I'm learning about bit flags and creating bit fields manually using bit-wise operators. I then came across bitsets, seemingly an easier and cleaner way of storing a field of bits. I understand the value of using bit fields as far as minimizing memory usage. After testing the sizeof(bitset), though, I'm having a hard time understanding how this is a better approach.
Consider:
#include <bitset>
#include <iostream>
int main ()
{
// Using Bit Set, Size = 8 Bytes
const unsigned int i1 = 0;
const unsigned int i2 = 1;
std::bitset<8> mySet(0);
mySet.set(i1);
mySet.set(i2);
std::cout << sizeof(mySet) << std::endl;
// Manually managing bit flags
const unsigned char t1 = 1 << 0;
const unsigned char t2 = 1 << 1;
unsigned char bitField = 0;
bitField |= t1 | t2;
std::cout << sizeof(bitField) << std::endl;
return 0;
}
The output is:
The mySet is 8 bytes. The bitField is 1 byte.
Should I not use std::bitset if minimal memory usage is desired?
For the lowest possible memory footprint you shouldn't use std::bitset. It will likely require more memory than a plain built in type like char or int of equivalent effective size. Thus, it probably has memory overhead, but how much will depend on the implementation.
One major advantage of std::bitset is that it frees you from hardware-dependent implementations of various types. In theory, the hardware can use any representation for any type, as long as it fulfills some requirements in the C++ standard. Thus when you rely on unsigned char t1 = 1 to be 00000001 in memory, this is not actually guaranteed. But if you create a bitset and initialize it properly, it won't give you any nasty surprises.
A sidenote on bitfiddling: considering the pitfalls to fiddling with bits in this way, can you really justify this error-prone method instead of using std::bitset or even types like int and bool? Unless you're extremely resource constrained (e.g. MCU / DSP programming), I don't think you can.
Those who play with bits will be bitten, and those who play with bytes will be bytten.
By the way, the char bitField you declare and manipulate using bitwise operators is a bit field, but it's not the C++ language notion of a bit field, which looks like this:
struct BitField{
unsigned char flag1 : 1, flag2 : 1, flag3 : 1;
}
Loosely speaking, it is a data structure whose data member(s) are subdivided into separate variables. In this case the unsigned char of (presumably) 8 bits is used to create three 1-bit variables (flag1, flag2 and flag3). It is explicitly subdivided, but at the end of the day this is just compiler-/language-assisted bit fiddling similar to what you did above. You can read more about bit fields here.
Disclaimer: Please correct me in the event that I make any false claims in this post.
Consider a struct that contains eight bool member variables.
/*
* Struct uses one byte for each flag.
*/
struct WithBools
{
bool f0 = true;
bool f1 = true;
bool f2 = true;
bool f3 = true;
bool f4 = true;
bool f5 = true;
bool f6 = true;
bool f7 = true;
};
The space allocated to each variable is a byte in length, which seems like a waste if the variables are used solely as flags. One solution to reduce this wasted space, as far as the variables are concerned, is to encapsulate the eight flags into a single member variable of unsigned char.
/*
* Struct uses a single byte for eight flags; retrieval and
* manipulation of data is achieved through accessor functions.
*/
struct WithoutBools
{
unsigned char getFlag(unsigned index)
{
return flags & (1 << (index % 8));
}
void toggleFlag(unsigned index)
{
flags ^= (1 << (index % 8));
}
private:
unsigned char flags = 0xFF;
};
The flags are retrieved and manipulated via. bitwise operators, and the struct provides an interface for the user to retrieve and manipulate the flags. While flag sizes have been reduced, we now have the two additional methods that add to the size of the struct. I do not know how to benchmark this difference, therefore I could not be certain of any fluctuation between the above structs.
My questions are:
1) Would the difference in space between these two structs be negligible?
2) Generally, is this approach of "optimising" a collection of bools by compacting them into a single byte a good idea? Either in an embedded systems context or otherwise.
3) Would a C++ compiler make such an optimisation that compacts a collection of bools wherever possible and appropriate.
we now have the two additional methods that add to the size of the
struct
Methods are code and do not increase the size of the struct. Only data makes up size on the structure.
3) Would a C++ compiler make such an optimisation that compacts a
collection of bools wherever possible and appropriate.
That is a sound resounding no. The compiler is not allowed to change data types.
1) Would the difference in space between these two structs be
negligible?
No, there definitely is a size difference between the two approaches.
2) Generally, is this approach of "optimising" a collection of bools
by compacting them into a single byte a good idea? Either in an
embedded systems context or otherwise.
Generally yes, the idiomatic way to model flags is with bit-wise manipulation inside an unsigned integer. Depending on the number of flags needed you can use std::uint8_t, std::uint16_t and so on.
However the most common way to model this is not via index as you've done, but via masks.
Would the difference in space between these two structs be negligible?
That depends on how many values you are storing and how much space you have to store them in. The size difference is 1 to 8.
Generally, is this approach of "optimising" a collection of bools by compacting them into a single byte a good idea? Either in an embedded systems context or otherwise.
Again, it depends on how many values and how much space. Also note that dealing with bits instead of bytes increases code size and execution time.
Many embedded systems have relatively little RAM and plenty of Flash. Code is stored in Flash, so the increased code size can be ignored, and the saved memory could be important on small RAM systems.
Would a C++ compiler make such an optimisation that compacts a collection of bools wherever possible and appropriate.
Hypothetically it could. I would consider that an aggressive space optimization, at the expense of execution time.
STL has a specialization for vector<bool> that I frequently avoid for performance reasons - vector<char> is much faster.
If, say, a 32-bit integer is overflowing, instead of upgrading int to long, can we make use of some 40-bit type if we need a range only within 240, so that we save 24 (64-40) bits for every integer?
If so, how?
I have to deal with billions and space is a bigger constraint.
Yes, but...
It is certainly possible, but it is usually nonsensical (for any program that doesn't use billions of these numbers):
#include <stdint.h> // don't want to rely on something like long long
struct bad_idea
{
uint64_t var : 40;
};
Here, var will indeed have a width of 40 bits at the expense of much less efficient code generated (it turns out that "much" is very much wrong -- the measured overhead is a mere 1-2%, see timings below), and usually to no avail. Unless you have need for another 24-bit value (or an 8 and 16 bit value) which you wish to pack into the same structure, alignment will forfeit anything that you may gain.
In any case, unless you have billions of these, the effective difference in memory consumption will not be noticeable (but the extra code needed to manage the bit field will be noticeable!).
Note:
The question has in the mean time been updated to reflect that indeed billions of numbers are needed, so this may be a viable thing to do, presumed that you take measures not to lose the gains due to structure alignment and padding, i.e. either by storing something else in the remaining 24 bits or by storing your 40-bit values in structures of 8 each or multiples thereof).
Saving three bytes a billion times is worthwhile as it will require noticeably fewer memory pages and thus cause fewer cache and TLB misses, and above all page faults (a single page fault weighting tens of millions instructions).
While the above snippet does not make use of the remaining 24 bits (it merely demonstrates the "use 40 bits" part), something akin to the following will be necessary to really make the approach useful in a sense of preserving memory -- presumed that you indeed have other "useful" data to put in the holes:
struct using_gaps
{
uint64_t var : 40;
uint64_t useful_uint16 : 16;
uint64_t char_or_bool : 8;
};
Structure size and alignment will be equal to a 64 bit integer, so nothing is wasted if you make e.g. an array of a billion such structures (even without using compiler-specific extensions). If you don't have use for an 8-bit value, you could also use an 48-bit and a 16-bit value (giving a bigger overflow margin).
Alternatively you could, at the expense of usability, put 8 40-bit values into a structure (least common multiple of 40 and 64 being 320 = 8*40). Of course then your code which accesses elements in the array of structures will become much more complicated (though one could probably implement an operator[] that restores the linear array functionality and hides the structure complexity).
Update:
Wrote a quick test suite, just to see what overhead the bitfields (and operator overloading with bitfield refs) would have. Posted code (due to length) at gcc.godbolt.org, test output from my Win7-64 machine is:
Running test for array size = 1048576
what alloc seq(w) seq(r) rand(w) rand(r) free
-----------------------------------------------------------
uint32_t 0 2 1 35 35 1
uint64_t 0 3 3 35 35 1
bad40_t 0 5 3 35 35 1
packed40_t 0 7 4 48 49 1
Running test for array size = 16777216
what alloc seq(w) seq(r) rand(w) rand(r) free
-----------------------------------------------------------
uint32_t 0 38 14 560 555 8
uint64_t 0 81 22 565 554 17
bad40_t 0 85 25 565 561 16
packed40_t 0 151 75 765 774 16
Running test for array size = 134217728
what alloc seq(w) seq(r) rand(w) rand(r) free
-----------------------------------------------------------
uint32_t 0 312 100 4480 4441 65
uint64_t 0 648 172 4482 4490 130
bad40_t 0 682 193 4573 4492 130
packed40_t 0 1164 552 6181 6176 130
What one can see is that the extra overhead of bitfields is neglegible, but the operator overloading with bitfield reference as a convenience thing is rather drastic (about 3x increase) when accessing data linearly in a cache-friendly manner. On the other hand, on random access it barely even matters.
These timings suggest that simply using 64-bit integers would be better since they are still faster overall than bitfields (despite touching more memory), but of course they do not take into account the cost of page faults with much bigger datasets. It might look very different once you run out of physical RAM (I didn't test that).
You can quite effectively pack 4*40bits integers into a 160-bit struct like this:
struct Val4 {
char hi[4];
unsigned int low[4];
}
long getLong( const Val4 &pack, int ix ) {
int hi= pack.hi[ix]; // preserve sign into 32 bit
return long( (((unsigned long)hi) << 32) + (unsigned long)pack.low[i]);
}
void setLong( Val4 &pack, int ix, long val ) {
pack.low[ix]= (unsigned)val;
pack.hi[ix]= (char)(val>>32);
}
These again can be used like this:
Val4[SIZE] vals;
long getLong( int ix ) {
return getLong( vals[ix>>2], ix&0x3 )
}
void setLong( int ix, long val ) {
setLong( vals[ix>>2], ix&0x3, val )
}
You might want to consider Variable-Lenght Encoding (VLE)
Presumably, you have store a lot of those numbers somewhere (in RAM, on disk, send them over the network, etc), and then take them one by one and do some processing.
One approach would be to encode them using VLE.
From Google's protobuf documentation (CreativeCommons licence)
Varints are a method of serializing integers using
one or more bytes. Smaller numbers take a smaller number of bytes.
Each byte in a varint, except the last byte, has the most significant
bit (msb) set – this indicates that there are further bytes to come.
The lower 7 bits of each byte are used to store the two's complement
representation of the number in groups of 7 bits, least significant
group first.
So, for example, here is the number 1 – it's a single byte, so the msb
is not set:
0000 0001
And here is 300 – this is a bit more complicated:
1010 1100 0000 0010
How do you figure out that this is 300? First you drop the msb from
each byte, as this is just there to tell us whether we've reached the
end of the number (as you can see, it's set in the first byte as there
is more than one byte in the varint)
Pros
If you have lots of small numbers, you'll probably use less than 40 bytes per integer, in average. Possibly much less.
You are able to store bigger numbers (with more than 40 bits) in the future, without having to pay a penalty for the small ones
Cons
You pay an extra bit for each 7 significant bits of your numbers. That means a number with 40 significant bits will need 6 bytes. If most of your numbers have 40 significant bits, you are better of with a bit field approach.
You will lose the ability to easily jump to a number given its index (you have to at least partially parse all previous elements in an array in order to access the current one.
You will need some form of decoding before doing anything useful with the numbers (although that is true for other approaches as well, like bit fields)
(Edit: First of all - what you want is possible, and makes sense in some cases; I have had to do similar things when I tried to do something for the Netflix challenge and only had 1GB of memory; Second - it is probably best to use a char array for the 40-bit storage to avoid any alignment issues and the need to mess with struct packing pragmas; Third - this design assumes that you're OK with 64-bit arithmetic for intermediate results, it is only for large array storage that you would use Int40; Fourth: I don't get all the suggestions that this is a bad idea, just read up on what people go through to pack mesh data structures and this looks like child's play by comparison).
What you want is a struct that is only used for storing data as 40-bit ints but implicitly converts to int64_t for arithmetic. The only trick is doing the sign extension from 40 to 64 bits right. If you're fine with unsigned ints, the code can be even simpler. This should be able to get you started.
#include <cstdint>
#include <iostream>
// Only intended for storage, automatically promotes to 64-bit for evaluation
struct Int40
{
Int40(int64_t x) { set(static_cast<uint64_t>(x)); } // implicit constructor
operator int64_t() const { return get(); } // implicit conversion to 64-bit
private:
void set(uint64_t x)
{
setb<0>(x); setb<1>(x); setb<2>(x); setb<3>(x); setb<4>(x);
};
int64_t get() const
{
return static_cast<int64_t>(getb<0>() | getb<1>() | getb<2>() | getb<3>() | getb<4>() | signx());
};
uint64_t signx() const
{
return (data[4] >> 7) * (uint64_t(((1 << 25) - 1)) << 39);
};
template <int idx> uint64_t getb() const
{
return static_cast<uint64_t>(data[idx]) << (8 * idx);
}
template <int idx> void setb(uint64_t x)
{
data[idx] = (x >> (8 * idx)) & 0xFF;
}
unsigned char data[5];
};
int main()
{
Int40 a = -1;
Int40 b = -2;
Int40 c = 1 << 16;
std::cout << "sizeof(Int40) = " << sizeof(Int40) << std::endl;
std::cout << a << "+" << b << "=" << (a+b) << std::endl;
std::cout << c << "*" << c << "=" << (c*c) << std::endl;
}
Here is the link to try it live: http://rextester.com/QWKQU25252
You can use a bit-field structure, but it's not going to save you any memory:
struct my_struct
{
unsigned long long a : 40;
unsigned long long b : 24;
};
You can squeeze any multiple of 8 such 40-bit variables into one structure:
struct bits_16_16_8
{
unsigned short x : 16;
unsigned short y : 16;
unsigned short z : 8;
};
struct bits_8_16_16
{
unsigned short x : 8;
unsigned short y : 16;
unsigned short z : 16;
};
struct my_struct
{
struct bits_16_16_8 a1;
struct bits_8_16_16 a2;
struct bits_16_16_8 a3;
struct bits_8_16_16 a4;
struct bits_16_16_8 a5;
struct bits_8_16_16 a6;
struct bits_16_16_8 a7;
struct bits_8_16_16 a8;
};
This will save you some memory (in comparison with using 8 "standard" 64-bit variables), but you will have to split every operation (and in particular arithmetic ones) on each of these variables into several operations.
So the memory-optimization will be "traded" for runtime-performance.
As the comments suggest, this is quite a task.
Probably an unnecessary hassle unless you want to save alot of RAM - then it makes much more sense. (RAM saving would be the sum total of bits saved across millions of long values stored in RAM)
I would consider using an array of 5 bytes/char (5 * 8 bits = 40 bits). Then you will need to shift bits from your (overflowed int - hence a long) value into the array of bytes to store them.
To use the values, then shift the bits back out into a long and you can use the value.
Then your RAM and file storage of the value will be 40 bits (5 bytes), BUT you must consider data alignment if you plan to use a struct to hold the 5 bytes. Let me know if you need elaboration on this bit shifting and data alignment implications.
Similarly, you could use the 64 bit long, and hide other values (3 chars perhaps) in the residual 24 bits that you do not want to use. Again - using bit shifting to add and remove the 24 bit values.
Another variation that may be helpful would be to use a structure:
typedef struct TRIPLE_40 {
uint32_t low[3];
uint8_t hi[3];
uint8_t padding;
};
Such a structure would take 16 bytes and, if 16-byte aligned, would fit entirely within a single cache line. While identifying which of the parts of the structure to use may be more expensive than it would be if the structure held four elements instead of three, accessing one cache line may be much cheaper than accessing two. If performance is important, one should use some benchmarks since some machines may perform a divmod-3 operation cheaply and have a high cost per cache-line fetch, while others might have have cheaper memory access and more expensive divmod-3.
If you have to deal with billions of integers, I'd try to encapuslate arrays of 40-bit numbers instead of single 40-bit numbers. That way, you can test different array implementations (e.g. an implementation that compresses data on the fly, or maybe one that stores less-used data to disk.) without changing the rest of your code.
Here's a sample implementation (http://rextester.com/SVITH57679):
class Int64Array
{
char* buffer;
public:
static const int BYTE_PER_ITEM = 5;
Int64Array(size_t s)
{
buffer=(char*)malloc(s*BYTE_PER_ITEM);
}
~Int64Array()
{
free(buffer);
}
class Item
{
char* dataPtr;
public:
Item(char* dataPtr) : dataPtr(dataPtr){}
inline operator int64_t()
{
int64_t value=0;
memcpy(&value, dataPtr, BYTE_PER_ITEM); // Assumes little endian byte order!
return value;
}
inline Item& operator = (int64_t value)
{
memcpy(dataPtr, &value, BYTE_PER_ITEM); // Assumes little endian byte order!
return *this;
}
};
inline Item operator[](size_t index)
{
return Item(buffer+index*BYTE_PER_ITEM);
}
};
Note: The memcpy-conversion from 40-bit to 64-bit is basically undefined behavior, as it assumes litte-endianness. It should work on x86-platforms, though.
Note 2: Obviously, this is proof-of-concept code, not production-ready code. To use it in real projects, you'd have to add (among other things):
error handling (malloc can fail!)
copy constructor (e.g. by copying data, add reference counting or by making the copy constructor private)
move constructor
const overloads
STL-compatible iterators
bounds checks for indices (in debug build)
range checks for values (in debug build)
asserts for the implicit assumptions (little-endianness)
As it is, Item has reference semantics, not value semantics, which is unusual for operator[]; You could probably work around that with some clever C++ type conversion tricks
All of those should be straightforward for a C++ programmer, but they would make the sample code much longer without making it clearer, so I've decided to omit them.
I'll assume that
this is C, and
you need a single, large array of 40 bit numbers, and
you are on a machine that is little-endian, and
your machine is smart enough to handle alignment
you have defined size to be the number of 40-bit numbers you need
unsigned char hugearray[5*size+3]; // +3 avoids overfetch of last element
__int64 get_huge(unsigned index)
{
__int64 t;
t = *(__int64 *)(&hugearray[index*5]);
if (t & 0x0000008000000000LL)
t |= 0xffffff0000000000LL;
else
t &= 0x000000ffffffffffLL;
return t;
}
void set_huge(unsigned index, __int64 value)
{
unsigned char *p = &hugearray[index*5];
*(long *)p = value;
p[4] = (value >> 32);
}
It may be faster to handle the get with two shifts.
__int64 get_huge(unsigned index)
{
return (((*(__int64 *)(&hugearray[index*5])) << 24) >> 24);
}
For the case of storing some billions of 40-bit signed integers, and assuming 8-bit bytes, you can pack 8 40-bit signed integers in a struct (in the code below using an array of bytes to do that), and, since this struct is ordinarily aligned, you can then create a logical array of such packed groups, and provide ordinary sequential indexing of that:
#include <limits.h> // CHAR_BIT
#include <stdint.h> // int64_t
#include <stdlib.h> // div, div_t, ptrdiff_t
#include <vector> // std::vector
#define STATIC_ASSERT( e ) static_assert( e, #e )
namespace cppx {
using Byte = unsigned char;
using Index = ptrdiff_t;
using Size = Index;
// For non-negative values:
auto roundup_div( const int64_t a, const int64_t b )
-> int64_t
{ return (a + b - 1)/b; }
} // namespace cppx
namespace int40 {
using cppx::Byte;
using cppx::Index;
using cppx::Size;
using cppx::roundup_div;
using std::vector;
STATIC_ASSERT( CHAR_BIT == 8 );
STATIC_ASSERT( sizeof( int64_t ) == 8 );
const int bits_per_value = 40;
const int bytes_per_value = bits_per_value/8;
struct Packed_values
{
enum{ n = sizeof( int64_t ) };
Byte bytes[n*bytes_per_value];
auto value( const int i ) const
-> int64_t
{
int64_t result = 0;
for( int j = bytes_per_value - 1; j >= 0; --j )
{
result = (result << 8) | bytes[i*bytes_per_value + j];
}
const int64_t first_negative = int64_t( 1 ) << (bits_per_value - 1);
if( result >= first_negative )
{
result = (int64_t( -1 ) << bits_per_value) | result;
}
return result;
}
void set_value( const int i, int64_t value )
{
for( int j = 0; j < bytes_per_value; ++j )
{
bytes[i*bytes_per_value + j] = value & 0xFF;
value >>= 8;
}
}
};
STATIC_ASSERT( sizeof( Packed_values ) == bytes_per_value*Packed_values::n );
class Packed_vector
{
private:
Size size_;
vector<Packed_values> data_;
public:
auto size() const -> Size { return size_; }
auto value( const Index i ) const
-> int64_t
{
const auto where = div( i, Packed_values::n );
return data_[where.quot].value( where.rem );
}
void set_value( const Index i, const int64_t value )
{
const auto where = div( i, Packed_values::n );
data_[where.quot].set_value( where.rem, value );
}
Packed_vector( const Size size )
: size_( size )
, data_( roundup_div( size, Packed_values::n ) )
{}
};
} // namespace int40
#include <iostream>
auto main() -> int
{
using namespace std;
cout << "Size of struct is " << sizeof( int40::Packed_values ) << endl;
int40::Packed_vector values( 25 );
for( int i = 0; i < values.size(); ++i )
{
values.set_value( i, i - 10 );
}
for( int i = 0; i < values.size(); ++i )
{
cout << values.value( i ) << " ";
}
cout << endl;
}
Yes, you can do that, and it will save some space for large quantities of numbers
You need a class that contains a std::vector of an unsigned integer type.
You will need member functions to store and to retrieve an integer. For example, if you want do store 64 integers of 40 bit each, use a vector of 40 integers of 64 bits each. Then you need a method that stores an integer with index in [0,64] and a method to retrieve such an integer.
These methods will execute some shift operations, and also some binary | and & .
I am not adding any more details here yet because your question is not very specific. Do you know how many integers you want to store? Do you know it during compile time? Do you know it when the program starts? How should the integers be organized? Like an array? Like a map? You should know all this before trying to squeeze the integers into less storage.
There are quite a few answers here covering implementation, so I'd like to talk about architecture.
We usually expand 32-bit values to 64-bit values to avoid overflowing because our architectures are designed to handle 64-bit values.
Most architectures are designed to work with integers whose size is a power of 2 because this makes the hardware vastly simpler. Tasks such as caching are much simpler this way: there are a large number of divisions and modulus operations which can be replaced with bit masking and shifts if you stick to powers of 2.
As an example of just how much this matters, The C++11 specification defines multithreading race-cases based on "memory locations." A memory location is defined in 1.7.3:
A memory location is either an object of scalar type or a maximal
sequence of adjacent bit-fields all having non-zero width.
In other words, if you use C++'s bitfields, you have to do all of your multithreading carefully. Two adjacent bitfields must be treated as the same memory location, even if you wish computations across them could be spread across multiple threads. This is very unusual for C++, so likely to cause developer frustration if you have to worry about it.
Most processors have a memory architecture which fetches 32-bit or 64-bit blocks of memory at a time. Thus use of 40-bit values will have a surprising number of extra memory accesses, dramatically affecting run-time. Consider the alignment issues:
40-bit word to access: 32-bit accesses 64bit-accesses
word 0: [0,40) 2 1
word 1: [40,80) 2 2
word 2: [80,120) 2 2
word 3: [120,160) 2 2
word 4: [160,200) 2 2
word 5: [200,240) 2 2
word 6: [240,280) 2 2
word 7: [280,320) 2 1
On a 64 bit architecture, one out of every 4 words will be "normal speed." The rest will require fetching twice as much data. If you get a lot of cache misses, this could destroy performance. Even if you get cache hits, you are going to have to unpack the data and repack it into a 64-bit register to use it (which might even involve a difficult to predict branch).
It is entirely possible this is worth the cost
There are situations where these penalties are acceptable. If you have a large amount of memory-resident data which is well indexed, you may find the memory savings worth the performance penalty. If you do a large amount of computation on each value, you may find the costs are minimal. If so, feel free to implement one of the above solutions. However, here are a few recommendations.
Do not use bitfields unless you are ready to pay their cost. For example, if you have an array of bitfields, and wish to divide it up for processing across multiple threads, you're stuck. By the rules of C++11, the bitfields all form one memory location, so may only be accessed by one thread at a time (this is because the method of packing the bitfields is implementation defined, so C++11 can't help you distribute them in a non-implementation defined manner)
Do not use a structure containing a 32-bit integer and a char to make 40 bytes. Most processors will enforce alignment and you wont save a single byte.
Do use homogenous data structures, such as an array of chars or array of 64-bit integers. It is far easier to get the alignment correct. (And you also retain control of the packing, which means you can divide an array up amongst several threads for computation if you are careful)
Do design separate solutions for 32-bit and 64-bit processors, if you have to support both platforms. Because you are doing something very low level and very ill-supported, you'll need to custom tailor each algorithm to its memory architecture.
Do remember that multiplication of 40-bit numbers is different from multiplication of 64-bit expansions of 40-bit numbers reduced back to 40-bits. Just like when dealing with the x87 FPU, you have to remember that marshalling your data between bit-sizes changes your result.
This begs for streaming in-memory lossless compression. If this is for a Big Data application, dense packing tricks are tactical solutions at best for what seems to require fairly decent middleware or system-level support. They'd need thorough testing to make sure one is able to recover all the bits unharmed. And the performance implications are highly non-trivial and very hardware-dependent because of interference with the CPU caching architecture (e.g. cache lines vs packing structure). Someone mentioned complex meshing structures : these are often fine-tuned to cooperate with particular caching architectures.
It's not clear from the requirements whether the OP needs random access. Given the size of the data it's more likely one would only need local random access on relatively small chunks, organised hierarchically for retrieval. Even the hardware does this at large memory sizes (NUMA). Like lossless movie formats show, it should be possible to get random access in chunks ('frames') without having to load the whole dataset into hot memory (from the compressed in-memory backing store).
I know of one fast database system (kdb from KX Systems to name one but I know there are others) that can handle extremely large datasets by seemlessly memory-mapping large datasets from backing store. It has the option to transparently compress and expand the data on-the-fly.
If what you really want is an array of 40 bit integers (which obviously you can't have), I'd just combine one array of 32 bit and one array of 8 bit integers.
To read a value x at index i:
uint64_t x = (((uint64_t) array8 [i]) << 32) + array32 [i];
To write a value x to index i:
array8 [i] = x >> 32; array32 [i] = x;
Obviously nicely encapsulated into a class using inline functions for maximum speed.
There is one situation where this is suboptimal, and that is when you do truly random access to many items, so that each access to an int array would be a cache miss - here you would get two cache misses every time. To avoid this, define a 32 byte struct containing an array of six uint32_t, an array of six uint8_t, and two unused bytes (41 2/3rd bits per number); the code to access an item is slightly more complicated, but both components of the item are in the same cache line.
I have a 64bit integer that is used as a handle. The 64bits must be sliced into the following fields, to be accessed individually:
size : 30 bits
offset : 30 bits
invalid flag : 1 bit
immutable flag : 1 bit
type flag : 1 bit
mapped flag : 1 bit
The two ways I can think of to achieve this are:
1) Traditional bit operations (& | << >>), etc. But I find this a bit cryptic.
2) Use a bitfield struct:
#pragma pack(push, 1)
struct Handle {
uint32_t size : 30;
uint32_t offset : 30;
uint8_t invalid : 1;
uint8_t immutable : 1;
uint8_t type : 1;
uint8_t mapped : 1;
};
#pragma pack(pop)
Then accessing a field becomes very clear:
handle.invalid = 1;
But I understand bitfields are quite problematic and non-portable.
I'm looking for ways to implement this bit manipulation with the object of maximizing code clarity and readability. Which approach should I take?
Side notes:
The handle size must not exceed 64bits;
The order these fields are laid in memory is irrelevant, as long as each field size is respected;
The handles are not saved/loaded to file, so I don't have to worry about endianess.
I would go for the bitfields solution.
Bitfields are only "non-portable" if you want to store the in binary form and later read the bitfield using a different compiler or, more commonly, on a different machine architecture. This is mainly because field order is not defined by the standard.
Using bitfields within your application will be fine, and as long as you have no requirement for "binary portability" (storing your Handle in a file and reading it on a different system with code compiled by a different compiler or different processor type), it will work just fine.
Obviously, you need to do some checking, e.g. sizeof(Handle) == 8 should be done somewhere, to ensure that you get the size right, and compiler hasn't decided to put your two 30-bit values in separate 32-bit words. To improve the chances of success on multiple architectures, I'd probably define the type as:
struct Handle {
uint64_t size : 30;
uint64_t offset : 30;
uint64_t invalid : 1;
uint64_t immutable : 1;
uint64_t type : 1;
uint64_t mapped : 1;
};
There is some rule that the compiler should not "split elements", and if you define something as uint32_t, and there are only two bits left in the field, the whole 30 bits move to the next 32-bit element. [It probably works in most compilers, but just in case, using the same 64-bit type throughout is a better choice]
I recommend bit operations. Of course you should hide all those operations inside a class. Provide member functions to perform set/get operations. Judicious use of constants inside the class will make most of the operations fairly transparent. For example:
bool Handle::isMutable() const {
return bits & MUTABLE;
}
void Handle::setMutable(bool f) {
if (f)
bits |= MUTABLE;
else
bits &= ~MUTABLE;
}
When asking a question on how to do wrapped N bit signed subtraction I got the following answer:
template<int bits>
int
sub_wrap( int v, int s )
{
struct Bits { signed int r: bits; } tmp;
tmp.r = v - s;
return tmp.r;
}
That's neat and all, but how will a compiler implement this? From this question I gather that accessing bit fields is more or less the same as doing it by hand, but what about when combined with arithmetic as in this example? Would it be as fast as a good manual bit-twiddling approach?
An answer for "gcc" in the role of "a compiler" would be great if anyone wants to get specific. I've tried reading the generated assembly, but it is currently beyond me.
As written in the other question, unsigned wrapping math can be done as:
int tmp = (a - b) & 0xFFF; /* 12 bit mask. */
Writing to a (12bit) bitfield will do exactly that, signed or unsigned. The only difference is that you might get a warning message from the compiler.
For reading though, you need to do something a bit different.
For unsigned maths, it's enough to do this:
int result = tmp; /* whatever bit count, we know tmp contains nothing else. */
or
int result = tmp & 0xFFF; /* 12bit, again, if we have other junk in tmp. */
For signed maths, the extra magic is the sign-extend:
int result = (tmp << (32-12)) >> (32-12); /* asssuming 32bit int, and 12bit value. */
All that does is replicate the top bit of the bitfield (bit 11) across the wider int.
This is exactly what the compiler does for bitfields. Whether you code them by hand or as bitfields is up to you, but just make sure you get the magic numbers right.
(I have not read the standard, but I suspect that relying on bitfields to do the right thing on overflow might not be safe?)
The compiler has knowledge about the size and exact position of r in your example. Suppose it is like
[xxxxrrrr]
Then
tmp.r = X;
could e.g. be expanded to (the b-suffix indicating binary literals, & is bitwise and, | is bitwise or)
tmp = (tmp & 11110000b) // <-- get the remainder which is not tmp.r
| (X & 00001111b); // <-- put X into tmp.r and filter away unwanted bits
Imagine your layout is
[xxrrrrxx] // 4 bits, 2 left-shifts
the expansion could be
tmp = (tmp & 11000011b) // <-- get the remainder which is not tmp.r
| ((X<<2) & 00111100b); // <-- filter 4 relevant bits, then shift left 2
How X actually looks like, whether a complex formulation or just a literal, is actually irrelevant.
If your architecture does not support such bitwise operations, there are still multiplications and divisions by power of two to simulate shifting, and probably these can also be used to filter out unwanted bits.