![]() The additional complexity should strongly motivate us to seek alternatives. Here is one of many examples of questions/answers discussing "flattening".Īs we extend this to 3 (or higher!) dimensions, the general case becomes overly complex to handle, IMO. If you think you must use the general 2D method, then go ahead, it's not impossible (although sometimes people struggle with the process!) However, due to the added complexity and reduced efficiency, the canonical "advice" here is to "flatten" your storage method, and use "simulated" 2D access. (note that allocating an array of objects, where the object(s) has an embedded pointer to a dynamic allocation, is essentially the same as the 2D array concept, and the example you linked in your question is a reasonable demonstration for that)Īlso, here is a thrust method for building a general dynamically allocated 2D array. the access will generally be less efficient than 1D access, because data access requires dereferencing 2 pointers, instead of 1.there is additional, non-trivial complexity.The answer given by talonmies there includes the proper mechanics, as well as appropriate caveats: data), then the cuda tag info page contains the "canonical" question for this, it is here. #3d coat cuda how to#If you wish to learn how to use a dynamically allocated 2D array in a CUDA kernel (meaning you can use doubly-subscripted access, e.g. That sort of host data allocation construction is particularly ill-suited to working with the data on the device. Also, you cannot use cudaMemcpy2D to transfer data when the underlying allocation has been created using a set of malloc (or new, or similar) operations in a loop. Instead the correct way to think about these is that they work with pitched allocations. Another example covering various concepts associated with cudaMallocPitch/ cudaMemcpy2d usage is here. For additional example usage, here is one of many questions on this. They could not be doubly-subscripted, or doubly dereferenced. The src and dst parameters are single-pointer parameters. This is easy to confirm simply by looking at the documentation, and noting the types of parameters in the function prototypes. Since your question compiles a list of other questions, I'll answer by compiling a list of other answers.įirst, the cuda runtime API functions like cudaMallocPitch and cudaMemcpy2D do not actually involve either double-pointer allocations or 2D (doubly-subscripted) arrays. I know I've asked a lot but in summary should I get used to squashed arrays as a fact of life or can I use the 2d allocate and copy functions without getting bad overhead like in the solution where alloc and cpy are called in a for loop? The classes I want to use CUDA with all have 2/3d arrays and wouldn't there be a lot of overhead in converting those to 1d arrays for CUDA? #3d coat cuda code#Should I just get used to this as a fact of life? I'm very persnickety about my code and it feels inelegant to me.Īnother solution I was considering was to max a matrix class that uses a 1d pointer array but I can't find a way to implement the double bracket operator.Īlso according to this link: Copy an object to device?Īnd the sub link answer: cudaMemcpy segmentation fault The other 'correct' answer for 2d arrays is to squash them into one array. Are these functions somehow less efficient? Why wouldn't this be the default answer? Many people claim the proper way to allocate an array of pointers is with a call to malloc and memcpy for each row yet the functions mallocPitch and memcpy2D exist. There are a lot of other sources mostly saying the same thing but in multiple instances I see warnings about pointer structures on the GPU. Submitted solution: Does not show allocation Problem: Allocating and transferring 2d arraysįourth link: How to use 2D Arrays in CUDA? Third link: Allocate 2D Array on Device Memory in CUDA Sub link solution: Coding pointer based structures on the GPU is a bad experience and highly inefficient, squash it into a 1d array. Problem: Allocating space on host and passing it to device "More correct" solution: Squash it into a 1d array "professional opinion," one comment saying no one with an eye on performance uses 2d pointer structures on the gpu "Correct" inefficient solution: Use malloc and memcpy in a for loop for each row (Absurd overhead) Problem: Allocating a 2d array of pointers I'm getting a lot of conflicting answers so I'm attempting to compile past questions to see if I can ask the right ones. There are a lot of questions online about allocating, copying, indexing, etc 2d and 3d arrays on CUDA. ![]()
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