CUDA activity from independent host processes will normally create independent CUDA contexts, one for each process. Thus, the CUDA activity launched from separate host processes will take place in separate CUDA contexts, on the same device. CUDA activity in separate contexts will be serialized. The GPU will execute the activity from one process, and when … Read more
The GTX 580 can have 16 * 48 concurrent warps (32 threads each) running at a time. That is 16 multiprocessors (SMs) * 48 resident warps per SM * 32 threads per warp = 24,576 threads. Don’t confuse concurrency and throughput. The number above is the maximum number of threads whose resources can be stored … Read more
We have finished first phase of research to increase performance of lossless data compression algorithms. Bzip2 was chosen for the prototype, our team optimized only one operation – Burrows–Wheeler transformation, and we got some results: 2x-4x speed up on good compressible files. The code works faster on all our tests. We are going to complete … Read more
MATLAB does support CUDA based GPU. You have to access it from the “Parallel Computing Toolbox”. Hope these 2 links also help: Parallel Computing Toolbox Features Key Features Parallel for-loops (parfor) for running task-parallel algorithms on multiple processors Support for CUDA-enabled NVIDIA GPUs Full use of multicore processors on the desktop via workers that run … Read more
In contrast to the other OpenGL shader types, compute shaders are not directly related to computer graphics and provide a much more direct abstraction of the underlying hardware, similar to CUDA and OpenCL. It provides customizable work group size, shared memory, intra-group synchronization and all those things known and loved from CUDA and OpenCL. The … Read more
Edit 1 See Mikael Rousson’s answer – Amazon is now the way forwards as you can “rent” computational power from them. Edit 2 I’ve created a series of guides on how to set up Amazon EC2 Instances for Deep Learning with theano. It’s a lot more convenient than running on a personal machine. Edit 3 … Read more
If you use OpenCL, you can easily use it both on Windows and Linux because having display drivers is enough to run OpenCL programs and for programming you would simply need to install the SDK. CUDA has more requirements on specific GCC versions etc. But it is not much more difficult to install on Linux … Read more
Although CUDA kernel launches are asynchronous, all GPU-related tasks placed in one stream (which is the default behavior) are executed sequentially. So, for example, kernel1<<<X,Y>>>(…); // kernel start execution, CPU continues to next statement kernel2<<<X,Y>>>(…); // kernel is placed in queue and will start after kernel1 finishes, CPU continues to next statement cudaMemcpy(…); // CPU … Read more
check what is using your GPU memory with sudo fuser -v /dev/nvidia* Your output will look something like this: USER PID ACCESS COMMAND /dev/nvidia0: root 1256 F…m Xorg username 2057 F…m compiz username 2759 F…m chrome username 2777 F…m chrome username 20450 F…m python username 20699 F…m python Then kill the PID that you no … Read more
OpenCL is created specifically for computing. When you do scientific computing using OpenGL you always have to think about how to map your computing problem to the graphics context (i.e. talk in terms of textures and geometric primitives like triangles etc.) in order to get your computation going. In OpenCL you just formulate you computation … Read more