I've a Process() function that is called very heavy within my DLL (VST plugin) loaded in a DAW (Host software), such as:
for (int i = 0; i < nFrames; i++) {
// ...
for (int voiceIndex = 0; voiceIndex < PLUG_VOICES_BUFFER_SIZE; voiceIndex++) {
Voice &voice = pVoiceManager->mVoices[voiceIndex];
if (voice.mIsPlaying) {
for (int envelopeIndex = 0; envelopeIndex < ENVELOPES_CONTAINER_NUM_ENVELOPE_MANAGER; envelopeIndex++) {
Envelope &envelope = pEnvelopeManager[envelopeIndex]->mEnvelope;
envelope.Process(voice);
}
}
}
}
void Envelope::Process(Voice &voice) {
if (mIsEnabled) {
// update value
mValue[voice.mIndex] = (mBlockStartAmp[voice.mIndex] + (mBlockStep[voice.mIndex] * mBlockFraction[voice.mIndex]));
}
else {
mValue[voice.mIndex] = 0.0;
}
}
It basically takes 2% of CPU within the Host (which is nice).
Now, if I slightly change the code to this (which basically are increments and assignment):
void Envelope::Process(Voice &voice) {
if (mIsEnabled) {
// update value
mValue[voice.mIndex] = (mBlockStartAmp[voice.mIndex] + (mBlockStep[voice.mIndex] * mBlockFraction[voice.mIndex]));
// next phase
mBlockStep[voice.mIndex] += mRate;
mStep[voice.mIndex] += mRate;
}
else {
mValue[voice.mIndex] = 0.0;
}
// connectors
mOutputConnector_CV.mPolyValue[voice.mIndex] = mValue[voice.mIndex];
}
CPU go to 6/7% (note, those var don't interact with other part of codes, or at least I think so).
The only reason I can think is that access to pointer is heavy? How can I reduce this amount of CPU?
Those arrays are basic double "pointer" arrays (the most lighter C++ container):
double mValue[PLUG_VOICES_BUFFER_SIZE];
double mBlockStartAmp[PLUG_VOICES_BUFFER_SIZE];
double mBlockFraction[PLUG_VOICES_BUFFER_SIZE];
double mBlockStep[PLUG_VOICES_BUFFER_SIZE];
double mStep[PLUG_VOICES_BUFFER_SIZE];
OutputConnector mOutputConnector_CV;
Any suggestions?
You might be thinking that "pointer arrays" are the lightest containers. but CPU's don't think in terms of containers. They just read and write values through pointers.
The problem here might very well be that you know that two containers do not overlap (there are no "sub-containers"). But the CPU might not be told that by the compiler. Writing to mBlockStep might affect mBlockFraction. The compiler doesn't have run-time values, so it needs to handle the case where it does. This will mean introducing more memory reads, and less caching of values in registers.
Pack all the data items in a structure and create an array of structure. I would simply use a vector.
In Process function get the single element out of this vector, and use its parameters. At the cache-line/instruction level, all items would be (efficiently) brought into local cache (L1), as the data element (members of struct) as contiguous. Use reference or pointer of struct type to avoid copying.
Try to use integer data-types unless double is needed.
EDIT:
struct VoiceInfo
{
double mValue;
...
};
VoiceInfo voices[PLUG_VOICES_BUFFER_SIZE];
// Or vector<VoiceInfo> voices;
...
void Envelope::Process(Voice &voice)
{
// Get the object (by ref/pointer)
VoiceInfo& info = voices[voice.mIndex];
// Work with reference 'info'
...
}
Related
I am working weather data (lightning energy detected from a weather satellite). I have written a function that takes satellite data (int) and inserts it into a multidimensional array after deciding which element it needs to be placed in.
The array is :
int conus_grid[1180][520];
This has worked flawlessly, but it has taken too long to process and so I have written 2 functions that split the array so I can run 2 threads using std::thread. This is where the trouble happens... and I am doing my best to keep my examples to a minimum.
Here is my original function that accesses the array, and works fine. You can see my two loops to access the array: one being 0-1180 (x) and the other 0-520 (y) :
void writeCell(long double latitude, long double longitude, int energy)
{
double lat = latitude;
double lon = longitude;
for(int x=0;x<1180;x++)
{
for(int y=0;y<520;y++)
{
// Check every cell for one that matches current lat and lon selection, then write into that cell.
if(lon < conus_grid_west[x][y] && lon > conus_grid_east[x][y] && lat < conus_grid_north[x][y] && lat > conus_grid_south[x][y])
{
grid_used[x][y] = 1;
conus_grid[x][y] = conus_grid[x][y] + energy; // this is where it accesses the array
}
}
}
}
When I converted the code to take advantage of multithreading, I created the following functions (based on the one above, replacing it). The only difference is that they each access only one specific portion of the array. (Exactly one half each)
This first handles X... 0 to 590, and Y... 0 to 260 :
void writeCellT1(long double latitude, long double longitude, int energy)
{
double lat = latitude;
double lon = longitude;
for(int x=0;x<590;x++)
{
for(int y=0;y<260;y++)
{
// Check every cell for one that matches current lat and lon selection, then write into that cell.
if(lon < conus_grid_west[x][y] && lon > conus_grid_east[x][y] && lat < conus_grid_north[x][y] && lat > conus_grid_south[x][y])
{
grid_used[x][y] = 1;
conus_grid[x][y] = conus_grid[x][y] + energy; // this is where it accesses the array
}
}
}
}
The second handles the other half- X is 590-1180 and Y is 260-520 :
void writeCellT2(long double latitude, long double longitude, int energy)
{
double lat = latitude;
double lon = longitude;
for(int x=590;x<1180;x++)
{
for(int y=260;y<520;y++)
{
// Check every cell for one that matches current lat and lon selection, then write into that cell.
if(lon < conus_grid_west[x][y] && lon > conus_grid_east[x][y] && lat < conus_grid_north[x][y] && lat > conus_grid_south[x][y])
{
grid_used[x][y] = 1;
conus_grid[x][y] = conus_grid[x][y] + energy; // this is where it accesses the array
}
}
}
}
The program does not crash but there is data that is missing in the array once it completes - only part of the data is there. It's hard for me to track which elements it does not write, but it is clear that when I have one function to do this task, it works but when I have more than one thread accessing the array with 2 functions, it is not putting data in the array completely.
I figured it was worth a try to use mutex() like this :
m.lock();
grid_used[x][y] = 1;
conus_grid[x][y] = conus_grid[x][y] + energy;
m.unlock();
However, this does not work either as it gives the same result with failing to write data to the array. Any idea as to why this would be happening? This is only my 3rd day working with so I hope it's something simple that I overlooked in tutorials.
Is mutex() needed to safely access different elements of an array with 2 threads at once?
If you don't write to elements that may be written to or read by another thread at the same time, you don't need a mutex.
The program does not crash but there is data that is missing in the array once it completes
As #G.M. implied, you should only split on one range (and it's X in this case), Otherwise you'll only handle half of the cells. One thread handles 1/4 and the other 1/4. You should split on X because you want each thread to handle data as closely placed as possible.
Note that data in 2D arrays is stored in row-major order in memory (which is why people usually use the notation [Y][X]) but it's fine to do as you do too. Splitting on X gives one thread half the memory rows and the other thread the other half.
An alternative could be to not do the thread management yourself. C++17 added execution policies which lets you write loops where the body of the loop can be executed in different threads, usually picked from an internal thread pool. How many threads that will be used is then up to the C++ implementation and the hardware your program is executed on.
I've made an example where I've swapped X and Y and made some assumptions about the actual types you are using, for which I've created aliases.
#include <algorithm> // std::for_each
#include <array>
#include <execution> // std::execution::par
#include <iostream>
#include <memory>
#include <type_traits>
// a class to keep everything together
struct conus {
static constexpr size_t y_size = 520, x_size = 1180;
// row aliases
using conus_int_row_t = std::array<int, x_size>;
using conus_bool_row_t = std::array<bool, x_size>;
using conus_real_row_t = std::array<double, x_size>;
// 2D array aliases
using conus_grid_int_t = std::array<conus_int_row_t, y_size>;
using conus_grid_bool_t = std::array<conus_bool_row_t, y_size>;
using conus_grid_real_t = std::array<conus_real_row_t, y_size>;
// a class to store the arrays
struct conus_data_t {
conus_grid_int_t conus_grid{};
conus_grid_bool_t grid_used{};
conus_grid_real_t conus_grid_west{}, conus_grid_east{},
conus_grid_north{}, conus_grid_south{};
// an iterator to be able to loop over the row number in the arrays
class iterator {
public:
using iterator_category = std::forward_iterator_tag;
using value_type = unsigned;
using difference_type = std::make_signed_t<value_type>;
using pointer = value_type*;
using reference = value_type&;
iterator(unsigned y = 0) : current(y) {}
iterator& operator++() {
++current;
return *this;
}
bool operator!=(const iterator& rhs) const {
return current != rhs.current;
}
unsigned operator*() { return current; }
private:
unsigned current;
};
// create iterators to use in loops
iterator begin() { return {0}; }
iterator end() { return {static_cast<unsigned>(conus_grid.size())}; }
};
// create arrays on the heap to save the stack
std::unique_ptr<conus_data_t> data = std::make_unique<conus_data_t>();
void writeCell(double lat, double lon, int energy) {
// Below is the std::execution::parallel_policy in use.
// A lambda, capturing its surrounding by reference, is called for each "y".
std::for_each(std::execution::par, data->begin(), data->end(), [&](unsigned y) {
// here we're most probably in a thread from the thread pool
// references to the rows
conus_int_row_t& row_grid = data->conus_grid[y];
conus_bool_row_t& row_used = data->grid_used[y];
conus_real_row_t& row_west = data->conus_grid_west[y];
conus_real_row_t& row_east = data->conus_grid_east[y];
conus_real_row_t& row_north = data->conus_grid_north[y];
conus_real_row_t& row_south = data->conus_grid_south[y];
for(unsigned x = 0; x < x_size; ++x) {
// Check every cell for one that matches current lat
// and lon selection, then write into that cell.
if(lon < row_west[x] && lon > row_east[x] &&
lat < row_north[x] && lat > row_south[x])
{
row_used[x] = true;
// this is where it accesses the array
row_grid[x] += energy;
}
}
});
}
};
If you use g++ or clang++ on Linux, you must link with tbb (add -ltbb when linking). Other compilers may have other library demands to be able to use execution policies. Visual Studio 2019 compiles and links it out-of-the-box if you select C++17 as your language.
I've often found that using std::execution::par is a quick and semi-easy way to speed things up, but you'll have to try it out yourself to see if it becomes faster on your target machine.
I'm working with a huge amount of data stored in an array, and am trying to optimize the amount of time it takes to access and modify it. I'm using Window, c++ and VS2015 (Release mode).
I ran some tests and don't really understand the results I'm getting, so I would love some help optimizing my code.
First, let's say I have the following class:
class foo
{
public:
int x;
foo()
{
x = 0;
}
void inc()
{
x++;
}
int X()
{
return x;
}
void addX(int &_x)
{
_x++;
}
};
I start by initializing 10 million pointers to instances of that class into a std::vector of the same size.
#include <vector>
int count = 10000000;
std::vector<foo*> fooArr;
fooArr.resize(count);
for (int i = 0; i < count; i++)
{
fooArr[i] = new foo();
}
When I run the following code, and profile the amount of time it takes to complete, it takes approximately 350ms (which, for my purposes, is far too slow):
for (int i = 0; i < count; i++)
{
fooArr[i]->inc(); //increment all elements
}
To test how long it takes to increment an integer that many times, I tried:
int x = 0;
for (int i = 0; i < count; i++)
{
x++;
}
Which returns in <1ms.
I thought maybe the number of integers being changed was the problem, but the following code still takes 250ms, so I don't think it's that:
for (int i = 0; i < count; i++)
{
fooArr[0]->inc(); //only increment first element
}
I thought maybe the array index access itself was the bottleneck, but the following code takes <1ms to complete:
int x;
for (int i = 0; i < count; i++)
{
x = fooArr[i]->X(); //set x
}
I thought maybe the compiler was doing some hidden optimizations on the loop itself for the last example (since the value of x will be the same during each iteration of the loop, so maybe the compiler skips unnecessary iterations?). So I tried the following, and it takes 350ms to complete:
int x;
for (int i = 0; i < count; i++)
{
fooArr[i]->addX(x); //increment x inside foo function
}
So that one was slow again, but maybe only because I'm incrementing an integer with a pointer again.
I tried the following too, and it returns in 350ms as well:
for (int i = 0; i < count; i++)
{
fooArr[i]->x++;
}
So am I stuck here? Is ~350ms the absolute fastest that I can increment an integer, inside of 10million pointers in a vector? Or am I missing some obvious thing? I experimented with multithreading (giving each thread a different chunk of the array to increment) and that actually took longer once I started using enough threads. Maybe that was due to some other obvious thing I'm missing, so for now I'd like to stay away from multithreading to keep things simple.
I'm open to trying containers other than a vector too, if it speeds things up, but whatever container I end up using, I need to be able to easily resize it, remove elements, etc.
I'm fairly new to c++ so any help would be appreciated!
Let's look from the CPU point of view.
Incrementing an integer means I have it in a CPU register and just increments it. This is the fastest option.
I'm given an address (vector->member) and I must copy it to a register, increment, and copy the result back to the address. Worst: My CPU cache is filled with vector pointers, not with vector-member pointers. Too few hits, too much cache "refueling".
If I could manage to have all those members just in a vector, CPU cache hits would be much more frequent.
Try the following:
int count = 10000000;
std::vector<foo> fooArr;
fooArr.resize(count, foo());
for (auto it= fooArr.begin(); it != fooArr.end(); ++it) {
it->inc();
}
The new is killing you and actually you don't need it because resize inserts elements at the end if the size it's greater (check the docs: std::vector::resize)
And the other thing it's about using pointers which IMHO should be avoided until the last moment and it's uneccesary in this case. The performance should be a little bit faster in this case since you get better locality of your references (see cache locality). If they were polymorphic or something more complicated it might be different.
I want to make a map such that a set of pointers point to arrays of dynamic size.
I did use hashing with chaining. But since data I am using it for is huge, the program give std::bad_alloc after few iterations. The reason of which may be new used to generate the linked list.
Someone please suggest which data structure shall I use?
Or anything else that can improve memory usage with my hash table?
Program is in C++.
This is what my code looks like:
Initialization of hashtable:
class Link
{
public:
double iData;
Link* pNext;
Link(double it) : iData(it)
{ }
void displayLink()
{ cout << iData << " "; }
};
class List
{
private:
Link* pFirst;
public:
List()
{ pFirst = NULL; }
void insert(double key)
{
if(pFirst==NULL)
pFirst = new Link(key);
else
{
Link* pLink = new Link(key);
pLink->pNext = pFirst;
pFirst = pLink;
}
}
};
class HashTable
{
public:
int arraySize;
vector<List*> hashArray;
HashTable(int size)
{
hashArray.resize(size);
for(int j=0; j<size; j++)
hashArray[j] = new List;
}
};
main snippet:
int t_sample = 1000;
for(int i=0; i < k; i++) // initialize random position
{
x[i] = (cal_rand() * dom_sizex); //dom_sizex = 20e-10 cal_rand() generates rand no between 0 and 1
y[i] = (cal_rand() * dom_sizey); //dom_sizey = 10e-10
}
for(int t=0; t < t_sample; t++)
{
int size;
size = cell_nox * cell_noy; //size of hash table cell_nox = 212, cell_noy = 424
HashTable theHashTable(size); //make table
int hashValue = 0;
for(int n=0; n<k; n++) // k = 10*212*424
{
int m = x[n] /cell_width; //cell_width = 4.7e-8
int l = y[n] / cell_width;
hashValue = (kx*l)+m;
theHashTable.hashArray[hashValue]->insert(n);
}
-------
-------
}
First things first, use a Standard Container. In your specific case, you might want:
either std::unordered_multimap<int, double>
or std::unordered_map<int, std::vector<double>>
(Note: if you do not have C++11, those are available in Boost)
Your main loop becomes (using the second option):
typedef std::unordered_map<int, std::vector<double>> HashTable;
for(int t = 0; t < t_sample; ++t)
{
size_t const size = cell_nox * cell_noy;
// size of hash table cell_nox = 212, cell_noy = 424
HashTable theHashTable;
theHashTable.reserve(size);
for (int n = 0; n < k; ++n) // k = 10*212*424
{
int m = x[n] / cell_width; //cell_width = 4.7e-8
int l = y[n] / cell_width;
int const cellId = (kx*l)+m;
theHashTable[cellId].push_back(n);
}
}
This will not leak memory (reliably), although of course you might have other leaks, and thus will give you a reliable baseline. It is also probably faster than your approach, with a more convenient interface, etc...
In general you should not re-invent the wheel, unless you have a specific need that is not addressed by the available wheels or you are actually trying to learn how to create a wheel or to create a better wheel.
The OS has to solve the same issues with the memory pages, maybe it's worth looking at how that is done? First of all, let's assume all pages are on the disk. A page is a fixed size memory chunk. For your use case, let's say it's an array of your records. Because RAM is limited, the OS maintains a mapping between the page number and it's location in RAM.
So, let's say your pages have 1000 records, and you want to access record 2024, you would ask the OS for page 2, and read record 24 from that page. That way, your map is only 1/1000 in size.
Now, if your page has no mapping to a memory location, then it is either on disk or has never been accessed before (is empty). Then you need to swap out another page, and load that page from disk (and update the location mapping).
This is a very simplified description of what happens and i wouldn't be surprised if someone jumps me in the neck for describing it like this.
The point is:
What does this mean for you?
First of all, your data exceeds your RAM - you won't get around writing to disk, if you don't want to try compression first.
Second, your chains can work as pages if you want, but i wonder whether just paging your hashcode would work better. What i mean is, use the upper bits as page number, and the lower bits as offset in the page. Avoiding collisions is still key, as you want to load the least pages possible. You can still chain your pages, and end up with a much smaller map.
Second - a crucial part is deciding which pages to swap out to make room for the new pages. LRU should do ok. If you can better predict which pages you will (not) need, so much better for you.
Third - you need placeholders for your pages to tell you whether they are in-memory or on disk.
Hope this helps.
I am a mechanical engineer so please understand I am not trained in proper coding. I have a finite element code that uses grids to make elements which make a model. The element is not important to this question so I have left it out. The elements and grids are read in from a file and that part works.
class Grid
{
private:
int id;
double x;
double y;
double z;
public:
Grid();
Grid(int, double, double, double);
int get_id() { return id;};
};
Grid::Grid() {};
Grid::Grid(int t_id, double t_x, double t_y double t_z)
{
id = t_id; x = t_x; y = t_y; z = t_z;
}
class SurfaceModel
{
private:
Grid** grids;
Element** elements;
int grid_count;
int elem_count;
public:
SurfaceModel();
SurfaceModel(int, int);
~SurfaceModel();
void read_grid(std::string);
int get_grid_count() { return grid_count; };
Grid* get_grid(int);
};
SurfaceModel::SurfaceModel()
{
grids = NULL;
elements = NULL;
}
SurfaceModel::SurfaceModel(int g, int e)
{
grids = new Grid*[g];
for (int i = 0; i < g; i++)
grids[i] = NULL;
elements = new Element*[e];
for (int i = 0; i < e; i++)
elements[i] = NULL;
}
void SurfaceModel::read_grid(std::string line)
{
... blah blah ...
grids[index] = new Grid(n_id, n_x, n_y, n_z);
... blah blah ....
}
Grid* SurfaceModel::get_grid(int i)
{
if (i < grid_count)
return grids[i];
else
return NULL;
}
When I need to actually use the grid I use the get_grid maybe something like this:
SurfaceModel model(...);
.... blah blah .....
for (int i = 0; i < model.get_grid_count(); i++)
{
Grid *cur_grid = model.get_grid(i);
int cur_id = cur_grid->get_id();
}
My problem is that the call to get_grid seems to be taking more time than I think it should to simply return my object. I have run the gprof on the code and found that get_grid gets called about 4 billion times when going through a very large simulation and another operation using the x, y, z occurs about the same. The operation does some multiplication. What I found is that the get_grid and math take about the same amount of time (~40 seconds). This seems like I have done something wrong. Is there a faster way to get that object out of there?
I think you're forgetting to set grid_count and elem_count.
This means, they will have uninitialized (indeterminate) values. If you loop for those values, you can easily end up looping a lot of iterations.
SurfaceModel::SurfaceModel()
: grid_count(0),
grids(NULL),
elem_count(0),
elements(NULL)
{
}
SurfaceModel::SurfaceModel(int g, int e)
: grid_count(g),
elem_count(e)
{
grids = new Grid*[g];
for (int i = 0; i < g; i++)
grids[i] = NULL;
elements = new Element*[e];
for (int i = 0; i < e; i++)
elements[i] = NULL;
}
Howeverm, I suggest you would want to get rid of each instance of new in this program (and use a vector for the grid)
On a modern CPU accessing memory often takes longer than doing multiplication. Getting good performance on modern systems can often mean focusing more on optimizing memory accesses than optimizing computation. Because you are storing your grid objects as an array of dynamically allocated pointers the grid objects themselves will be stored non-contiguously in memory and you will likely get many cache misses when trying to access them. In this example you would probably see a significant speedup by storing your grid objects directly in an array or vector since you will be accessing contiguous memory in your loop and so get good cache utilization and effective hardware prefetching.
4 billion times a microsecond (which is a pretty acceptable time in many cases) gives 4 000 seconds. And since you only get about 40 s (if I get it right), I doubt there's something seriously wrong here. If it's still slow for the task, I'd consider the use of parallel computing.
Anyone thought about how to write a memory manager (in C++) that is completely branch free? I've written a pool, a stack, a queue, and a linked list (allocating from the pool), but I am wondering how plausible it is to write a branch free general memory manager.
This is all to help make a really reusable framework for doing solid concurrent, in-order CPU, and cache friendly development.
Edit: by branchless I mean without doing direct or indirect function calls, and without using ifs. I've been thinking that I can probably implement something that first changes the requested size to zero for false calls, but haven't really got much more than that.
I feel that it's not impossible, but the other aspect of this exercise is then profiling it on said "unfriendly" processors to see if it's worth trying as hard as this to avoid branching.
While I don't think this is a good idea, one solution would be to have pre-allocated buckets of various log2 sizes, stupid pseudocode:
class Allocator {
void* malloc(size_t size) {
int bucket = log2(size + sizeof(int));
int* pointer = reinterpret_cast<int*>(m_buckets[bucket].back());
m_buckets[bucket].pop_back();
*pointer = bucket; //Store which bucket this was allocated from
return pointer + 1; //Dont overwrite header
}
void free(void* pointer) {
int* temp = reinterpret_cast<int*>(pointer) - 1;
m_buckets[*temp].push_back(temp);
}
vector< vector<void*> > m_buckets;
};
(You would of course also replace the std::vector with a simple array + counter).
EDIT: In order to make this robust (i.e. handle the situation where the bucket is empty) you would have to add some form of branching.
EDIT2: Here's a small branchless log2 function:
//returns the smallest x such that value <= (1 << x)
int
log2(int value) {
union Foo {
int x;
float y;
} foo;
foo.y = value - 1;
return ((foo.x & (0xFF << 23)) >> 23) - 126; //Extract exponent (base 2) of floating point number
}
This gives the correct result for allocations < 33554432 bytes. If you need larger allocations you'll have to switch to doubles.
Here's a link to how floating point numbers are represented in memory.
The only way I know to create a truly branchless allocator is to reserve all the memory it will potentially use in advance. Otherwise there's always going to be some hidden code somewhere to see if we're exceeding some current capacity whether it's in a hidden push_back in a vector checking if the size exceeds capacity used to implement it or something of that sort.
Here is one such crude example of a fixed alloc which has a completely branchless malloc and free method.
class FixedAlloc
{
public:
FixedAlloc(int element_size, int num_reserve)
{
element_size = max(element_size, sizeof(Chunk));
mem = new char[num_reserve * element_size];
char* ptr = mem;
free_chunk = reinterpret_cast<Chunk*>(ptr);
free_chunk->next = 0;
Chunk* last_chunk = free_chunk;
for (int j=1; j < num_reserve; ++j)
{
ptr += element_size;
Chunk* chunk = reinterpret_cast<Chunk*>(ptr);
chunk->next = 0;
last_chunk->next = chunk;
last_chunk = chunk;
}
}
~FixedAlloc()
{
delete[] mem;
}
void* malloc()
{
assert(free_chunk && free_chunk->next && "Reserve memory exhausted!");
Chunk* chunk = free_chunk;
free_chunk = free_chunk->next;
return chunk->mem;
}
void free(void* mem)
{
Chunk* chunk = static_cast<Chunk*>(mem);
chunk->next = free_chunk;
free_chunk = chunk;
}
private:
union Chunk
{
Chunk* next;
char mem[1];
};
char* mem;
Chunk* free_chunk;
};
Since it's totally branchless, it simply segfaults if you try to allocate more memory than initially reserved. It also has undefined behavior for trying to free a null pointer. I also avoided dealing with alignment for the sake of a simpler example.