The question is very straight: is it fastest to access a byte than a bit? If I store 8 booleans in a byte will it be slower when I have to compare them than if I used 8 bytes? Why?
Chances are no. The smallest addressable unit of memory in most machines today is a byte. In most cases, you can't address or access by bit.
In fact, accessing a specific bit might be even more expensive because you have to build a mask and use some logic.
EDIT:
Your question mentions "compare", I'm not sure exactly what you mean by that. But in some cases, you perform logic very efficiently on multiple booleans using bitwise operators if your booleans are densely packed into larger integer types.
As for which to use: array of bytes (with one boolean per byte), or a densely packed structure with one boolean per bit is a space-effiicency trade-off. For some applications that need to store a massive amount of bools, dense packing is better since it saves memory.
The underlying hardware that your code runs on is built to access bytes (or longer words) from memory. To read a bit, you have to read the entire byte, and then mask off the bits you don't care about, and possibly also shift to get the bit into the ones position. So the instructions to access a bit are a superset of the instructions to access a byte.
It may be faster to store the data as bits for a different reason - if you need to traverse and access many 8-bit sets of flags in a row. You will perform more ops per boolean flag, but you will traverse less memory by having it packed in fewer bytes. You will also be able to test multiple flags in a single operation, although you may be able to do this with bools to some extent as well, as long as they lie within a single machine word.
The memory latency penalty is far higher than register bit twiddling. In the end, only profiling the code on the hardware on which it will actually run will tell you which way is best.
From a hardware point of view, I would say that in general all the bit masking and other operations in the best case might occur within a single clock (resulting in no different), but that entirely depends on hardware layer that you likely won't ever know the specifics of, and as such you cannot bank on it.
It's worth pointing out that things like the .NET system.collections.bitarray uses a 32bit integer array underneath to store it's bit data. There is likely a performance reason behind this implementation (even if only in a general case that 32bit words perform above average), I would suggest reading up about the inner workings of that might be revealing.
From a coding point of view, it really depends what you're going to do with the bits afterwards. That is to say if you're going to store your data in booleans such as:
bool a0, a1, a2, a3, a4, a5, a6, a7;
And then in your code you compare them one by one (and most of them together):
if ( a0 && a1 && !a2 && a3 && !a4 && (!a5 || a6) || a7) {
...
}
Then you will find that it will be faster (and likely neater in code) to use a bit mask. But really the only time this would matter is if you're going to be running this code millions of times in a high performance or time critical environment.
I guess what I'm getting at here is that you should do whatever your coding standards say (and if you don't have any or they don't consider such details then just do what looks neatest for your application and need).
But I highly suggest trying to look around and read a blog or two explaining the inner workings of the .NET system.collections.bitarray.
This depends on the kind of processor and motherboard data bus, i.e. 32 bit data bus will compare your data faster if you collect them into "word"s rather than "bool"s or "byte"s....
This is only valid when you are writing in assembly language when you can compare each instruction how many cycles it takes .... but since you are using compiler then it is almost the same.
However, collecting booleans into words or integers will be useful in saving memory required for variables.
Computers tend to access things in words. Accessing a bit is slower because it requires more effort:
Imagine I said something to you, then said "oh change my second word to instead".
Now imagine my edit instead was "oh, change the third letter in the second word to 's'".
Which requires more thinking on your part?
Related
How does the flash ECC algorithm (Flash Error Correction Code) implemented on STM32L1xx work?
Background:
I want to do multiple incremental writes to a single word in program flash of a STM32L151 MCU without doing a page erase in between. Without ECC, one could set bits incrementally, e.g. first 0x00, then 0x01, then 0x03 (STM32L1 erases bits to 0 rather than to 1), etc. As the STM32L1 has 8 bit ECC per word, this method doesn't work. However, if we knew the ECC algorithm, we could easily find a short sequence of values, that could be written incrementally without violating the ECC.
We could simply try different sequences of values and see which ones work (one such sequence is 0x0000001, 0x00000101, 0x00030101, 0x03030101), but if we don't know the ECC algorithm, we can't check, whether the sequence violates the ECC, in which case error correction wouldn't work if bits would be corrupted.
[Edit] The functionality should be used to implement a simple file system using STM32L1's internal program memory. Chunks of data are tagged with a header, which contains a state. Multiple chunks can reside on a single page. The state can change over time (first 'new', then 'used', then 'deleted', etc.). The number of states is small, but it would make things significantly easier, if we could overwrite a previous state without having to erase the whole page first.
Thanks for any comments! As there are no answers so far, I'll summarize, what I found out so far (empirically and based on comments to this answer):
According to the STM32L1 datasheet "The whole non-volatile memory embeds the error correction code (ECC) feature.", but the reference manual doesn't state anything about ECC in program memory.
The datasheet is in line with what we can find out empirically when subsequentially writing multiple words to the same program mem location without erasing the page in between. In such cases some sequences of values work while others don't.
The following are my personal conclusions, based on empirical findings, limited research and comments from this thread. It's not based on official documentation. Don't build any serious work on it (I won't either)!
It seems, that the ECC is calculated and persisted per 32-bit word. If so, the ECC must have a length of at least 7 bit.
The ECC of each word is probably written to the same nonvolatile mem as the word itself. Therefore the same limitations apply. I.e. between erases, only additional bits can be set. As stark pointed out, we can only overwrite words in program mem with values that:
Only set additional bits but don't clear any bits
Have an ECC that also only sets additional bits compared to the previous ECC.
If we write a value, that only sets additional bits, but the ECC would need to clear bits (and therefore cannot be written correctly), then:
If the ECC is wrong by one bit, the error is corrected by the ECC algorithm and the written value can be read correctly. However, ECC wouldn't work anymore if another bit failed, because ECC can only correct single-bit errors.
If the ECC is wrong by more than one bit, the ECC algorithm cannot correct the error and the read value will be wrong.
We cannot (easily) find out empirically, which sequences of values can be written correctly and which can't. If a sequence of values can be written and read back correctly, we wouldn't know, whether this is due to the automatic correction of single-bit errors. This aspect is the whole reason for this question asking for the actual algorithm.
The ECC algorithm itself seems to be undocumented. Hamming code seems to be a commonly used algorithm for ECC and in AN4750 they write, that Hamming code is actually used for error correction in SRAM. The algorithm may or may not be used for STM32L1's program memory.
The STM32L1 reference manual doesn't seem to explicitely forbid multiple writes to program memory without erase, but there is no documentation stating the opposit either. In order not to use undocumented functionality, we will refrain from using such functionality in our products and find workarounds.
Interessting question.
First I have to say, that even if you find out the ECC algorithm, you can't rely on it, as it's not documented and it can be changed anytime without notice.
But to find out the algorithm seems to be possible with a reasonable amount of tests.
I would try to build tests which starts with a constant value and then clearing only one bit.
When you read the value and it's the start value, your bit can't change all necessary bits in the ECC.
Like:
for <bitIdx>=0 to 31
earse cell
write start value, like 0xFFFFFFFF & ~(1<<testBit)
clear bit <bitIdx> in the cell
read the cell
next
If you find a start value where the erase tests works for all bits, then the start value has probably an ECC of all bits set.
Edit: This should be true for any ECC, as every ECC needs always at least a difference of two bits to detect and repair, reliable one defect bit.
As the first bit difference is in the value itself, the second change needs to be in the hidden ECC-bits and the hidden bits will be very limited.
If you repeat this test with different start values, you should be able to gather enough data to prove which error correction is used.
Upon studying hash data structure and cache memory from computer architecture, I noticed that they're very similar.
Division hash function calculates index by hash(k) = k Mod (table size M) but my DS book says M should be a prime number or at least an odd number, because if M is an even number, the result is always even when k is even, odd when k is odd, so even M should be avoided since you often use memory addresses which are always even.
And yet, my CA book says for direct-mapped cache you use (Block address) Mod (Number of blocks in the cache) and the result indices look uniform. Why is this? It's all very confusing because MIPS uses 32 bit address every 4 bytes which is even number. But I think it's because they threw out the last 2 bits since they're byte offsets?
And, since it uses (Block address) Mod (Number of blocks in the cache), it makes the cache size power of 2 so that you can just use the lower x bits of the block address.
But this method looks exactly the same as division hash function, except you make the hash table power of 2, which is even (data structure book said use prime or odd) and use the lower bits of the block address.
Are these 2 different methods? If so, what's the cache one called? I would really appreciate a reply please. Thank you.
The reason for not using an even number for hash table is described here.
And how caches use addresses to calculate line numbers are described here. And its ok for caches to map more than one entry to the same line. Just because an address is mapped to a cacheline which has data, we don't blindly use the data in that cacheline. We also do a tag comparison to make sure that the content is the cacheline is what exactly we are looking for.
The reason for using a prime to take the modulo by is to get "mixing" of the bits, which is helpful if the integers that you're hashing have a poor structure. That isn't the only way to deal with it though, and for example the Java standard library doesn't use that, it uses a separate "mixing" function (that XORs the input with right-shifted versions of itself) and then uses a power-of-two sizes table. Either way it's protection against badly distributed input, which isn't necessary in and of itself - if the input was always nicely distributed you wouldn't need it.
Memory addresses are usually fairly nicely distributed, because it's typically used in sequential pieces. The obvious exception is that you will see highly aligned big objects, which would conflict with each other in the cache if nothing was done about it. Of course you will probably use a set-associative cache rather than direct mapped, since it is far more robust against degradation, and that would take care of a lot of that. But nothing is ever immune to bad patterns (that also goes for hash-mod-prime, which you can easily defeat if you know the prime), but a fairly simple improvement (which is also used in practice, or at least was, more advanced techniques exist now - combined with adaptive replacement strategies that mitigate bad access patterns) is to XOR some of the higher address bits into the index. This is hash-strengthening, the same technique used in the Java standard library, but a much simpler version of it.
Computing a remainder by a prime number (or really anything that isn't a power of two) is not something you'd want to do in this case, it's a slow computation by itself, and it leaves you with an awkwardly sized cache that doesn't fully use the power of its decoders, which adds to the slowness (or reduces cache size for a given latency, depending on how you look at it). The difference between that and XORing some of the high bits into the low bits is much bigger in hardware than it is in software, since XOR is really a trivial operation in hardware, much faster as a circuit operation than as an instruction.
When I create models in core data, I'm always a little perplexed by which integer type I should choose—16, 32, 64. I'm almost always needing something for a simple, basic number: a count of people in the household, for my present case. Probably going to be a number between 1-20. Or, I have an incrementing case id number in another instance...can't imagine that going further than few hundred people.
And here's the deal...It's clear that true computer science folk think of numbers differently, taking into account factors like the architecture that's going to be processing the numbers, the space required to process and store the data, backwards compatibility, future proofing, etc. When I think of numbers, I basically think of how large a value is being represented. So when I get to that point of my process when I have to choose between three types of integers, I basically say to myself, "Well, this is going to be a small number, let's just use the Int 16 option...", or "Shoot, I could end up with a really big number here so let's use the Int 64 choice." Basically, I pick these data types with the same sort of logic I use when ordering fries...if I'm really hungry I go for the large, if I'm feeling a big guilty I'll just get the small.
I'm learning enough to know that I'm not thinking about this in the right terms, but I don't really know why, and I don't know the appropriate way to choose the best option. What factors should I really be considering...what's the most important criteria for selecting between Int 16, Int 32, and Int 64?
It doesn't matter much.
Assuming you're using a SQLite persistent store, the three integer types are all represented as SQLite INTEGER fields (same for Core Data's "Boolean" type). And in SQLite field type is purely advisory anyway, so even that doesn't mean much. Therefore: it makes literally no difference in terms of storage space. SQLite will optimize itself based on how big the integer values are, and larger int types at the Core Data level will have no effect.
For memory usage, it might have a small impact. If you use a 64 bit int instead of a 16 bit, you're requesting more bits than you need. But unless you have extremely large data sets, it's unlikely that you'll ever have a reason to care.
My usual rule then, is to use Integer 64 for any integral value.
You do it correctly: The size of the maximum expected number. But do not care to much about it. Even if you choose a integer "too big", is doesn't fail, but simply uses more memory than needed. On disk.
I think that different sized integers are an anachronism.
Let's say I'm writing a virtual machine. I read in the program data into an array of bytes. Now I need to loop through those bytes (instructions are two bytes) and instantiate a little class representing each instruction and it's arguments.
What would be a fast parsing approach? Here are the two way's I've thought of:
Logically branching by inspecting each bit from the left to the right until I narrowed it down to a particular op code. This would be like a binary search.
Inspecting some programs to come up with a list of opcodes ordered by frequency of use, and then checking the for the full opcode in that order.
Note: I will be using bit shifting and masking in C to check, not regexes or string comps or anything high-level like that.
You don't need to parse anything. If this is in C, you make a table of function pointers which has 256 entries in it, one for each possible byte value, then jump to the appropriate function based on the first byte value. If the second byte is significant then a switch statement can be used within the function to handle the second byte. This is how the original Visual Basic interpreter (versions 1-6) worked.
This question already has answers here:
Count the number of set bits in a 32-bit integer
(65 answers)
Count bits in the number [duplicate]
(3 answers)
Closed 8 years ago.
I was asked the above question in an interview and interviewer is very certain of the answer. But i am not sure. Can anyone help me here?
Sure. The obvious brute force method is just a big lookup table with one entry for every possible value of the input number. That's not very practical if the number is very big, but is still enough to prove it's possible.
Edit: the notion has been raised that this is complete nonsense, and the same could be said of essentially any algorithm.
To a limited degree, that's a fair statement -- but the limitations are so severe that for most algorithms it remains utterly meaningless.
My original point (at least as well as I remember it) was that population counting is about equivalent to many other operations like addition and subtraction that we normally assume are O(1).
At the hardware level, circuitry for a single-cycle POPCNT instruction is probably easier than for a single-cycle ADD instruction. Just for one example, for any practical size of data word, we can use table lookups on 4-bit chunks in parallel, then add the results from those pieces together. Even using fairly unlikely worst-case assumptions (e.g., separate storage for each of those tables) this would still be easy to implement in a modern CPU -- in fact, it's probably at least somewhat simpler than the single-cycle addition or subtraction mentioned above1.
This is a decided contrast to many other algorithms. For one obvious example, let's consider sorting. For even the most trivial sort most people can imagine -- 2 items, 8 bits apiece, we're already at a 64 kilobyte lookup table to get constant complexity. Long before we can do even a fairly trivial sort (e.g., 100 items) we need a lookup table that contains far more data items than there are atoms in the universe.
Looking at it from the opposite direction, it's certainly true that at some point, essentially nothing is O(1) any more. Let's consider the most trivial operations possible. For an N-bit CPU, bitwise OR is normally implemented as a set of N OR gates in parallel. Unlike addition, there's no interaction between one bit and another, so for any practical size of CPU, this easy to execute in a single instruction.
Nonetheless, if I specify a bit-wise OR in which each operand is 100 petabits, there's nothing even approaching a practical way to do the job with constant complexity. Using the usual method of parallel OR gates, we end up with (among other things) 300 petabits worth of input and output lines -- a number that completely dwarfs even the number of pins on the largest CPUs.
On reasonable hardware, doing a bitwise OR on 100 petabit operands is going to take a while (not to mention quite a bit of hard drive space). If we increase that to 200 petabit operands, the time is likely to (about) double -- so from that viewpoint, it's an O(N) operation. Obviously enough, the same is going to be true with the other "trivial" operations like addition, subtraction, bit-wise AND, bit-wise XOR, and so on.
Nonetheless, unless you have very specific instructions to say you're going to be dealing with utterly immense operands, you're typically going to treat every one of these as a constant complexity operation. Looked at in these terms, a POPCNT instruction falls about halfway between bit-wise AND/OR/XOR on one hand, and addition/subtraction on the other, in terms of the difficulty to execute in fixed time.
1. You might wonder how it could possibly be simpler than an add when it actually includes an add after doing some other operations. If so, kudos -- it's an excellent question.
The answer is that it's because it only needs a much smaller adder. For example, a 64-bit CPU needs one half-adder and 63 full-adders. In the simple implementation, you carry out the addition bit-wise -- i.e., you add bit-0 of one operand to bit-0 of the other. That generates an output bit, and a carry bit. That carry bit becomes an input to the addition for the next pair of bits. There are some tricks to parallelize that to some degree, but the nature of the beast (so to speak) is bit-serial.
With a POPCNT instruction, we have an addition after doing the individual table lookups, but our result is limited to the size of the input words. Given the same size of inputs (64 bits) our final result can't be any larger than 64. That means we only need a 6-bit adder instead of a 64-bit adder.
Since, as outlined above, addition is basically bit-serial, this means that the addition at the end of the POPCNT instruction is fundamentally a lot faster than a normal add. To be specific, it's logarithmic on the operand size, whereas simple addition is roughly linear on the operand size.
If the bit size is fixed (e.g. natural word size of a 32- or 64-bit machine), you can just iterate over the bits and count them directly in O(1) time (though there are certainly faster ways to do it). For arbitrary precision numbers (BigInt, etc.), the answer must be no.
Some processors can do it in one instruction, obviously for integers of limited size. Look up the POPCNT mnemonic for further details.
For integers of unlimited size obviously you need to read the whole input, so the lower bound is O(n).
The interviewer probably meant the bit counting trick (the first Google result follows): http://www.gamedev.net/topic/547102-bit-counting-trick---new-to-me/