When we store data in memory.
How does it get stored, so it can recognize what type of data it is when loaded.
What I want to ask is how the data types like Natural numbers, integers, characters, etc are stored in memory. So they can be recognized easily later when extracted from memory.
When we see at memory, what we see are hex numbers.
How can we relate these hex numbers for ASCII value or Integer Value or any other etc.
Since all of your data is written in binary, there isn't much difference between how the char a is written and how the int 97 is written, since they represent the same binary string (at least the last 8 bits of those strings). That being said, when you read from memory, you read a data type, by that type, you know how you should interpret the data
Memory does not operate in terms of "character" or "integer", these are high-level concepts that assume an abstract machine.
Typically, but not necessarily, a character is just an integer with a smaller size, often 8 bits (but a character could as well be 32 bits!) which represents one symbol or letter, rather than a discrete number. In some cases, a character may even be encoded using a variable length.
Memory operates in terms of bits that are organized in bytes (smallest directly addressable unit) or words. These are -- unbeknownst to you -- organized in banks. The hardware typically allows access in units called "cache lines", but this is something that happens secretly behind your back.
In assembler language, you can typically access bytes and power-of-two multiples of these, sometimes with special alignment requirements (there's usually also bit operations, but while they only change one bit, they still work on whole bytes/words).
All of that is, however, not very interesting, and also widely irrelevant for you. It is first and foremost the compiler's (or interpreter's) job to make sure that when you speak of an integer or a character, that whatever you want comes out at the other end. It is also the tool's responsibility to convert one into another if possible, and produce an error if not possible.
You do not even know for certain whether the value of an integer or a character has a memory location at all (it may very well be stored in a register) unless you explicitly enforce that.
You cannot distinguish a byte at some memory location that came from a "character" from a byte that belongs to an "integer". They look just the same.
And while it is possible to read the raw bytes of one type as another type in most languages, this is not something you normally need to do (or should do).
Related
A u32 takes 4 bytes of memory, a String takes 3 pointer-sized integers (for location, size, and reserved space) on the stack, plus some amount on the heap.
This to me implies that Rust doesn't know, when the code is executed, what type is stored at a particular location, because that knowledge would require more memory.
But at the same time, does it not need to know what type is stored at 0xfa3d2f10, in order to be able to interpret the bytes at that location? For example, to know that the next bytes form the spec of a String on the heap?
How does Rust store types at runtime?
It doesn't, generally.
Rust doesn't know, when the code is executed, what type is stored at a particular location
Correct.
does it not need to know what type is stored
No, the bytes in memory should be correct, and the rest of the code assumes as much. The offsets of fields in a struct are baked-in to the generated machine code.
When does Rust store something like type information?
When performing dynamic dispatch, a fat pointer is used. This is composed of a pointer to the data and a pointer to a vtable, a collection of functions that make up the interface in question. The vtable could be considered a representation of the type, but it doesn't have a lot of the information that you might think goes into "a type" (unless the trait requires it). Dynamic dispatch isn't super common in Rust as most people prefer static dispatch when it's possible, but both techniques have their benefits.
There's also concepts like TypeId, which can represent one specific type, but only of a subset of types. It also doesn't provide much capability besides "are these the same type or not".
Isn't this all terribly brittle?
Yes, it can be, which is one of the things that makes Rust so interesting.
In a language like C or C++, there's not much that safeguards the programmer from making dumb mistakes that go out and mess up those bytes floating around in memory. Making those mistakes is what leads to bugs due to memory safety. Instead of interpreting your password as a password, it's interpreted as your username and printed out to an attacker (oops!)
Rust provides safeguards against that in the form of a strong type system and tools like the borrow checker, but still all done at compile time. Unsafe Rust enables these dangerous tools with the tradeoff that the programmer is now expected to uphold all the guarantees themselves, much like if they were writing C or C++ again.
See also:
When does type binding happen in Rust?
How does Rust implement reflection?
How do I print the type of a variable in Rust?
How to introspect all available methods and members of a Rust type?
In Delphi, why are AnsiStrings indexed from one and dynamic arrays indexed from zero? Is this a historical accident, to make AnsiStrings work more like ShortStrings, or is there some deeper logic at work?
One of the contributing factors that led to "Pascal" strings being 1 indexed instead of 0 indexed was that the length of the string was stored in the zeroth byte. Yes, that could have been hidden from the programmer's view by having the compiler internally add a constant offset to the string index expression (as was done in Delphi's long strings later) but in the beginning things were much simpler. Allocate a block of memory, store the length in byte zero, index the char data from byte 1. End of story.
As I recall UCSD Pascal was using this length-in-zero-byte convention long before Turbo Pascal came along.
As for why dynamic arrays are zero based, I don't recall any specific reason but I would guess it reflects the dynamic array's kinship to dynamically allocating a buffer and indexing off the buffer pointer. The array types that you would use to create array pointer types were zero based arrays. The first byte is found at buffer pointer + 0 offset. This is the C rationalization for zero based everything. There was no compelling reason to carry string's 1 based indexing pattern over to compiler managed arrays when string's 1 based indexing was already (and had always been) the exception rather than the norm.
It may well be that because the string type was the first array-like data type that everyone first encountered and possibly the most used data type across the board, there may be a perception of a bias towards 1 based indexing in the language. However, if you look closely I think you'll find arrays in Pascal (distinct from string) have never been inherently 1 based, especially when dynamically allocated.
The reason for the Delphi string tradition of 1-based strings is quite simple. The tradition comes from the implementation of old style Turbo Pascal strings. That data type stored the length of the string in the first byte of the variable, index 0. The string data began in the next byte, index 1.
You can still use that data type today. It's now called ShortString. As is immediately obvious from it's implementation, there is a 255 character limit. This limit led to the introduction of huge strings, if I recall correctly, in Delphi 2. When huge strings were introduced the language designers chose to retain 1-based indexing to make it easier for developers to switch from short strings to huge strings.
I guess Turbo Pascal didn't invent the idea of using element 0 for length. It's just that I'm too young to remember what came before then!
Dynamic arrays weren't bound by the past in the same way and had a free choice. I don't know why zero based was chosen. Perhaps because it fits more easily with the prevailing fashion on platform on which Delphi existed at that time, namely Windows. That's just a guess though. Danny Thorpe worked on the Delphi compiler at that time, and even he can't remember the rationale!
The Delphi language designers are currently moving towards zero based string indexing for huge strings. The initial steps in this direction can be seen in XE3 in the TStringHelper class which uses 0-based indexing. And also in the ZEROBASEDSTRINGS conditional which allows you to opt in to 0-based indexing. Expect the next generation Delphi compiler to use 0-based indexing only. The times they are changin'.
Historical accident.
Pascal strings and arrays traditionally start at 1.
C - and perhaps consequently AnsiStrings - start at 0.
I don't know the rationale for "breaking with Pascal tradition" for Dynamic arrays, which also start at zero. But it makes sense, and I agree with it ...
IMHO...
I'm writing a basic text editor, well it's really an edit control box where I want to write code, numerical values and expressions for my main program.
The way I'm currently doing it is that I feed character strings into the edit control. In the edit control I have a class that breaks the string up into “glyphs” like words, numbers, line breaks, tabs, format tokens, etc. The word glyphs for example contain a string representing a literal word and a short integer that represents the number of trailing white spaces. The glyphs also contain info needed when drawing the text and calculating line wrapping.
For example the text line “My name is Karl” would equal a linked list of glyphs like this:
NewLineGlyph → WordGlyph (“My”, 1 whitespace) → WordGlyph (“name”, 1 whitespace) → WordGlyph(“is”, 1 whitespace ) → WordGlyph (“Karl”, 0 whitespace) → NULL.
So instead of storing the string in memory as a continuous block of chars (or WCHARs), it is stored in small chunks with potentially lots of small allocations and deallocations.
My question is; should I be concerned with heap fragmentation when doing it this way? Do you have any tips on makinging this more efficient? Or a completely different way of doing it? :)
PS. I'm working in C++ on Win7.
Should you be concerned about fragmentation? The answer likely depends on how large your documents are (e.g., number of words), and how much editing will occur and the nature of those edits. The approach you have outlined might be reasonable for a static (read-only) document where you can "parse" the document once, but I imagine there will be a fair amount of work that needs to happen behind the scenes to keep your data structures in the correct state as a user is making arbitrary edits. Also, you'll have to decide on what a "word" is, which isn't necessarily obvious/consistent in every case. For example, is "hard-working" one word or two? If it's one, does that mean you will never word wrap at the hyphen? Or, consider the case where a "word" will not fit on a single line. In that case, will you simply truncate, or would you want to force break the word across lines?
My recommendation would be store the text as a block, and store the line breaks separately (as offsets into the text block), then recalculate line breaks as needed each time there is a change. If you're concerned about fragmentation and minimizing the number of allocations/deallocations, you could allocate fixed-size blocks and then manage memory inside of those blocks yourself. Here's what I've done in the past:
Text is stored as a block of characters, but rather than having a single contiguous block for the entire document, I maintain a linked list of blocks that are always allocated 4KB (i.e., either 4K single-byte chars, or 2K WCHARs). In other words, the text is stored as a linked list of arrays, where each array is allocated to a constant size.
Each block keeps track of how much space (i.e., characters) are used/free within that block.
When inserting one or more characters, if there is space in the current block, I can simply shift memory within that block (no allocation/deallocation required). If no space is available in the current block, but space is available in the adjacent block, then again I can just shift memory between existing blocks (no allocation/deallocation required). If both blocks are full, only then do I allocate a new 4KB block and add at the appropriate position in the linked list.
When deleting one or more characters, I simply need to shift memory (at most 4KB) rather than entire document text. I also may have to deallocate and remove any block(s) that become completely empty.
I also do some "garbage collection" to coalesce free space at appropriate times. This is fairly straightforward and involves moving characters from one block to another so that some blocks become empty and can be removed.
From the OS and/or runtime library's perspective, all of the allocations/dellocations are the same size (4KB), so there is no fragmentation. And since I manage the contents of that memory, I can avoid fragmentation within my allocated space by shifting memory contents to eliminate wasted space. The other advantage is that it minimizes the number of alloc/dealloc calls, which can be a performance concern depending on what allocator you are using. So, it's an optimization for both speed and size -- how often does that happen? :-)
I wouldn't worry about heap fragmentation; modern heap manager is pretty good at dealing with that.
I might worry about poor data locality, though. With each glyph as a separate allocation in a linked list (especially a non-invasive list like std::list), any sort of pass through the document is going to jump all over memory in a potentially non-cache-friendly way.
Text editors are harder than they seem at first blush. There are a lot of specialized data structures out there for representing blocks of text and structured documents. They each optimize for different types of operations. I recommend searching for explanations of them and then considering the types of operations you'll have to do most.
This paper is old, but it has a lot of good information: http://www.cs.unm.edu/~crowley/papers/sds.pdf
My problem is that I have a 100,000+ different elements and as I understand it Huffman works by assigning the most common element a 0 code, and the next 10, the next 110, 1110, 11110 and so on. My question is, if the code for the nth element is n-bits long then surely once I have passed the 32nd term it is more space efficient to just sent 32-bit data types as they are, such as ints for example? Have I missed something in the methodology?
Many thanks for any help you can offer. My current implementation works by doing
code = (code << 1) + 2;
to generate each new code (which seems to be correct!), but the only way I could encode over 100,000 elements would be to have an int[] in a makeshift new data type, where to access the value we would read from the int array as one continuous long symbol... that's not as space efficient as just transporting a 32-bit int? Or is it more a case of Huffmans use being with its prefix codes, and being able to determine each unique value in a continuous bit stream unambiguously?
Thanks
Your understanding is a bit off - take a look at http://en.wikipedia.org/wiki/Huffman_coding. And you have to pack the encoded bits into machine words in order to get compression - Huffman encoded data can best be thought of as a bit-stream.
You seem to understand the principle of prefix codes.
Could you tell us a little more about these 100,000+ different elements you mention?
The fastest prefix codes -- universal codes -- do, in fact, involve a series of bit sequences that can be pre-generated without regard to the actual symbol frequencies. Compression programs that use these codes, as you mentioned, associate the most-frequent input symbol to the shortest bit sequence, the next-most-frequent input symbol to the next-shorted bit sequence, and so on.
What you describe is one particular kind of prefix code: unary coding.
Another popular variant of the unary coding system assigns elements in order of frequency to the fixed codes
"1", "01", "001", "0001", "00001", "000001", etc.
Some compression programs use another popular prefix code: Elias gamma coding.
The Elias gamma coding assigns elements in order of frequency to the fixed set of codewords
1
010
011
00100
00101
00110
00111
0001000
0001001
0001010
0001011
0001100
0001101
0001110
0001111
000010000
000010001
000010010
...
The 32nd Elias gamma codeword is about 10 bits long, about half as long as the 32nd unary codeword.
The 100,000th Elias gamma codeword will be around 32 bits long.
If you look carefully, you can see that each Elias gamma codeword can be split into 2 parts -- the first part is more or less the unary code you are familiar with. That unary code tells the decoder how many more bits follow afterward in the rest of that particular Elias gamma codeword.
There are many other kinds of prefix codes.
Many people (confusingly) refer to all prefix codes as "Huffman codes".
When compressing some particular data file, some prefix codes do better at compression than others.
How do you decide which one to use?
Which prefix code is the best for some particular data file?
The Huffman algorithm -- if you neglect the overhead of the Huffman frequency table -- chooses exactly the best prefix code for each data file.
There is no singular "the" Huffman code that can be pre-generated without regard to the actual symbol frequencies.
The prefix code choosen by the Huffman algorithm is usually different for different files.
The Huffman algorithm doesn't compress very well when we really do have 100,000+ unique elements --
the overhead of the Huffman frequency table becomes so large that we often can find some other "suboptimal" prefix code that actually gives better net compression.
Or perhaps some entirely different data compression algorithm might work even better in your application.
The "Huffword" implementation seems to work with around 32,000 or so unique elements,
but the overwhelming majority of Huffman code implementations I've seen work with around 257 unique elements (the 256 possible byte values, and the end-of-text indicator).
You might consider somehow storing your data on a disk in some raw "uncompressed" format.
(With 100,000+ unique elements, you will inevitably end up storing many of those elements in 3 or more bytes).
Those 257-value implementations of Huffman compression will be able to compress that file;
they re-interpret the bytes of that file as 256 different symbols.
My question is, if the code for the nth element is n-bits long then
surely once I have passed the 32nd term it is more space efficient to
just sent 32-bit data types as they are, such as ints for example?
Have I missed something in the methodology?
One of the more counter-intuitive features of prefix codes is that some symbols (the rare symbols) are "compressed" into much longer bit sequences. If you actually have 2^8 unique symbols (all possible 8 bit numbers), it is not possible to gain any compression if you force the compressor to use prefix codes limited to 8 bits or less. By allowing the compressor to expand rare values -- to use more than 8 bits to store a rare symbol that we know can be stored in 8 bits -- that frees up the compressor to use less than 8 bits to store the more-frequent symbols.
related:
Maximum number of different numbers, Huffman Compression
I'm converting some legacy code to Delphi 2010.
There are a fair number of old ShortStrings, like string[25]
Why does the assignment below:
type
S: String;
ShortS: String[25];
...
S := ShortS;
cause the compiler to generate this warning:
W1057 Implicit string cast from 'ShortString' to 'string'.
There's no data loss that is occurring here. In what circumstances would this warning be helpful information to me?
Thanks!
Tomw
It's because your code is implicitly converting a single-byte character string to a UnicodeString. It's warning you in case you might have overlooked it, since that can cause problems if you do it by mistake.
To make it go away, use an explicit conversion:
S := string(ShortS);
The ShortString type has not changed. It continues to be, in effect, an array of AnsiChar.
By assigning it to a string type, you are taking what is a group of AnsiChars (one byte) and putting it into a group of WideChars (two bytes). The compiler can do that just fine, and is smart enough not to lose data, but the warning is there to let you know that such a conversion has taken place.
The warning is very important because you may lose data. The conversion is done using the current Windows 8-bit character set, and some character sets do not define all values between 0 and 255, or are multi-byte character sets, and thus cannot convert all byte values.
The data loss can occur on a standard computer in a country with specific standard character sets, or on a computer in USA that has been set up for a different locale, because the user communicates a lot with people in other languages.
For instance, if the local code page is 932, the byte values 129 and 130 will both convert to the same value in the Unicode string.
In addition to this, the conversion involves a Windows API call, which is an expensive operation. If you do a lot of these, it can slow down your application.
It's safe ( as long as you're using the ShortString for its intended purpose: to hold a string of characters and not a collection of bytes, some of which may be 0 ), but may have performance implications if you do it a lot. As far as I know, Delphi has to allocate memory for the new unicode string, extract the characters from the ShortString into a null-terminated string (that's why it's important that it's a properly-formed string) and then call something like the Windows API MultiByteToWideChar() function. Not rocket science, but not a trivial operation either.
ShortStrings don't have a code page associated with them, AnsiStrings do (since D2009).
The conversion from ShortString to UnicodeString can only be done on the assumption that ShortStrings are encoded in the default ANSI encoding which is not a safe assumption.
I don't really know Delphi, but if I remember correctly, the Shortstrings are essentially a sequence of characters on the stack, whereas a regular string (AnsiString) is actually a reference to a location on the heap. This may have different implications.
Here's a good article on the different string types:
http://www.codexterity.com/delphistrings.htm
I think there might also be a difference in terms of encoding but I'm not 100% sure.