memory

问题 TL;DR Question Regarding numpy arrays that are neighter C or F contiguous (array's c_contiguous and f_contiguous flags are False): Can an array really be neither C or F contiguous? Or falsy flags just mean numpy can't figure out the correct contigious type? What are the performance implications on such arrays? Are there any optimizations we miss when staying in this state? An array for example: import numpy as np arr = np.random.randint(0, 255, (1000, 1000, 3), dtype='uint8') arr = arr[:, :,

问题 TL;DR Question Regarding numpy arrays that are neighter C or F contiguous (array's c_contiguous and f_contiguous flags are False): Can an array really be neither C or F contiguous? Or falsy flags just mean numpy can't figure out the correct contigious type? What are the performance implications on such arrays? Are there any optimizations we miss when staying in this state? An array for example: import numpy as np arr = np.random.randint(0, 255, (1000, 1000, 3), dtype='uint8') arr = arr[:, :,

问题 I am building a memory game with four cards(2x2). These four cards have an onClick named "cards". This onClick consists of an If statement that flips the cards back if they are not the same, and keeps them if they are the same. The front image of the card is the same for the 4, but the back has different images.My problem is that I want the cards to flip, but they already have an onClick. So how can I write "if button clicked" in an If statement or is there another solution? EDIT: button1

问题 I am using 64-bit Linux and Java JVM. I want to confirm if the memory used by JVM is smaller than physical memory size of the machine, there will be no disk memory swap by OS? 回答1: No, that's not necessarily true. Physical memory is shared by all processes, as well as by a bunch of other kernel things (e.g. the disk cache). So the amount of virtual memory used by your application is not the only consideration. 回答2: You can start your java application with the jvm argument -Xmx512m wich will

问题 I'm trying to implement a linked list in a CUDA application to model a growing network. In oder to do so I'm using malloc inside the __device__ function, aiming to allocate memory in the global memory. The code is: void __device__ insereviz(Vizinhos **lista, Nodo *novizinho, int *Gteste) { Vizinhos *vizinho; vizinho=(Vizinhos *)malloc(sizeof(Vizinhos)); vizinho->viz=novizinho; vizinho->proxviz=*lista; *lista=vizinho; novizinho->k=novizinho->k+1; } After a certain number of allocated elements

问题 I am working with an array of structure, and I want for each block to load in shared memory one cell of the array. For example : block 0 will load array[0] in shared memory and block 1 will load array[1]. In order to do that I cast the array of structure in float* in order to try to coalesce memory access. I have two version of the code Version 1 __global__ void load_structure(float * label){ __shared__ float shared_label[48*16]; __shared__ struct LABEL_2D* self_label; shared_label[threadIdx

问题 I am working with an array of structure, and I want for each block to load in shared memory one cell of the array. For example : block 0 will load array[0] in shared memory and block 1 will load array[1]. In order to do that I cast the array of structure in float* in order to try to coalesce memory access. I have two version of the code Version 1 __global__ void load_structure(float * label){ __shared__ float shared_label[48*16]; __shared__ struct LABEL_2D* self_label; shared_label[threadIdx

问题 I am making various ggplot s on a very large dataset (much larger than the examples). I created a binning function on both x- and y-axes to enable plotting of such large dataset. In the following example, the memory.size() is recorded at the start. Then the large dataset is simulated as dt . dt 's x2 is plotted against x1 with binning. Plotting is repeated with different subsets of dt . The size of the ploted object is checked by object.size() and stored. After the plotting objects have been

问题 In C++ where we use pass by reference we reference the address of whatever it is that we passed from the argument to the parameter of the function which is essentially a pointer right? So while they are essentially the same thing, alias and all, doesnt a pointer require memory space as well? So shouldnt whatever we have in a parameter function let us call B point to the memory location of whatever the argument was that was passed let us call A which in turn is the memory location of our value

问题 In C++ where we use pass by reference we reference the address of whatever it is that we passed from the argument to the parameter of the function which is essentially a pointer right? So while they are essentially the same thing, alias and all, doesnt a pointer require memory space as well? So shouldnt whatever we have in a parameter function let us call B point to the memory location of whatever the argument was that was passed let us call A which in turn is the memory location of our value