Does choosing a programming language decide performance when all of it is compiled to some 1's and 0's
Eg: printf (in C) vs cout (C++) vs print (in Python)
Do all of the above have same binary compiled code ?
Appreciate any help in understanding this concept of programming language and role on hardware in detail! Thanks in advance
The choice of programming language can have many impacts on the performance of your code, how portable it is, the comparability and among other things, how easily the objective can be put into code. To answer you question directly, C and C++ would likely produce the 'same binary' when printing an output, if they were both done for the same target environment. Python is different because it is an interpreted language, meaning the code is read by a program written in code native to the architecture and acted upon accordingly. Python is something of an edge case in this regard because it is technically compiled at execution time (and can be before distribution) but into an intermediate code similar in principle to Java byte code that is only understood by the Python interpreter.
The difference you bring up between lower language's like C and higher ones like Java, Python and even JavaScript is the nature of their execution being done by native hardware or by the interpreter. Language's running on bare metal are generally understood to be faster than those on interpreters as the interpreter takes time to understand the code and uses it's own system resources. Java tends to break this rule because it's interpreter is a full virtual machine that understands very simple byte code, making it competitive in speed to language's like C.
To what kind of binary code they are compiled depends on the compiler. For C and C++ there are dozens of different compilers which might generate different binary code. Besides that, most compilers even have optimization flags that influence the generated binary code a lot.
Python isn't even directly compiled into "machine code", it's compiled into bytecode for a python interpreter. The Python interpreter itself is a program that runs on the machine, then reads the python-bytecode and executes it probably by internally calling predefined functions (that already exist in machine-code)
I'm looking into installing a disassembler (or decompiler) on my Linux Mint 17.3 OS and I wanted to know what the difference is between a disassembler and a decompiler. I have a rough idea of what they are (the names are fairly self-explanatory), but they are still a bit confusing.
I've read that a disassembler turns a program into assembly language, which I don't know, so it seems kind of useless to me. I've also read that a decompiler turns a 'binary file' into its source code. What exactly is a binary file?
Apparently, decompilers cannot decompile to C, only Python and other similar languages. So how can I turn a program into its original C source code?
A disassembler is a pretty straightforward application that transfers machine code into assembly language statements - This activity is the reverse operation that an assembler program does and is straightforward because there is a strict one-to-one relationship between machine code and assembly. A disassembler aims at a specific CPU. The original assembler that was used to create the executable is only of minor relevance.
A decompiler aims at recreating a compiled high-level language program from machine code into its original format - Thus trying the reverse operation of a C or Forth (popular languages for which de-compilers exist) compiler. Because there are so many high-level languages and thus so many ways in how original high-level language constructs could be expressed in machine code (even a lot of different strategies for the same language and construct, even in the same compiler, and even different strategies depending on the compiler mode and situation), this operation is much more complex and very dependent on the original compiler (and maybe even the command line that was used, it's chosen optimization level and also the used version).
Even if all that fits, most of the work of a decompiler is educated guessing and will most probably never reach a point where it can reconstruct the original program in its source code form 100% - It will rather end up with a version of source code that could have been the original program.
I have an old piece of Fortran f77 code that I would like to understand and edit for future reuse. For that purpose, I would like this code to be translated to either C or Matlab / Octave language. I have found an instance of f2c exe online, but it wouldn't run because of inappropriate OS ( my OS is Win 7 x64, f2c wanted older x32 ).
My main concern is being able to understand the code. Translation in terms of execution efficiency is not of importance. I am open to any suggestion, apart from learning Fortran 77. I am aware of that option myself, but would do it only as a last resort. Thank you.
Just learn Fortran. The output of machine-translated code may well be functionally correct and suitable for compilation and execution, but it's going to be really hard to understand and maintain. (Just look at what generated code targeting a single language, like the output of a GUI builder wizard, looks like.) In particular, while Matlab is built on Fortran, its idioms at the M-code level are different enough that it would be pretty incomprehensible. If you already know C or any other Algol-like language, picking up Fortran is not that hard. And the idioms and features that are particular to Fortran – that is, the new stuff you'd have to learn – are probably going to be especially weird and incomprehensible when sent through a translator.
The one translator that might actually be useful is a Fortran 77 -> Fortran 90 translator. The modern '90 dialect would be easier to learn and more succinct, and since the translation is within the same language family the output probably wouldn't be too ugly.
Since are using Octave, you can call Fortran code, there is no need to rewrite it. You will need to write a very simple C++ wrapper to it but Octave already provides macros to do all of the hard work. It is all documented on the manual. Actually, Octave itself calls on many fortran subroutines, so this is perfectly normal.
If you want to modify it, you should be learning Fortran then.
I wish that John McCarthy was still alive, but...
From LISP 1.5 Programmer's Manual :
LISP can interpret and execute programs written in the form of S-
expressions. Thus, like machine language, and unlike most other higher
level languages, it can be used to generate programs for further
execution.
I need more clarification about how machine language can used to generate programs and how Lisp can do it?
All that is saying is that machine code can directly write machine instructions to memory and jump to those instructions to execute them; this is the basis of many attack vectors to break into software, in fact.
The point is, when you're writing machine code, it's easy to generate machine code. But when you're writing in a compiled language like C, you can't just generate C code at run time and then execute it - unless your program includes a C compiler.
Lisp - and, these days, many other languages, especially "scripting languages" like Perl, Python, Ruby, Tcl, Javascript, and command shells - have the ability to execute code that is generated at runtime. In Lisp, since code and data have the same structure, this is usually less work than it is in the other languages, where the code to be evaluated is generally a string that has to be parsed. (Though Perl has the ability to eval a block instead of a string, which lets the compiler do the parsing ahead of time for literal code.)
A machine language can alter itself while running. The last assembly programming i did was for MS DOS and resident program that i used to run before testing other programs. When my program misbehaved, a keystroke switched to the resident program and could peek into the running program and alter it directly before resuming. It was quite handy since I didn't have a debugger.
LISP had this from the very beginning since it was originally interpreted. You could change the definition of a function while you were running and the whole langugage was always available at runtime, even eval and define. When it started getting compiled it wasn't compiled like Algol, but partially, allowing for interpreted and compiled code to intermix at the same time. The fact that its code structure was list structure and that symbols are a data type contributed to this.
Last interview I saw with McCarthy he was asked about what he thought of modern programming languages (Not LISP family but the Algol family language Ruby, that is said to be influenced by LISP), and before answering he asked if they could represent code as data (like list structure). Since it didn't, Ruby is still behind what LISP was in the 60s in his opinion.
Many new programming languages are emerging in the Algol family and some of the most promising ones, like Perl6 and Nemerle, are getting closer to the features LISP had in the 60s.
Machine language programs can fill memory regions with arbitrary bytes. Then they can just jump to the start of such region which will thus get executed right away.
Lisp language programs can easily create arbitrary S-expressions in memory, using cons. Then they can just call eval on these S-expressions to evaluate (interpret) them.
High level languages programs can easily fill memory regions with characters representing new code in the language's syntax. But they can not run such a code.
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I work as support staff in a biology research institute as a student, and Perl seems to be used everywhere. Not for every single project, but it seems that more than half the people here have a few Perl books in/on their office/desk.
Why is Perl used so much in biology?
Lincoln Stein highlighted some of the saving graces of Perl for bioinformatics in his article:
How Perl Saved the Human Genome Project.
From his analysis:
I think several factors are responsible:
Perl is remarkably good for slicing, dicing, twisting, wringing, smoothing, summarizing and otherwise mangling text. Although the biological sciences do involve a good deal of numeric analysis now, most of the primary data is still text: clone names, annotations, comments, bibliographic references. Even DNA sequences are textlike. Interconverting incompatible data formats is a matter of text mangling combined with some creative guesswork. Perl's powerful regular expression matching and string manipulation operators simplify this job in a way that isn't equalled by any other modern language.
Perl is forgiving. Biological data is often incomplete, fields can be missing, or a field that is expected to be present once occurs several times (because, for example, an experiment was run in duplicate), or the data was entered by hand and doesn't quite fit the expected format. Perl doesn't particularly mind if a value is empty or contains odd characters. Regular expressions can be written to pick up and correct a variety of common errors in data entry. Of course this flexibility can be also be a curse. I talk more about the problems with Perl below.
Perl is component-oriented. Perl encourages people to write their software in small modules, either using Perl library modules or with the classic Unix tool-oriented approach. External programs can easily be incorporated into a Perl script using a pipe, system call or socket. The dynamic loader introduced with Perl5 allows people to extend the Perl language with C routines or to make entire compiled libraries available for the Perl interpreter. An effort is currently under way to gather all the world's collected wisdom about biological data into a set of modules called "bioPerl" (discussed at length in an article to be published later in the Perl Journal).
Perl is easy to write and fast to develop in. The interpreter doesn't require you to declare all your function prototypes and data types in advance, new variables spring into existence as needed, calls to undefined functions only cause an error when the function is needed. The debugger works well with Emacs and allows a comfortable interactive style of development.
Perl is a good prototyping language. Because Perl is quick and dirty, it often makes sense to prototype new algorithms in Perl before moving them to a fast compiled language.
Sometimes it turns out that Perl is fast enough so that of the algorithm doesn't have to be ported; more frequently one can write a small core of the algorithm in C, compile it as a dynamically loaded module or external executable, and leave the rest of the application in Perl (for an example of a complex genome mapping application implemented in this way, see http://waldo.wi.mit.edu/ftp/distribution/software/rhmapper/).
Perl is a good language for Web CGI scripting, and is growing in importance as more labs turn to the Web for publishing their data.
The real answer probably has less to do with Perl than you think. Many of the things that happen are accidents of history. At the time, way back when, Perl was pretty popular, Java was getting more popular, not too many people were paying attention to Python, and Ruby was just getting started.
The people who needed to get work done used Perl and made some libraries in Perl, and other people started using those libraries. Once people start using something that is moderately useful to them, they tend not to switch (economists call those "switching costs"). From there, even more people start using it because a lot of other people are using it.
The same evolution might not happen today. I'd say that Perl, Python, and Ruby are all completely adequate and up to the task. All the things that mobrule quotes from Lincoln Stein could apply to any of the three today. If everyone had to start from scratch today, any one of those languages could be the one that everyone uses.
I've noticed, from my own client base though (a very small and unrepresentative sample of biotech), that the people pushing the programming for a lot of the biological stuff seemed to be at least part-time sysadmins who were supporting scientists. The scientists worried about the science and did some light programming, but the IT support people were doing a lot of the heavy lifting for the non-science parts. Perl is very well positioned as a sysadmin tool since it's the duct-tape of the internet.
Probably because Perl is good at manipulating strings, and much research in genetics involves the manipulation of veeery long "ACTGCATG..." strings. Just guessing...
I use lots of Perl for dealing with qualitative and quantitative data in social science research. In terms of getting things done (largely with text) quickly, finding libraries on CPAN (nice central location), and generally just getting things done quickly, it can't be surpassed.
Perl is also excellent glue, so if you have some instrumental records, and you need to glue them to data analysis routines, then Perl is your language.
Perl seems to be the language of choice for bioinformatics - there's even an O'Reilly title on just this subject: Beginning Perl for Bioinformatics.
Perl is very powerful when it comes to deal with text and it's present in almost every Linux/Unix distribution. In bioinformatics, not only are sequence data very easy to manipulate with Perl, but also most of the bionformatics algorithms will output some kind of text results.
Then, the biggest bioinformatics centers like the EBI had that great guy, Ewan Birney, who was leading the BioPerl project. That library has lots of parsers for every kind of popular bioinformatics algorithms' results, and for manipulating the different sequence formats used in major sequence databases.
Nowadays, however, Perl is not the only language used by bioinformaticians: along with sequence data, labs produce more and more different kinds of data types and other languages are more often used in those areas.
The R statistics programming language for example, is widely used for statistical analysis of microarray and qPCR data (among others). Again, why are we using it so much? Because it has great libraries for that kind of data (see bioconductor project).
Now when it comes to web development, CGI is not really state of the art today, but people who know Perl may stick to it. In my company though it is no longer used...
I hope this helps.
Perl basically forces very short development cycles. That's the kind of development that gets stuff done.
It's enough to outweigh Perl's disadvantages.
Bioinformatics deals primarily in text parsing and Perl is the best programming language for the job as it is made for string parsing. As the O'Reilly book (Beginning Perl for Bioinformatics) says that "With [Perl]s highly developed capacity to detect patterns in data, Perl has become one of the most popular languages for biological data analysis."
This seems to be a pretty comprehensive response. Perhaps one thing missing, however, is that most biologists (until recently, perhaps) don't have much programming experience at all. The learning curve for Perl is much lower than for compiled languages (like C or Java), and yet Perl still provides a ton of features when it comes to text processing. So what if it takes longer to run? Biologists can definitely handle that. Lab experiments routinely take one hour or more finish, so waiting a few extra minutes for that data processing to finish isn't going to kill them!
Just note that I am talking here about biologists that program out of necessity. I understand that there are some very skilled programmers and computer scientists out there that use Perl as well, and these comments may not apply to them.
People missed out DBI, the Perl abstract database interface that makes it really easy to work with bioinformatic databases.
There is also the one-liner angle. You can write something to reformat data in a single line in Perl and just use the -pe flag to embed that at the command line. Many people using AWK and sed moved to Perl. Even in full programs, file I/O is incredibly easy and quick to write, and text transformation is expressive at a high level compared to any engineering language around. People who use Java or even Python for one-off text transformation are just too lazy to learn another language. Java especially has a high dependence on the JVM implementation and its I/O performance.
At least you know how fast or slow Perl will be everywhere, slightly slower than C I/O. Don't learn grep, cut, sed, or AWK; just learn Perl as your command line tool, even if you don't produce large programs with it. Regarding CGI, Perl has plenty of better web frameworks such as Catalyst and Mojolicious, but the mindshare definitely came from CGI and bioinformatics being one of the earliest heavy users of the Internet.
Perl is very easy to learn as compared to other languages. It can fully exploit the biological data which is becoming the big data. It can manipulate big data and perform good for manipulation data curation and all type of DNA programming, automation of biology has become easy due languages like Perl, Python and Ruby. It is very easy for those who are knowing biology, but not knowing how to program that in other programming languages.
Personally, and I know this will date me, but it's because I learned Perl first. I was being asked to take FASTA files and mix with other FASTA files. Perl was the recommended tool when I asked around.
At the time I'd been through a few computer science classes, but I didn't really know programming all that well.
Perl proved fairly easy to learn. Once I'd gotten regular expressions into my head I was parsing and making new FASTA files within a day.
As has been suggested, I was not a programmer. I was a biochemistry graduate working in a lab, and I'd made the mistake of setting up a Linux server where everyone could see me. This was back in the day when that was an all-day project.
Anyway, Perl became my goto for anything I needed to do around the lab. It was awesome, easy to use, super flexible, other Perl guys in other labs we're a lot like me.
So, to cut it short, Perl is easy to learn, flexible and forgiving, and it did what I needed.
Once I really got into bioinformatics I picked up R, Python, and even Java. Perl is not that great at helping to create maintainable code, mostly because it is so flexible. Now I just use the language for the job, but Perl is still one of my favorite languages, like a first kiss or something.
To reiterate, most bioinformatics folks learned coding by just kluging stuff together, and most of the time you're just trying to get an answer for the principal investigator (PI), so you can't spend days on code design. Perl is superb at just getting an answer, it probably won't work a second time, and you will not understand anything in your own code if you see it six months later; BUT if you need something now, then it is a good choice even though I mostly use Python now.
I hope that gives you an answer from someone who lived it.