gpgpu

问题 I study a lot of articles and the manual of OpenACC but still i don't understand the main difference of these two constructs. 回答1: kernels directive is the more general case and probably one that you might think of, if you've written GPU (e.g. CUDA) kernels before. kernels simply directs the compiler to work on a piece of code, and produce an arbitrary number of "kernels", of arbitrary "dimensions", to be executed in sequence, to parallelize/offload a particular section of code to the

问题 I study a lot of articles and the manual of OpenACC but still i don't understand the main difference of these two constructs. 回答1: kernels directive is the more general case and probably one that you might think of, if you've written GPU (e.g. CUDA) kernels before. kernels simply directs the compiler to work on a piece of code, and produce an arbitrary number of "kernels", of arbitrary "dimensions", to be executed in sequence, to parallelize/offload a particular section of code to the

问题 AccelerEyes announced in December 2012 that it works with Mathworks on the GPU code and has discontinued its product Jacket for MATLAB: http://blog.accelereyes.com/blog/2012/12/12/exciting-updates-from-accelereyes/ Unfortunately they do not sell Jacket licences anymore. As far as I understand, the Jacket GPU Array solution based on ArrayFire was much faster than the gpuArray solution provided by MATLAB. I started working with gpuArray, but I see that many functions are implemented poorly. For

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 3 years ago . Have there been any studies comparing OpenCL to OpenMP performance? Specifically I am interested in the overhead cost of launching threads with OpenCL, e.g., if one were to decompose the domain into a very large number of individual work items (each run by a thread doing a small

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 3 years ago . Have there been any studies comparing OpenCL to OpenMP performance? Specifically I am interested in the overhead cost of launching threads with OpenCL, e.g., if one were to decompose the domain into a very large number of individual work items (each run by a thread doing a small

问题 Is there any way by which I can know the number of free/active SMs? Or atleast to read the voltage/power or temperature values of each SM by which I can know whether its working or not? (in real time while some job is getting executed on the gpu device). %smid helped me in knowing the Id of each SM. Something similar would be helpful. Thanks and Regards, Rakesh 回答1: The CUDA Profiling Tools Interface (CUPTI) contains an Events API that enables run time sampling of GPU PM counters. The CUPTI

问题 I have allocated memory on device using cudaMalloc and have passed it to a kernel function. Is it possible to access that memory from host before the kernel finishes its execution? 回答1: The only way I can think of to get a memcpy to kick off while the kernel is still executing is by submitting an asynchronous memcpy in a different stream than the kernel. (If you use the default APIs for either kernel launch or asynchronous memcpy, the NULL stream will force the two operations to be serialized

问题 I was just reading: Efficiently dividing unsigned value by a power of two, rounding up and I was wondering what was the fastest way to do this in CUDA. Of course by "fast" I mean in terms of throughput (that question also addressed the case of subsequent calls depending on each other). For the lg() function mentioned in that question (base-2 logarithm of divisor), suppose we have: template <typename T> __device__ int find_first_set(T x); template <> __device__ int find_first_set<uint32_t>

问题 I am trying to run the vector step addition function on CUDA C++ code, but for large float arrays of size 5,000,000 too, it runs slower than my CPU version. Below is the relevant CUDA and cpu code that I am talking about: #define THREADS_PER_BLOCK 1024 typedef float real; __global__ void vectorStepAddKernel2(real*x, real*y, real*z, real alpha, real beta, int size, int xstep, int ystep, int zstep) { int i = blockDim.x * blockIdx.x + threadIdx.x; if (i < size) { x[i*xstep] = alpha* y[i*ystep] +

问题 I am trying to run the vector step addition function on CUDA C++ code, but for large float arrays of size 5,000,000 too, it runs slower than my CPU version. Below is the relevant CUDA and cpu code that I am talking about: #define THREADS_PER_BLOCK 1024 typedef float real; __global__ void vectorStepAddKernel2(real*x, real*y, real*z, real alpha, real beta, int size, int xstep, int ystep, int zstep) { int i = blockDim.x * blockIdx.x + threadIdx.x; if (i < size) { x[i*xstep] = alpha* y[i*ystep] +