (written in a compiled language) that reads either the source or a bytecode and then essentially emulates a hypothetical machine that would have run the program directly if the machine existed.
Think of the interpreted runtime as an emulator for a machine that you don't happen to actually have around at the moment.
This is obviously complicated by the JIT (Just In Time) compilers that Java, C#, and others have. In theory, they are just as good as "AOT" ("At One Time") compilers but in practice those languages run slower and are handicapped by needing to have the compiler around using up memory and time at the program's runtime. But if you say any of that here on SO be prepared to attract rabid JIT defenders who insist that there is no theoretical difference between JIT and AOT. If you ask them if Java and C# are as fast as C and C++, then they start making excuses and kind of calm down a little. :-)
So, C++ totally rules in games where the maximum amount of available computing can always be put to use.
On the desktop and web, information-oriented tasks are often done by languages with more abstraction or at least less compilation, because the computers are very fast and the problems are not computationally intensive, so we can spend some time on goals like time-to-market, programmer productivity, reliable memory-safe environments, dynamic modularity, and other powerful tools.
This is a good question, but should be formulated a little different in my opinion, for example: "Why are interpreted languages slower than compiled languages?"
I think it is a common misconception that interpreted languages are slow per se. Interpreted languages are not slow, but, depending on the use case, might be slower than the compiled version. In most cases interpreted languages are actually fast enough!
"Fast enough", plus the increase in productivity from using a language like Python over, for example, C should be justification enough to consider an interpreted language. Also, you can always replace certain parts of your interpreted program with a fast C implementation, if you really need speed. But then again, measure first and determine if speed is really the problem, then optimize.
In addition to the other answers there's optimization: when you're compiling a programme, you don't usually care how long it takes to compile - the compiler has lots of time to optimize your code. When you're interpreting code, it has to be done very quickly so some of the more clever optimizations might not be able to be made.
Loop a 100 times, the contents of the loop are interpreted 100 times into low level code.
Not cached, not reused, not optimised.
In simple terms, a compiler interprets once into low level code
Edit, after comments:
JIT is compiled code, not interpreted. It's just compiled later not up-front
I refer to the classical definition, not modern practical implementations
A simple question, without any real simple answer. The bottom line is that all computers really "understand" is binary instructions, which is what "fast" languages like C are compiled into.
Then there are virtual machines, which understand different binary instructions (like Java and .NET) but those have to be translated on the fly to machine instructions by a Just-In-Compiler (JIT). That is almost as fast (even faster in some specific cases because the JIT has more information than a static compiler on how the code is being used.)
Then there are interpreted languages, which usually also have their own intermediate binary instructions, but the interpreter functions much like a loop with a large switch statement in it with a case for every instruction, and how to execute it. This level of abstraction over the underlying machine code is slow. There are more instructions involved, long chains of function calls in the interpreter to do even simple things, and it can be argued that the memory and cache aren't used as effectively as a result.
But interpreted languages are often fast enough for the purposes for which they're used. Web applications are invariably bound by IO (usually database access) which is an order of magnitude slower than any interpreter.
From about.com:
An Interpreted language is processed
at runtime. Every line is read,
analysed, and executed. Having to
reprocess a line every time in a loop
is what makes interpreted languages so
slow. This overhead means that
interpreted code runs between 5 - 10
times slower than compiled code. The
interpreted languages like Basic or
JavaScript are the slowest. Their
advantage is not needing to be
recompiled after changes and that is
handy when you're learning to program.
The 5-10 times slower is not necessarily true for languages like Java and C#, however. They are interpreted, but the just-in-time compilers can generate machine language instructions for some operations, speeding things up dramatically (near the speed of a compiled language at times).
There is no such thing as an interpreted language. Any language can be implemented by an interpreter or a compiler. These days most languages have implementations using a compiler.
That said, interpreters are usually slower, because they need process the language or something rather close to it at runtime and translate it to machine instructions. A compiler does this translation to machine instructions only once, after that they are executed directly.
Yeah, interpreted languages are slow...
However, consider the following. I had a problem to solve. It took me 4 minutes to solve the problem in Python, and the program took 0.15 seconds to run. Then I tried to write it in C, and I got a runtime of 0.12 seconds, and it took me 1 hour to write it. All this because the practical way to solve problem in question was to use hashtables, and the hashtable dominated the runtime anyway.
Interpreted languages need to read and interpret your source code at execution time. With compiled code a lot of that interpretation is done ahead of time (at compilation time).
Very few contemporary scripting languages are "interpreted" these days; they're typically compiled on the fly, either into machine code or into some intermediate bytecode language, which is (more efficiently) executed in a virtual machine.
Having said that, they're slower because your cpu is executing many more instructions per "line of code", since many of the instructions are spent understanding the code rather than doing whatever the semantics of the line suggest!
Read this Pros And Cons Of Interpreted Languages
This is the relevant idea in that post to your problem.
An execution by an interpreter is
usually much less efficient then
regular program execution. It happens
because either every instruction
should pass an interpretation at
runtime or as in newer
implementations, the code has to be
compiled to an intermediate
representation before every execution.
For the same reason that it's slower to talk via translator than in native language. Or, reading with dictionary. It takes time to translate.
Update: no, I didn't see that my answer is the same as the accepted one, to a degree ;-)
Wikipedia says,
Interpreting code is slower than running the compiled code because the interpreter must analyze each statement in the program each time it is executed and then perform the desired action, whereas the compiled code just performs the action within a fixed context determined by the compilation. This run-time analysis is known as "interpretive overhead". Access to variables is also slower in an interpreter because the mapping of identifiers to storage locations must be done repeatedly at run-time rather than at compile time.
Refer this IBM doc,
Interpreted program must be translated each time it is executed, there is a higher overhead. Thus, an interpreted language is generally more suited to ad hoc requests than predefined requests.
In Java though it is considered as an interpreted language, It uses JIT (Just-in-Time) compilation which mitigate the above issue by using a caching technique to cache the compiled bytecode.
The JIT compiler reads the bytecodes in many sections (or in full, rarely) and compiles them dynamically into machine code so the program can run faster. This can be done per-file, per-function or even on any arbitrary code fragment; the code can be compiled when it is about to be executed (hence the name "just-in-time"), and then cached and reused later without needing to be recompiled.