optimization

问题 I have a book collection with currently 7.7 million records and I have setup a text index as follows that will allow me to search the collection by title and author as follows: db.book.createIndex( { title: "text", author: "text" }, {sparse: true, background: true, weights: {title: 15, author: 5}, name: "text_index"} ) The problem is when I use a search query that will return a lot of results eg John and then sort by the textScore the time to perform the query is over 60 seconds. Please see

问题 I have a book collection with currently 7.7 million records and I have setup a text index as follows that will allow me to search the collection by title and author as follows: db.book.createIndex( { title: "text", author: "text" }, {sparse: true, background: true, weights: {title: 15, author: 5}, name: "text_index"} ) The problem is when I use a search query that will return a lot of results eg John and then sort by the textScore the time to perform the query is over 60 seconds. Please see

问题 I'm trying to write the C code for an algorithm that approximates pi . It's supposed to get the volume of a cube and the volume of a sphere inside that cube (the sphere's radius is 1/2 of the cube's side). Then I am supposed to divide the cube's volume by the sphere's and multiply by 6 to get pi. It's working but it's doing something weird in the part that is supposed to get the volumes. I figure it's something to do the with delta I chose for the approximations. With a cube of side 4 instead

问题 I have some legacy code that compiles with both -02 and -03 set. From the GCC man file I get the guarantee that: -O3 turns on all optimizations specified by -O2 and also turns on the -finline-functions, -funswitch-loops, -fpredictive-commoning, -fgcse-after-reload and -ftree-vectorize options. So, at first glance it would seem likely that turning both of these flags on would be the same as just -O3. However, that got me thinking is that the right thing to do in that case as -O2 is probably

问题 Lets say I have an abstract class that is expensive to create and copy: class AbstractBase { public: AbstractBase() { for (int i = 0; i < 50000000; ++i) { values.push_back(i); } } virtual void doThing() = 0; private: vector<int> values; }; It has two subclasses FirstDerived : class FirstDerived : public AbstractBase { public: void doThing() { std::cout << "I did the thing in FirstDerived!\n"; } }; and SecondDerived : class SecondDerived : public AbstractBase { public: void doThing() { std:

问题 I have a code in C which does additions in the same way as a human does, so if for example I have two arrays A[0..n-1] and B[0..n-1] , the method will do C[0]=A[0]+B[0] , C[1]=A[1]+B[1] ... I need help in making this function faster, even if the solution is using intrinsics. My main problem is that I have a really big dependency problem, as the iteration i+1 depends on the carry of the iteration i , as long as I use base 10. So if A[0]=6 and B[0]=5 , C[0] must be 1 and I have a carry of 1 for

问题 After few hours of debugging, I realized that a very simple toy example was not efficient due to a missing ! in an expression return $ 1 + x (thanks duplode!... but how come ghc does not optimize that??). I also realized it because I was comparing it with a Python code that was quicker, but I won't always write Python code to benchmark my code... So here is my question: is there a way to automatically detect these "lazy memory leaks", that slow down a program for no real reason? I'm still

问题 I have this functions, the result is correct but the compiler don't vectorize this. How can I achive that the compiler vectorize this and how can I optimize this codes? void LongNumSet( char *L, unsigned N, char digit ) { for (int i = 0; i < N; ++i){ L[i] = digit; } } void LongNumCopy( char *Vin, char *Vout, unsigned N ) { for ( int i=0; i< N; ++i ) { Vout[i] = Vin[i]; } } char LongNumAddition( char *__restrict Vin1, char * __restrict Vin2, char * __restrict Vout, unsigned N ) { char CARRY =

问题 I have this functions, the result is correct but the compiler don't vectorize this. How can I achive that the compiler vectorize this and how can I optimize this codes? void LongNumSet( char *L, unsigned N, char digit ) { for (int i = 0; i < N; ++i){ L[i] = digit; } } void LongNumCopy( char *Vin, char *Vout, unsigned N ) { for ( int i=0; i< N; ++i ) { Vout[i] = Vin[i]; } } char LongNumAddition( char *__restrict Vin1, char * __restrict Vin2, char * __restrict Vout, unsigned N ) { char CARRY =

问题 Is there any difference (performance or otherwise) between the following two statements in Python? v = my_dict.get(key, some_default) vs v = my_dict.get(key) or some_default 回答1: There is a huge difference if your value is false-y: >>> d = {'foo': 0} >>> d.get('foo', 'bar') 0 >>> d.get('foo') or 'bar' 'bar' You should not use or default if your values can be false-y. On top of that, using or adds additional bytecode; a test and jump has to be performed. Just use dict.get() , there is no