In CUDA, is there any runtime API that will tell whether a GPU device is being used or not? And whether the user is from video display or a GUGPU application? And what is the GPU occupancy?
On linux at least, you can use the program nvidia-smi to see the current memory use, and if any compute processes are running. Think though that the status about compute processes is only supported on a selected number of graphics cards, e.g. tesla.
While it doesn't show exactly what is using it, MSI Afterburner on Windows will show you the core usage, memory usage, fan speed, and temperature of GPU's in a system (NV or otherwise.)
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I want to optimize my local memory access pattern within my OpenCL kernel. I read at somewhere about configurable local memory. E.g. we should be able to configure which amount is used for local mem and which amount is used for automatic caching.
Also i read that the bank size can be chosen for the latest (Kepler) Nvidia hardware here:
http://www.acceleware.com/blog/maximizing-shared-memory-bandwidth-nvidia-kepler-gpus. This point seems to be very crucial for double precision value storing in local memory.
Does Nvidia provide the functionality of setting up the local memory exclusively for CUDA users? I can't find similar methods for OpenCL. So is this maybe called in a different way or does it really not exist?
Unfortunately there is no way to control the L1 cache/local memory configuration when using OpenCL. This functionality is only provided by the CUDA runtime (via cudaDeviceSetCacheConfig or cudaFuncSetCacheConfig).
I have an application which has an algorithm, accelerated with CUDA. There is also a standard CPU implementation of it. We plan to release this application for various platforms, so most of the time, there won't be a NVIDIA card to run the accelerated CUDA code. What I want is to first check whether the user's system has a CUDA enabled NVIDIA card and if it does, initializing the CUDA runtime after. If the system does not support CUDA, then I want to execute the CPU path. This question is very similar to mine, but I don't want to use any other libraries other than the plain CUDA runtime. OpenCL is an alternative, but there isn't enough time to implement an OpenCL version of the algorithm for the first release. Without any CUDA existence check, the program will surely crash since it can't find the needed .dll's for the CUDA runtime and we surely don't want that. So, I need advices on how to handle this initialization step.
Use the calls cudaGetDeviceCount and cudaGetDeviceProperties to find CUDA devices on the running system. First find out how many, then loop through all the available devices, and inspect the properties to decide which ones qualify. What I mean by "qualify" depends on your application. Do you want to require a certain compute capability? Or need a certain amount of memory? If there's more than one device, you might want to sort on some criteria, then set the device cudaSetDevice. If there are no devices, or none that are sufficient, then fall back on the CPU code path.
I'd also suggest having some mechanism to force CUDA mode off, in case some user's environment just doesn't work due to driver issues, or an old board, or something else. You can use a command-line option, or an environment variable, or whatever...
EDITING:
Regarding DLLs, you should package cudart[whatever].dll with your application. That will ensure that the program starts, and at least the CUDA query functions will operate.
Is there any power mode selection feature for nvidia gpu? for example, normal, high performance, or power save mode? If so, is it possible to select a power mode when I compile my cuda program by nvcc? And is it possible to check current power mode status of my gpu?
Actually I could not find any clue of this, though have searched a little long time from web.
Depending on your GPU you may be able to use nvidia-smi or NVML (check out the documentation for more information) to read the current power state, the GPU will dynamically change the power state to conserve power when idle and to provide performance when under load.
As a user it is not possible to set the power state of the GPU - the Tesla product line does have power-capping for the server products but that's not under user control obviously.
NVIDIA offers GPUDirect to reduce memory transfer overheads. I'm wondering if there is a similar concept for AMD/ATI? Specifically:
1) Do AMD GPUs avoid the second memory transfer when interfacing with network cards, as described here. In case the graphic is lost at some point, here is a description of the impact of GPUDirect on getting data from a GPU on one machine to be transferred across a network interface: With GPUDirect, GPU memory goes to Host memory then straight to the network interface card. Without GPUDirect, GPU memory goes to Host memory in one address space, then the CPU has to do a copy to get the memory into another Host memory address space, then it can go out to the network card.
2) Do AMD GPUs allow P2P memory transfers when two GPUs are shared on the same PCIe bus, as described here. In case the graphic is lost at some point, here is a description of the impact of GPUDirect on transferring data between GPUs on the same PCIe bus: With GPUDirect, data can move directly between GPUs on the same PCIe bus, without touching host memory. Without GPUDirect, data always has to go back to the host before it can get to another GPU, regardless of where that GPU is located.
Edit: BTW, I'm not entirely sure how much of GPUDirect is vaporware and how much of it is actually useful. I've never actually heard of a GPU programmer using it for something real. Thoughts on this are welcome too.
Although this question is pretty old, I would like to add my answer as I believe the current information here is incomplete.
As stated in the answer by #Ani, you could allocate a host memory using CL_MEM_ALLOC_HOST_PTR and you will most likely get a pinned host memory that avoids the second copy depending on the implementation. For instance, NVidia OpenCL Best Practices Guide states:
OpenCL applications do not have direct control over whether memory objects are
allocated in pinned memory or not, but they can create objects using the
CL_MEM_ALLOC_HOST_PTR flag and such objects are likely to be allocated in
pinned memory by the driver for best performance
The thing I find missing from previous answers is the fact that AMD offers DirectGMA technology. This technology enables you to transfer data between the GPU and any other peripheral on the PCI bus (including other GPUs) directly witout having to go through system memory. It is more similar to NVidia's RDMA (not available on all platforms).
In order to use this technology you must:
have a compatible AMD GPU (not all of them support DirectGMA). you can use either OpenCL, DirectX or OpenGL extentions provided by AMD.
have the peripheral driver (network card, video capture card etc) either expose a physical address to which the GPU DMA engine can read/write from/to. Or be able to program the peripheral DMA engine to transfer data to / from the GPU exposed memory.
I used this technology to transfer data directly from video capture devices to the GPU memory and from the GPU memory to a proprietary FPGA. Both cases were very efficent and did not involve any extra copying.
Interfacing OpenCL with PCIe devices
I think you may be looking for the CL_MEM_ALLOC_HOST_PTR flag in clCreateBuffer. While the OpenCL specification states that this flag "This flag specifies that the application wants the OpenCL implementation to allocate memory from host accessible memory", it is uncertain what AMD's implementation (or other implementations) might do with it.
Here's an informative thread on the topic http://www.khronos.org/message_boards/viewtopic.php?f=28&t=2440
Hope this helps.
Edit: I do know that nVidia's OpenCL SDK implements this as allocation in pinned/page-locked memory. I am fairly certain this is what AMD's OpenCL SDK does when running on the GPU.
As pointed out by #ananthonline and #harrism, many of the features of GPUDirect have no direct equivalent in OpenCL. However, if you are trying to reduce memory transfer overhead, as mentioned in the first sentence of your question, zero copy memory might help. Normally, when an application creates a buffer on the GPU, the contents of the buffer are copied from CPU memory to GPU memory en masse. With zero copy memory, there is no upfront copy; instead, data is copied over as it is accessed by the GPU kernel.
Zero copy does not make sense for all applications. Here is advice from the AMD APP OpenCL Programming Guide on when to use it:
Zero copy host resident memory objects can boost performance when host
memory is accessed by the device in a sparse manner or when a large
host memory buffer is shared between multiple devices and the copies
are too expensive. When choosing this, the cost of the transfer must
be greater than the extra cost of the slower accesses.
Table 4.3 of the Programming Guide describes which flags to pass to clCreateBuffer to take advantage of zero copy (either CL_MEM_ALLOC_HOST_PTR or CL_MEM_USE_PERSISTENT_MEM_AMD, depending on whether you want device-accessible host memory or host-accessible device memory). Note that zero copy support is dependent on both the OS and the hardware; it appears to not be supported under Linux or older versions of Windows.
AMD APP OpenCL Programming Guide: http://developer.amd.com/sdks/AMDAPPSDK/assets/AMD_Accelerated_Parallel_Processing_OpenCL_Programming_Guide.pdf
Where can I find information / changesets / suggestions for using the new enhancements in CUDA 4.0? I'm especially interested in learning about Unified Virtual Addressing?
Note: I would really like to see an example were we can access the RAM directly from the GPU.
Yes, using host memory (if that is what you mean by RAM) will most likely slow your program down, because transfers to/from the GPU take some time and are limited by RAM and PCI bus transfer rates. Try to keep everything in GPU memory. Upload once, execute kernel(s), download once. If you need anything more complicated try to use asynchronous memory transfers with streams.
As far as I know "Unified Virtual Addressing" is really more about using multiple devices, abstracting from explicit memory management. Think of it as a single virtual GPU, everything else still valid.
Using host memory automatically is already possible with device-mapped-memory. See cudaMalloc* in the reference manual found at the nvidia cuda website.
CUDA 4.0 UVA (Unified Virtual Address) does not help you in accessing the main memory from the CUDA threads. As in the previous versions of CUDA, you still have to map the main memory using CUDA API for direct access from GPU threads, but it will slow down the performance as mentioned above. Similarly, you cannot access GPU device memory from CPU thread just by dereferencing the pointer to the device memory. UVA only guarantees that the address spaces do not overlap across multiple devices (including CPU memory), and does not provide coherent accessibility.