checkCudaErrors | CUDA Error Checking Function : Do you want to check | GPU library

I have a function that I wrote for 1 gpu, and it runs for 10 seconds with one set of args, and I have a very long list of args to go through. I would like to use both my AMD gpus, so I have some wrapper code that launches 2 threads, and runs my function on thread 0 with an argument gpu_idx 0 and on thread 1 with an argument gpu_idx 1.

I have a cuda version for another machine, and I just run checkCudaErrors(cudaSetDevice((unsigned int)device_id)); to get my desired behavior.

With openCL I have tried to do the following:

I’m trying to write a kernel whose threads iteratively process items in a work queue. My understanding is that I should be able to do this by using atomic operations to manipulate the work queue (i.e., grab work items from the queue and insert new work items into the queue), and using grid synchronization via cooperative groups to ensure all threads are at the same iteration (I ensure the number of thread blocks doesn’t exceed the device capacity for the kernel). However, sometimes I observe that work items are skipped or processed multiple times during an iteration.

The following code is a working example to show this. In this example, an array with the size of input_len is created, which holds work items 0 to input_len - 1. The processWorkItems kernel processes these items for max_iter iterations. Each work item can put itself and its previous and next work items in the work queue, but marked array is used to ensure that during an iteration, each work item is added to the work queue at most once. What should happen in the end is that the sum of values in histogram be equal to input_len * max_iter, and no value in histogram be greater than 1. But I observe that occasionally both of these criteria are violated in the output, which implies that I’m not getting atomic operations and/or proper synchronization. I would appreciate it if someone could point out the flaws in my reasoning and/or implementation. My OS is Ubuntu 18.04, CUDA version is 10.1, and I’ve run experiments on P100, V100, and RTX 2080 Ti GPUs, and observed similar behavior.

The command I use for compiling for RTX 2080 Ti:

nvcc -O3 -o atomicsync atomicsync.cu --gpu-architecture=compute_75 -rdc=true

Some inputs and outputs of runs on RTX 2080 Ti:

Would like to pass the content from jpeg file (3 byte RGB) as a texture to a CUDA kernel but getting compilation error

a pointer to a bound function may only be used to call the function

on value.x = tex2D(_texture, u, v) * 1.0f / 255.0f; and the rest of the tex2D() calls.

What may be the reason(s) for the error?

Host side code where the texture is created:

Arrays are stored as xyzxyz..., I want to get the maximum and minimum for some direction(x or y or z), and here is the test program:

I need read a image and store it into a unsigned char array and use the array to construct a class. The class construction is device function. so I need read the image and copy to device. The code is similar to below.

I'm new to cuda and was working on a small project to learn how to use it when I came across the following error when executing my program:

I am new to CUDA development and wanted to write a simple benchmark to test some image processing feasibility. I have 32 images that are each 720x540, one byte per pixel greyscale.

I am running benchmarks for 10 seconds, and counting how many times they are able to process. There are three benchmarks I am running:

• The first is just transferring the images into the GPU global memory, via cudaMemcpy
• The second is transferring and processing the images.
• The third is running the equivalent test on a CPU.

For a starting, simple test, the image processing is just counting the number of pixels above a certain greyscale value. I'm finding that accessing global memory on the GPU is very slow. I have my benchmark structured such that it creates one block per image, and one thread per row in each image. Each thread counts its pixels into a shared memory array, after which the first thread sums them up (See below).

The issue I am having is that this all runs very slowly - about 50fps. Much slower than a CPU version - about 230fps. If I comment out the pixel value comparison, resulting in just a count of all pixels, I get 6x the performance. I tried using texture memory but didn't see a performance gain. I am running a Quadro K2000. Also: the image copy only benchmark is able to copy at around 330fps, so that doesn't appear to be the issue.

Any help / pointers would be appreciated. Thank you.

I have installed latest cudatoolkit and driver today however when I try to build and run matrixMul program using visual studio 2019, I get the following error:

[Matrix Multiply Using CUDA] - Starting… CUDA error at C:\ProgramData\NVIDIA Corporation\CUDA Samples\v10.2\common\inc\helper_cuda.h:775 code=35(cudaErrorInsufficientDriver) “cudaGetDeviceCou nt(&device_count)” C:\ProgramData\NVIDIA Corporation\CUDA Samples\v10.2\0_Simple\matrixMul…/…/bin/win64/Debug/matrixMul.exe (process 7140) exited with code 1.

More information about the setup: 1: per Nvidia control panel driver version is 391.35

2: GPU GeForce GT 420M which is cuda 2.1 as per https://developer.nvidia.com/cuda-gpus#compute

3: Visual Studio 2019

4: The program i am trying to build/run is C:\ProgramData\NVIDIA Corporation\CUDA Samples\v10.2\0_Simple\matrixMul\matrixMul_vs2019.sln

5: with a bit of debugging it appears that program is failing at line checkCudaErrors(cudaGetDeviceCount(&device_count)); inside cuda_runtime_api.h @ line 1288. The function is supposed to returns the number of devices with compute capability

greater or equal to 2.0 Apparently it sounds like GeForce GT420M is Cuda 2.1 capable but current runtime is not recognizing it and failing. Could someone please help me to resolve this error?

I am not familiar with the problems about memory alignment and pointer conversion. I am learning from the Nvidia official sample code as the following.