With my current project, I did my best to adhere to the principle that premature optimization is the root of all evil. However, now the code is tested, and it is time for optimization. I did some profiling, and it turns out my code spends almost 20% of its time in a function where it finds all possible children, puts them in a vector, and returns them. As a note, I am optimizing for speed, memory limitations are not a factor.
Right now the function looks like this:
void Board::GetBoardChildren(std::vector<Board> &children)
{
children.reserve(open_columns_.size()); // only reserve max number of children
UpdateOpenColumns();
for (auto i : open_columns_)
{
short position_adding_to = ColumnToPosition(i);
MakeMove(position_adding_to); // make the possible move
children.push_back(*this); // add to vector of children
ReverseMove(); // undo move
}
}
According to the profiling, my code spends about 40% of the time just on the line children.push_back(*this); I am calling the function like this:
std::vector<Board> current_children;
current_state.GetBoardChildren(current_children);
I was thinking since the maximum number of possible children is small (7), would it be better to just use an array? Or is there not a ton I can do to optimize this function?
From your responses to my comments, it seems very likely that most of the time is spent copying the board in
children.push_back(*this);
You need to find a way to avoid making all those copies, or a way to make them cheaper.
Simply changing the vector into an array or a list will likely not make any difference to performance.
The most important question is: Do you really need all States at once inside current_state?
If you just iterate over them once or twice in the default order, then there is no need for a vector, just generate them on demand.
If you really need it, here is the next step. Since Board is expensive for copying, a DifferenceBoard that keeps only track of the difference may be better. Pseudocode:
struct DifferenceBoard { // or maybe inherit from Board that a DifferenceBoard
// can be built from another DifferenceBoard
Board *original;
int fromposition, toposition;
State state_at_position;
State get(int y, int x) const {
if ((x,y) == fromposition) return Empty;
if ((x,y) == toposition ) return state_at_position;
return original->get();
}
};
Related
I want to improve the performance of the following code. What aspect might affect the performance of the code when it's executed?
Also, considering that there is no limit to how many objects you can add to the container, what improvements could be made to “Object” or “addToContainer” to improve the performance of the program?
I was wondering if std::push_back in C++ affects performance of the code in any way? Especially if there is no limit to adding to list.
struct Object{
string name;
string description;
};
vector<Object> container;
void addToContainer(Object object) {
container.push_back(object);
}
int main() {
addToContainer({ "Fira", "+5 ATTACK" });
addToContainer({ "Potion", "+10 HP" });
}
Before you do ANYTHING profile the code and get a benchmark. After you make a change profile the code and get a benchmark. Compare the benchmarks. If you do not do this, you're rolling dice. Is it faster? Who knows.
Profile profile profile.
With push_back you have two main concerns:
Resizing the vector when it fills up, and
Copying the object into the vector.
There are a number of improvements you can make to the resizing cost cost of push_back depending on how items are being added.
Strategic use of reserve to minimize the amount of resizing, for example. If you know how many items are about to be added, you can check the capacity and size to see if it's worth your time to reserve to avoid multiple resizes. Note this requires knowledge of vector's expansion strategy and that is implementation-specific. An optimization for one vector implementation could be a terribly bad mistake on another.
You can use insert to add multiple items at a time. Of course this is close to useless if you need to add another container into the code in order to bulk-insert.
If you have no idea how many items are incoming, you might as well let vector do its job and optimize HOW the items are added.
For example
void addToContainer(Object object) // pass by value. Possible copy
{
container.push_back(object); // copy
}
Those copies can be expensive. Get rid of them.
void addToContainer(Object && object) //no copy and can still handle temporaries
{
container.push_back(std::move(object)); // moves rather than copies
}
std::string is often very cheap to move.
This variant of addToContainer can be used with
addToContainer({ "Fira", "+5 ATTACK" });
addToContainer({ "Potion", "+10 HP" });
and might just migrate a pointer and as few book-keeping variables per string. They are temporaries, so no one cares if it will rips their guts out and throws away the corpses.
As for existing Objects
Object o{"Pizza pop", "+5 food"};
addToContainer(std::move(o));
If they are expendable, they get moved as well. If they aren't expendable...
void addToContainer(const Object & object) // no copy
{
container.push_back(object); // copy
}
You have an overload that does it the hard way.
Tossing this one out there
If you already have a number of items you know are going to be in the list, rather than appending them all one at a time, use an initialization list:
vector<Object> container{
{"Vorpal Cheese Grater", "Many little pieces"},
{"Holy Hand Grenade", "OMG Damage"}
};
push_back can be extremely expensive, but as with everything, it depends on the context. Take for example this terrible code:
std::vector<float> slow_func(const float* ptr)
{
std::vector<float> v;
for(size_t i = 0; i < 256; ++i)
v.push_back(ptr[i]);
return v;
}
each call to push_back has to do the following:
Check to see if there is enough space in the vector
If not, allocate new memory, and copy the old values into the new vector
copy the new item to the end of the vector
increment end
Now there are two big problems here wrt performance. Firstly each push_back operation depends upon the previous operation (since the previous operation modified end, and possibly the entire contents of the array if it had to be resized). This pretty much destroys any vectorisation possibilities in the code. Take a look here:
https://godbolt.org/z/RU2tM0
The func that uses push_back does not make for very pretty asm. It's effectively hamstrung into being forced to copy a single float at a time. Now if you compare that to an alternative approach where you resize first, and then assign; the compiler just replaces the whole lot with a call to new, and a call to memcpy. This will be a few orders of magnitude faster than the previous method.
std::vector<float> fast_func(const float* ptr)
{
std::vector<float> v(256);
for(size_t i = 0; i < 256; ++i)
v[i] = ptr[i];
return v;
}
BUT, and it's a big but, the relative performance of push_back very much depends on whether the items in the array can be trivially copied (or moved). If you example you do something silly like:
struct Vec3 {
float x = 0;
float y = 0;
float z = 0;
};
Well now when we did this:
std::vector<Vec3> v(256);
The compiler will allocate memory, but also be forced to set all the values to zero (which is pointless if you are about to overwrite them again!). The obvious way around this is to use a different constructor:
std::vector<Vec3> v(ptr, ptr + 256);
So really, only use push_back (well, really you should prefer emplace_back in most cases) when either:
additional elements are added to your vector occasionally
or, The objects you are adding are complex to construct (in which case, use emplace_back!)
without any other requirements, unfortunately this is the most efficient:
void addToContainer(Object) { }
to answer the rest of your question. In general push_back will just add to the end of the allocated vector O(1), but will need to grow the vector on occasion, which can be amortized out but is O(N)
also, it would likely be more efficient not to use string, but to keep char * although memory management might be tricky unless it is always a literal being added
I am currently running into a disgusting problem. Suppose there is a list aList of objects(whose type we call Object), and I want to iterate through it. Basically, the code would be like this:
for(int i = 0; i < aList.Size(); ++i)
{
aList[i].DoSth();
}
The difficult part here is, the DoSth() method could change the caller's position in the list! So two consequences could occur: first, the iteration might never be able to come to an end; second, some elements might be skipped (the iteration is not necessarily like above, since it might be a linked list). Of course, the first one is the major concern.
The problem must be solved with these constraints:
1) The possibility of doing position-exchanging operations cannot be excluded;
2) The position-exchanging operations can be delayed until the iteration finishes, if necessary and doable;
3) Since it happens quite often, the iteration can be modified only minimally (so actions like creating a copy of the list is not recommended).
The language I'm using is C++, but I think there are similar problems in JAVA and C#, etc.
The following are what I've tried:
a) Try forbidding the position-exchanging operations during the iteration. However, that involves too many client code files and it's just not practical to find and modify all of them.
b) Modify every single method(e.g., Method()) of Object that can change the position of itself and will be called by DoSth() directly or indirectly, in this way: first we can know that aList is doing the iteration, and we'll treat Method() accordingly. If the iteration is in progress, then we delay what Method() wants to do; otherwise, it does what it wants to right now. The question here is: what is the best (easy-to-use, yet efficient enough) way of delaying a function call here? The parameters of Method() could be rather complex. Moreover, this approach will involve quite a few functions, too!
c) Try modifying the iteration process. The real situation I encounter here is quite complex because it involves two layers of iterations: the first of them is a plain array iteration, while the second is a typical linked list iteration lying in a recursive function. The best I can do about the second layer of iteration for now, is to limit its iteration times and prevent the same element from being iterated more than once.
So I guess there could be some better way to tackle this problem? Maybe some awesome data structure will help?
Your question is a little light on detail, but from what you have written it seems that you are making the mistake of mixing concerns.
It is likely that your object can perform some action that causes it to either continue to exist or not. The decision that it should no longer exist is a separate concern to that of actually storing it in a container.
So let's split those concerns out:
#include <vector>
enum class ActionResult {
Dies,
Lives,
};
struct Object
{
ActionResult performAction();
};
using Container = std::vector<Object>;
void actions(Container& cont)
{
for (auto first = begin(cont), last = end(cont)
; first != last
; )
{
auto result = first->performAction();
switch(result)
{
case ActionResult::Dies:
first = cont.erase(first); // object wants to die so remove it
break;
case ActionResult::Lives: // object wants to live to continue
++first;
break;
}
}
}
If there are indeed only two results of the operation, lives and dies, then we could express this iteration idiomatically:
#include <algorithm>
// ...
void actions(Container& cont)
{
auto actionResultsInDeath = [](Object& o)
{
auto result = o.performAction();
return result == ActionResult::Dies;
};
cont.erase(remove_if(begin(cont), end(cont),
actionResultsInDeath),
end(cont));
}
Well, problem solved, at least in regard to the situation I'm interested in right now. In my situation, aList is really a linked list and the Object elements are accessed through pointers. If the size of aList is relatively small, then we have an elegant solution just like this:
Object::DoSthBig()
{
Object* pNext = GetNext();
if(pNext)
pNext->DoSthBig();
DoSth();
}
This has the underlying hypothesis that each pNext keeps being valid during the process. But if the element-deletion operation has already been dealt with discreetly, then everything is fine.
Of course, this is a very special example and is unable to be applied to other situations.
I am currently going through some code and I currently have a road class, with a vector of pointers to lanes (a private member), and this road class includes a lane class. This lane class contains a vector of pointers to vehicles, which is another class that contains simple get and set functions to update and obtain a vehicle's position, velocity etc. Now, I have vehicles moving in separate lanes and I allow them to switch lanes, as it is so in traffic flow. However, I would like my vehicles to continuously find a distance from it and the vehicle in front, i.e., look in the vehicles vector and find the closest vehicle. Then I intend to use that to instruct whether a car should decelerate or not. I would also like to make sure that cars which are leading the rest, since once a vehicle leaves the displaywindow height, they should be deleted.
My attempt at this is as follows:
void Lane::Simulate(double time)
{ // This simulate allows check between other vehicles.
double forwardDistance = 0;
for (unsigned int iV = 0; iV < fVehicles.size(); iV++)
{
for(unsigned int jV = 0; jV < fVehicles.size(); jV++)
{
forwardDistance = fVehicles[iV]->getPosition() - fVehicles[jV]->getPosition();
}
}
if(fVehicles.size() < 15)
{
addRanVehicle(); // Adds a vehicle, with position zero but random velocities, to each lane.
}
for (unsigned int iVehicle = 0; iVehicle < fVehicles.size(); iVehicle++)
{
fVehicles[iVehicle]->Simulate(time); // Updates position based on time, velocity and acceleration.
}
}
There may be a much better method than using this forwardDistance parameter. The idea is to loop over each pair of vehicles, avoid the point iV == jV, and find the vehicle which is in front of the iVth vehicle, and record the distance between the two vehicles into a setDistance() function (which is a function of my Vehicle class). I should then be able to use this to check whether a car is too close, check whether it can overtake, or whether it just has to brake.
Currently, I am not sure how to make an efficient looping mechanism for this.
Investigate the cost of performing an ordered insert of Vehicles into the lane. If the Vehicles are ordered according to position on the road, detecting the distance of two Vehicles is child's play:
Eg
for (size_t n = 0; n < fVehicles.size() - 1; n++)
{
distance = fVehicles[n].getPosition() - fVehicles[n+1].getPosition();
}
This is O(N) vs O(N^2) (using ^ as exponent, not XOR). The price of this simplification is the requiring ordered insert into fVehicles, and that should be O(N): One std::binary_search to detect the insertion point and whatever shuffling is required by fVehicles to free up space to place the Vehicle.
Maintaining ordering of fVehicles may be beneficial in other places as well. Visualizing the list (graphically or by print statements) will be much easier, debugging is generally easier on the human brain when everything is in a nice predictable order, and CPUs... They LOVE going in a nice, predictable straight line. Sometimes you get a performance boost that you didn't see coming. Great write-up on that here: Why is it faster to process a sorted array than an unsorted array?
Only way to be sure if this is better is to try it and measure it.
Other Suggestions:
Don't use pointers to the vehicles.
Not only are they harder to manage, they can slow you down quite a bit. As mentioned above, modern CPUs are really good at going in straight lines, and pointers can throw a kink in that straight line.
You never really know where in dynamic memory a pointer is going to be relative to the last pointer you looked at. But with a contiguous block of Vehicles , when the CPU loads Vehicle N it can possibly also grab Vehicles N+1 and N+2. If it can't because they are too big, it doesn't matter much because it already knows where they are, and while the CPU is processing, and idle memory channel could be reading ahead and grabbing the data you're going to need soon.
With the pointer you save a bit every time you move a Vehicle from lane to lane (pointers are usually much cheaper than objects to copy), but may suffer on each and every loop iteration in each and every simulation tick and the volume really adds up. Bjarne Stroustrup, God-Emperor of C++, has an excellent write up on this problem using linked lists as an example (Note linked list is often worse than vector of pointer, but the idea is the same).
Take advantage of std::deque.
std::vector Is really good at stack-like behaviour. You can add to and remove from the end lightning fast, but if you add to or remove from the beginning, everything in the vector is moved.
Most of the lane insertions are likely to be at one end and the removals at the other simply because older Vehicles will gravitate toward the end as Vehicles are added to the beginning or vise versa. This is a certainty if suggestion 1 is taken and fVehicles is ordered. New vehicles will be added to the lane at the beginning, a few will change lanes into or out of the middle, and old vehicles will be removed from the end. deque is optimized for inserting and removing at both ends so adding new cars is cheap, removing old cars is cheap and you only pay full price for cars that change lanes.
Documentation on std::deque
Addendum
Take advantage of range-based for where possible. Range-based for takes most of the iteration logic away and hides it from you.
Eg this
for (unsigned int iV = 0; iV < fVehicles.size(); iV++)
{
for(unsigned int jV = 0; jV < fVehicles.size(); jV++)
{
forwardDistance = fVehicles[iV]->getPosition() - fVehicles[jV]->getPosition();
}
}
becomes
for (auto v_outer: fVehicles)
{
for (auto v_inner: fVehicles)
{
forwardDistance = v_outer->getPosition() - v_inner->getPosition();
}
}
It doesn't look much better if you are counting lines, but you can't accidentally
iV <= fVehicles.size()
or
fVehicles[iV]->getPosition() - fVehicles[iV]->getPosition()
It removes the possibility for you to make silly, fatal, and hard-to-spot errors.
Let's break one down:
for (auto v_outer: fVehicles)
^ ^ ^
type | |
variable name |
Container to iterate
Documentation on Range-based for
In this case I'm also taking advantage of auto. auto allows the compiler to select the type of the data. The compiler knows that fVehicles contains pointers to Vehicles, so it replaces auto with Vehicle * for you. This takes away some of the headaches if you find yourself refactoring the code later.
Documentation on auto
Unfortunately in this cans it can also trap you. If you follow the suggestions above, fVehicles becomes
std::dequeue<Vehicle> fVehicles;
which means auto is now Vehicle. Which makes v_outer a copy, costing you copying time and meaning if you change v_outer, you change a copy and the original goes unchanged. to avoid that, tend toward
for (auto &v_outer: fVehicles)
The compiler is good at deciding how best to handle that reference or if it even needs it.
I am a bit curiuous about vector optimization and have couple questions about it. (I am still a beginner in programing)
example:
struct GameInfo{
EnumType InfoType;
// Other info...
};
int _lastPosition;
// _gameInfoV is sorted beforehand
std::vector<GameInfo> _gameInfoV;
// The tick function is called every game frame (in "perfect" condition it's every 1.0/60 second)
void BaseClass::tick()
{
for (unsigned int i = _lastPosition; i < _gameInfoV.size(); i++{
auto & info = _gameInfoV[i];
if( !info.bhasbeenAdded ){
if( DoWeNeedNow() ){
_lastPosition++;
info.bhasbeenAdded = true;
_otherPointer->DoSomething(info.InfoType);
// Do something more with "info"....
}
else return; //Break the cycle since we don't need now other "info"
}
}
}
The _gameInfoV vector size can be between 2000 and 5000.
My main 2 questions are:
Is it better to leave the way how it is or it's better to make smaller chunks of it, which is checked for every different GameInfo.InfoType
Is it worth the hassle of storing the last start position index of the vector instead of iterating from the beginning.
Note that if using smaller vectors there will be like 3 to 6 of them
The third thing is probably that I am not using vector iterators, but is it safe to use then like this?
std::vector<GameInfo>::iterator it = _gameInfoV.begin() + _lastPosition;
for (it = _gameInfoV.begin(); it != _gameInfoV.end(); ++it){
//Do something
}
Note: It will be used in smartphones, so every optimization will be appreciated, when targeting weaker phones.
-Thank you
Don't; except if you frequently move memory around
It is no hassle if you do it correctly:
std::vector<GameInfo>::const_iterator _lastPosition(gameInfoV.begin());
// ...
for (std::vector<GameInfo>::iterator info=_lastPosition; it!=_gameInfoV.end(); ++info)
{
if (!info->bhasbeenAdded)
{
if (DoWeNeedNow())
{
++_lastPosition;
_otherPointer->DoSomething(info->InfoType);
// Do something more with "info"....
}
else return; //Break the cycle since we don't need now other "i
}
}
Breaking one vector up into several smaller vectors in general doesn't improve performance. It could even slightly degrade performance because the compiler has to manage more variables, which take up more CPU registers etc.
I don't know about gaming so I don't understand the implication of GameInfo.InfoType. Your processing time and CPU resource requirements are going to increase if you do more total iterations through loops (where each loop iteration performs the same type of operation). So if separating the vectors causes you to avoid some loop iterations because you can skip entire vectors, that's going to increase performance of your app.
iterators are the most secure way to iterate through containers. But for a vector I often just use the index operator [] and my own indexer (a plain old unsigned integer).
Imagine you have a pretty big array of double and a simple function avg(double*,size_t) that computes the average value (just a simple example: both the array and the function could be whatever data structure and algorithm). I would like that if the function is called a second time and the array is not changed in the meanwhile, the return value comes directly from the previous one, without going through the unchanged data.
To hold the previous value looks simple, I just need a static variable inside the function, right? But what about detecting the changes in the array? Do I need to write an interface to access the array which sets a flag to be read by the function? Can something smarter and more portable be done?
As Kerrek SB so astutely put it, this is known as "memoization." I'll cover my personal favorite method at the end (both with double* array and the much easier DoubleArray), so you can skip to there if you just want to see code. However, there are many ways to solve this problem, and I wanted to cover them all, including those suggested by others. Skip to the horizontal rule if you just want to see code.
The first part is some theory and alternate approaches. There are fundamentally four parts to the problem:
Prove the function is idempotent (calling a function once is the same as calling it any number of times)
Cache results keyed to the inputs
Search cached results given a new set of inputs
Invalidating cached results which are no longer accurate/current
The first step is easy for you: average is idempotent. It has no side effects.
Caching the results is a fun step. You obviously are going to create some "key" for the inputs that you can compare against the cached "keys." In Kerrek SB's memoization example, the key is a tuple of all of the arguments, compared against other keys with ==. In your system, the equivalent solution would be to have the key be the contents of the entire array. This means each key comparison is O(n), which is expensive. If the function was more expensive to calculate than the average function is, this price may be acceptable. However in the case of averaging, this key is terribly expensive.
This leads one on the open-ended search for good keys. Dieter Lücking's answer was to key the array pointer. This is O(1), and wicked fast to boot. However, it also makes the assumption that once you've calculated the average for an array, that array's values never change, and that memory address is never re-used for another array. Solutions for this come later, in the invalidation portion of the task.
Another popular key is HotLick's (1) in the comments. You use a unique identifier for the array (pointer or, better yet, a unique integer idx that will never be used again) as your key. Each array then has a "dirty bit for avg" that they are expected to set to true whenever a value is changed. Caches first look for the dirty bit. If it is true, they ignore the cached value, calculate the new value, cache the new value, then clear the dirty bit indicating that the cached value is now valid. (this is really invalidation, but it fit well in this part of the answer)
This technique assumes that there are more calls to avg than updates to the data. If the array is constantly dirty, then avg still has to keep recalculating, but we still pay the price of setting the dirty bit on every write (slowing it down).
This technique also assumes that there is only one function, avg, which needs cached results. If you have many functions, it starts to get expensive to keep all of the dirty bits up to date. The solution is an "epoch" counter. Instead of a dirty bit, you have an integer, which starts at 0. Every write increments it. When you cache a result, you cache not only the identity of the array, but its epoch as well. When you check to see if you have a cached value, you also check to see if the epoch changed. If it did change, you can't prove your old results are current, and have to throw them out.
Storing the results is an interesting task. It is very easy to write a storing algorithm which uses up gobs of memory by remembering hundreds of thousands of old results to avg. Generally speaking, there needs to be a way to let the caching code know that an array has been destroyed, or a way to slowly remove old unused cache results. In the former case, the deallocator of the double arrays needs to let the cache code know that that array is being deallocated. In the latter case, it is common to limit a cache to 10 or 100 entries, and have evict old cache results.
The last piece is invalidation of caches. I spoke earlier regarding the dirty bit. The general pattern for this is that a value inside a cache must be marked invalid if the key it was stored in didn't change, but the values in the array did change. This can obviously never happen if the key is a copy of the array, but it can occur when the key is an identifing integer or a pointer.
Generally speaking, invalidation is a way to add a requirement to your caller: if you want to use avg with caching, here's the extra work you are required to do to help the caching code.
Recently I implemented a system with such caching invalidation scheme. It was very simple, and stemmed from one philosophy: the code which is calling avg is in a better position to determine if the array has changed than avg is itself.
There were two versions of the equvalent of avg: double avg(double* array, int n) and double avg(double* array, int n, CacheValidityObject& validity).
Calling the 2 argument version of avg never cached, because it had no guarantees that array had not changed.
Calling the 3 argument version of avg activated caching. The caller guarentees that, if it passes the same CacheValidityObject to avg without marking it dirty, then the arrays must be the same.
Putting the onus on the caller makes average trivial. CacheValidityObject is a very simple class to hold on to the results
class CacheValidityObject
{
public:
CacheValidityObject(); // creates a new dirty CacheValidityObject
void invalidate(); // marks this object as dirty
// this function is used only by the `avg` algorithm. "friend" may
// be used here, but this example makes it public
boost::shared_ptr<void>& getData();
private:
boost::shared_ptr<void> mData;
};
inline void CacheValidityObject::invalidate()
{
mData.reset(); // blow away any cached data
}
double avg(double* array, int n); // defined as usual
double avg(double* array, int n, CacheValidityObject& validity)
{
// this function assumes validity.mData is null or a shared_ptr to a double
boost::shared_ptr<void>& data = validity.getData();
if (data) {
// The cached result, stored on the validity object, is still valid
return *static_pointer_cast<double>(data);
} else {
// There was no cached result, or it was invalidated
double result = avg(array, n);
data = make_shared<double>(result); // cache the result
return result;
}
}
// usage
{
double data[100];
fillWithRandom(data, 100);
CacheValidityObject dataCacheValidity;
double a = avg(data, 100, dataCacheValidity); // caches the aveerage
double b = avg(data, 100, dataCacheValidity); // cache hit... uses cached result
data[0] = 0;
dataCacheValidity.invalidate();
double c = avg(data, 100, dataCacheValidity); // dirty.. caches new result
double d = avg(data, 100, dataCacheValidity); // cache hit.. uses cached result
// CacheValidityObject::~CacheValidityObject() will destroy the shared_ptr,
// freeing the memory used to cache the result
}
Advantages
Nearly the fastest caching possible (within a few opcodes)
Trivial to implement
Doesn't leak memory, saving cached values only when the caller thinks it may want to use them again
Disadvantages
Requires the caller to handle caching, instead of doing it implicitly for them.
If you wrap the double* array in a class, you can minimize the disadvantage. Assign each algorithm an index (can be done at run time) Have the DoubleArray class maintain a map of cached values. Each modification to DoubleArray invalidates the cached results. This is the most easy to use version, but doesn't work with a naked array... you need a class to help you out
class DoubleArray
{
public:
// all of the getters and setters and constructors.
// Special note: all setters MUST call invalidate()
CacheValidityObject getCache(int inIdx)
{
return mCaches[inIdx];
}
void setCache(int inIdx, const CacheValidityObject& inObj)
{
mCaches[inIdx] = inObj;
}
private:
void invalidate()
{
mCaches.clear();
}
std::map<int, CacheValidityObject> mCaches;
double* mArray;
int mSize;
};
inline int getNextAlgorithmIdx()
{
static int nextIdx = 1;
return nextIdx++;
}
static const int avgAlgorithmIdx = getNextAlgorithmIdx();
double avg(DoubleArray& inArray)
{
CacheValidityObject valid = inArray.getCache(avgAlgorithmIdx);
// use the 3 argument avg in the previous example
double result = avg(inArray.getArray(), inArray.getSize(), valid);
inArray.setCache(avgAlgorithmIdx, valid);
return result;
}
// usage
DoubleArray array(100);
fillRandom(array);
double a = avg(array); // calculates, and caches
double b = avg(array); // cache hit
array.set(0, 5); // invalidates caches
double c = avg(array); // calculates, and caches
double d = avg(array); // cache hit
#include <limits>
#include <map>
// Note: You have to manage cached results - release it with avg(p, 0)!
double avg(double* p, std::size_t n) {
typedef std::map<double*, double> map;
static map results;
map::iterator pos = results.find(p);
if(n) {
// Calculate or get a cached value
if(pos == results.end()) {
pos = results.insert(map::value_type(p, 0.5)).first; // calculate it
}
return pos->second;
}
// Erase a cached value
results.erase(pos);
return std::numeric_limits<double>::quiet_NaN();
}