What do you think the future of GPU as a CPU initiatives like CUDA are? Do you think they are going to become mainstream and be the next adopted fad in the industry? Apple is building a new framework for using the GPU to do CPU tasks and there has been alot of success in the Nvidias CUDA project in the sciences. Would you suggest that a ...
I'm currently implementing a raytracer. Since raytracing is extremely computation heavy and since I am going to be looking into CUDA programming anyway, I was wondering if anyone has any experience with combining the two. I can't really tell if the computational models match and I would like to know what to expect. I get the impression t...
I have recently begun working on a project to establish how best to leverage the processing power available in modern graphics cards for general programming. It seems that the field general purpose GPU programming (GPGPU) has a large bias towards scientific applications with a lot of heavy math as this fits well with the GPU computationa...
I am interested to know whether anyone has written an application that takes advantage of a GPGPU by using, for example, nVidia CUDA. If so, what issues did you find and what performance gains did you achieve compared with a standard CPU? ...
So, im trying to write some code that utilizes Nvidia's CUDA architecture. I noticed that copying to and from the device was really hurting my overall performance, so now I am trying to move a large amount of data onto the device. As this data is used in numerous functions, I would like it to be global. Yes, I can pass pointers around, ...
I'm exploring various options for mapping common C# code constructs to C++ CUDA code for running on a GPU. The structure of the system is as follows (arrows represent method calls): C# program -> C# GPU lib -> C++ CUDA implementation lib A method in the GPU library could look something like this: public static void Map<T>(this ICollec...
nVidia released their CUDA API allowing developers to utilize their graphics cards, taking advantage of the massively parallel architecture and vectorized operations. Libraries such as pyCUDA were created to allow developers of scripting languages to send selected code to the GPU. And there has been a growing effort to design multi-ling...
I have a neural network written in Erlang, and I just bought a GeForce GTX 260 card with a 240 core GPU on it. Is it trivial to use CUDA as glue to run this on the graphics card? ...
This article describes a way, in C#, to allow the addition of arbitrary value types which have a + operator defined for them. In essence it allows the following code: public T Add(T val1, T val2) { return val1 + val2; } This code does not compile as there is no guarantee that the T type has a definition for the '+' operator, but th...
With Windows 7 probably going to RTM next October (and DirectX 11 with it), would it be worth waiting for DirectX 11's explicit GPGPU features, meaning it will be cross-platform (ATI/Nvidia, not Windows/Linux/Mac/Whatever); or should I create a CUDA application now? ...
I know that there are a lot of CUDA language bindings, such as PyCUDA, but are there any good bindings for .Net? The only one I've seen is this one, but I'd like to know if there are any others. ...
When writing CUDA applications, you can either work at the driver level or at the runtime level as illustrated on this image (The libraries are CUFFT and CUBLAS for advanced math): I assume the tradeoff between the two are increased performance for the low-evel API but at the cost of increased complexity of code. What are the concrete...
I'm working on a C# library which offloads certain work tasks to the GPU using NVIDIA's CUDA. An example of this is adding two arrays together using extension methods: float[] a = new float[]{ ... } float[] b = new float[]{ ... } float[] c = a.Add(b); The work in this code is done on the GPU. However, I would like it to be done asynch...
Is there an efficient way to take a subset of a C# array and pass it to another peice of code (without modifying the original array)? I use CUDA.net which has a function which copies an array to the GPU. I would like to e.g. pass the function a 10th of the array and thus copy each 10th of the array to the GPU seperately (for pipelining p...
Hello, I'm trying to take advantage of the constant memory, but I'm having a hard time figuring out how to nest arrays. What I have is an array of data that has counts for internal data but those are different for each entry. So based around the following simplified code I have two problems. First I don't know how to allocate the dat...
I have a CUDA kernel which I'm compiling to a cubin file without any special flags: nvcc text.cu -cubin It compiles, though with this message: Advisory: Cannot tell what pointer points to, assuming global memory space and a reference to a line in some temporary cpp file. I can get this to work by commenting out some seemingly ar...
Hello, I'm starting to use CUDA at the moment and have to admit that I'm a bit disappointed with the C API. I understand the reasons for choosing C but had the language been based on C++ instead, several aspects would have been a lot simpler, e.g. device memory allocation (via cudaMalloc). My plan was to do this myself, using overloade...
I am developing a product with heavy 3D graphics computations, to a large extent closest point and range searches. Some hardware optimization would be useful. While I know little about this, my boss (who has no software experience) advocates FPGA (because it can be tailored), while our junior developer advocates GPGPU with CUDA, because ...
Does anyone know a project which implements standard compression methods (like Zip, GZip, BZip2, LZMA,...) using NVIDIA's CUDA library? I was wondering if algorithms which can make use of a lot of parallel tasks (like compression) wouldn't run much faster on a graphics card than with a dual or quadcore CPU. What do you think about the ...
I'm debating about whether to learn GP-GPU stuff, such as CUDA, or whether to put it off. My problem domain (bioinformatics) is such that it might be nice to know, since a lot of our problems do have massive parallelism, but most people in the field certainly don't know it. My question is, how difficult the API for CUDA and other GP-GP...