performance

问题 This question already has answers here : Closed 9 years ago . Possible Duplicate: When should I use a List vs a LinkedList If I expect not to use the access by index for my data structure how much do I save by using LinkedList over List ? If if am not 100% sure I will never use access by index, I would like to know the difference. Suppose I have N instances. inserting and removing in a LinkedList will only be a o(1) op , where as in List it may me be O(n), but since it it optimized, it would

问题 I need to read a binary blob from file into a JavaScript array. The blob is little endian, uint 12 bit, I.e. --------------------------------- | 7 | 6 | 5 | 4 | 3 | 2 | 1 | 0 | |-------------------------------| | data1[7:0] | |-------------------------------| | data2[3:0] | data1[11:8] | |-------------------------------| | data2[11:4] | --------------------------------- It seems like TypedArrays and bit shifting might be the best way (that's how I solved in Python), but I'm trying to make

问题 I was happily using Map for indexed accessed everywhere in my JavaScript codebase, but I've just stumbled upon this benchmark: https://stackoverflow.com/a/54385459/365104 I've re-created it here as well: https://jsben.ch/HOU3g What benchmark is doing is basically filling a map with 1M elements, then iterating over them. I'd expect the results for Map and Object to be on par, but they differ drastically - in favor of Object. Is this expected behavior? Can it be explained? Is it because of the

问题 I have one very large (10mb) csv file. I parsed it and put it into memory using a generic list. I created a class to represent each line. This class has only several fields (data type ip-address, string). I thoguht that since the file is only 10 megabytes I could expect a similar size in-memory. I was quite surprised when I found out that the method that is creating the list is allocating 300 mb and not freeing it up. Is this normal, and what can be causing this. Note that the csv file has

问题 I have one very large (10mb) csv file. I parsed it and put it into memory using a generic list. I created a class to represent each line. This class has only several fields (data type ip-address, string). I thoguht that since the file is only 10 megabytes I could expect a similar size in-memory. I was quite surprised when I found out that the method that is creating the list is allocating 300 mb and not freeing it up. Is this normal, and what can be causing this. Note that the csv file has

问题 The snippet of my serial code is shown below. Program main use omp_lib Implicit None Integer :: i, my_id Real(8) :: t0, t1, t2, t3, a = 0.0d0 !$ t0 = omp_get_wtime() Call CPU_time(t2) ! ------------------------------------------ ! Do i = 1, 100000000 a = a + Real(i) End Do ! ------------------------------------------ ! Call CPU_time(t3) !$ t1 = omp_get_wtime() ! ------------------------------------------ ! Write (*,*) "a = ", a Write (*,*) "The wall time is ", t1-t0, "s" Write (*,*) "The CPU

问题 The below program is a simple threaded program . For some reason which i am not able to figure , its getting stuck at infinite loop of both produce() and consume() methods simultaneously in both the threads. It produces the output for a few times and then there is no output at the console. So I presume its getting stuck at the loop. My question is , since the loop depends on the value of the flag valueSet of the same object of Item class , valueSet can't be both true and false at the same

问题 I have a list of indices: indx = torch.LongTensor([ [ 0, 2, 0], [ 0, 2, 4], [ 0, 4, 0], [ 0, 10, 14], [ 1, 4, 0], [ 1, 8, 2], [ 1, 12, 0] ]) And I have a tensor of 2x2 blocks: blocks = torch.FloatTensor([ [[1.5818, 2.3108], [2.6742, 3.0024]], [[2.0472, 1.6651], [3.2807, 2.7413]], [[1.5587, 2.1905], [1.9231, 3.5083]], [[1.6007, 2.1426], [2.4802, 3.0610]], [[1.9087, 2.1021], [2.7781, 3.2282]], [[1.5127, 2.6322], [2.4233, 3.6836]], [[1.9645, 2.3831], [2.8675, 3.3770]] ]) What I want to do is to

问题 I have a list of indices: indx = torch.LongTensor([ [ 0, 2, 0], [ 0, 2, 4], [ 0, 4, 0], [ 0, 10, 14], [ 1, 4, 0], [ 1, 8, 2], [ 1, 12, 0] ]) And I have a tensor of 2x2 blocks: blocks = torch.FloatTensor([ [[1.5818, 2.3108], [2.6742, 3.0024]], [[2.0472, 1.6651], [3.2807, 2.7413]], [[1.5587, 2.1905], [1.9231, 3.5083]], [[1.6007, 2.1426], [2.4802, 3.0610]], [[1.9087, 2.1021], [2.7781, 3.2282]], [[1.5127, 2.6322], [2.4233, 3.6836]], [[1.9645, 2.3831], [2.8675, 3.3770]] ]) What I want to do is to

问题 Given: #include <iostream> #include <functional> template<class T> // Just for overloading purposes struct behaviour1 : std::reference_wrapper<T const> { using base_t = std::reference_wrapper<T const>; using base_t::base_t; // This wrapper will never outlive the temporary // if used correctly behaviour1(T&& t) : base_t(t) {} }; template<class T> behaviour1(T&&) -> behaviour1<std::decay_t<T> >; struct os_wrapper : std::reference_wrapper<std::ostream> { using std::reference_wrapper<std::ostream