![]() The oneAPI Technical Advisory Boards have been iteratively refining the oneAPI specification in line with industry standards. It also includes specific libraries and a hardware abstraction layer. The oneAPI specification includes Data Parallel C++ (DPC++), oneAPI’s implementation of the Khronos SYCL standard. The oneAPI specification addresses these challenges. It must boost developer productivity while providing consistent performance across architectures. We need a high-level, open standard, heterogeneous programming language that’s both built on evolutions of standards and extensible. To achieve high performance and efficiency, we need a unified and simplified programming model enabling us to select the optimal hardware for the task at hand. We must ensure we don’t leave any transistors, resistors, or semiconductors behind. CUDA VECTOR ADD DIM3 CODEAs a result, developing applications across architectures is challenging.īut if we care about performance and efficiency, we need to regularly reuse our code on new hardware as it becomes available. This situation typically exposes us to various programming languages and vendor-specific libraries. Each device may require specific optimization procedures for top performance. The code is really easy and i have absolutely no idea what there could potentially be cat main.As developers, we’ve continuously worked on dedicated architectures to accelerate our applications. Now, this is really really strange to me. I seem to have only 4 multiprocessors and 32 cores, while your card reports 16 multiprocessors and 128 cores. Total sum of histogram elements: 100000000 allocating GPU memory and copying input data $ ~/NVIDIA_CUDA_SDK/bin/linux/release/histogram64 -help Maximum sizes of each dimension of a block:ĕ12 x 512 x 64 Total amount of global memory: 536150016 bytes On the first test, and passes the 2nd one: $ ~/NVIDIA_CUDA_SDK/bin/linux/release/deviceQuery There is 1 device supporting CUDA The odd thing here is that we seem to have the same card (maybe nt the same vendor but same chip) yet mine gives out a totally different output So other than the two issues I listed in the brackets, I had no problems with it. I got the expected result of having only the first element in the output set to 4 (you’ve been noted on this in a previous reply - block dimensions…). CUDA VECTOR ADD DIM3 FREEI ran your code (added free and cudaFree calls at the end of it External Image, and also I zeroed the C_d array using cudaMemSet). my card should be able to handle cuda but for some reason the histogram test does not work. One test failed and the second one succeeds. Total sum of histogram elements: -1464298936 …allocating GPU memory and copying input data Maximum sizes of each dimension of a grid: 65535 x 65535 x 1 Maximum sizes of each dimension of a block: 512 x 512 x 64 Total number of registers available per block: 8192 Total amount of shared memory per block: 16384 bytes Total amount of constant memory: 65536 bytes Total amount of global memory: 267714560 bytes I experimented with the numbers but it makes no real difference. You are also printing 8 elements but the kernel only writes to 4 of them. However, that would run the 4 threads with threadIdx.x=0 so I’m not sure why the 0’th element is not correct. You are launching 1 block that is 1x4, but indexing by threadIdx.x in your kernel. Performance level 0: gpu 540MHz/shader 1188MHz/memory 700MHz/0.00V/100%Ĭan anybody tell me what i am doing wrong?Īfter a quick read-through of your code, the only thing amiss I see is that your grid/thread configuration is incorrect. PATH=/usr/local/cuda/bin:/usr/local/cuda/bin/:/home/hs/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games No core dumps, no compiler warnings - just crapty make Then i copy the answer back into the CPU. I am compiling two vectors, copy them to the GPU and add them up. Global void matAdd(float *A, float *B, float *C)Ī_h = (float *) (malloc(sizeof(float) * VAR)) ī_h = (float *) (malloc(sizeof(float) * VAR)) Ĭ_h = (float *) (malloc(sizeof(float) * VAR)) ĬudaMalloc( (void **) &A_d, sizeof(float) * VAR) ĬudaMalloc( (void **) &B_d, sizeof(float) * VAR) ĬudaMalloc( (void **) &C_d, sizeof(float) * VAR) ĬudaMemcpy(C_h, C_d, sizeof(float) * VAR, cudaMemcpyDeviceToHost) The code is really easy and i have absolutely no idea what there could potentially be cat main.cu I am totally new to CUDA and i wanted to do some simple vector addition but somehow i always get zero answers. ![]()
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