gvdb-voxels | Sparse volume compute and rendering on NVIDIA GPUs | GPU library
kandi X-RAY | gvdb-voxels Summary
kandi X-RAY | gvdb-voxels Summary
NVIDIA GVDB Voxels is a new library and SDK for simulation, compute, and rendering of sparse volumetric data. Details on the GVDB technology can be found at [
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gvdb-voxels Key Features
gvdb-voxels Examples and Code Snippets
Community Discussions
Trending Discussions on GPU
QUESTION
In my understanding, VkPhysicalDevice represents an implementation of Vulkan, which could be represented as a GPU and its drivers. We are supposed to record commands with VkCommandBuffers and send them through queues to, potentially, multithread the work we send to the gpu. That is why I understand the fact there can be multiple queues. I understand as well that QueueFamilies groups queues depending on the features they can do (the extensions available for them e.g. presentation, as well as graphics computations, transfer, etc).
However, if a GPU is able to do Graphics work, why are there queues unable to do so? I heard that using queues with less features could be faster, but why? What is a queue concretely? Is it only tied to vulkan implementation? Or is it related to hardware specific things?
I just don't understand why queues with different features exist, and even after searching through the Vulkan doc, StackOverflow, vulkan-tutorial and vkguide, the only thing I found was "Queues in Vulkan are an “execution port” for GPUs.", which I don't really understand and on which I can't find anything on google.
Thank you in advance for your help!
...ANSWER
Answered 2022-Apr-03 at 21:56A queue is a thing that consumes and executes commands, such that each queue (theoretically) executes separately from every other queue. You can think of a queue as a mouth, with commands as food.
Queues within a queue family typically execute commands using the same underlying hardware to process them. This would be like a creature with multiple mouths but all of them connect to the same digestive tract. How much food they can eat is separate from how much food they can digest. Food eaten by one mouth may have to wait for food previously eaten by another to pass through the digestive tract.
Queues from different families may (or may not) have distinct underlying execution hardware. This would be like a creature with multiple mouths and multiple digestive tracts. If a mouth eats, that food need not wait for food from a different mouth to digest.
Of course, distinct underlying execution hardware is typically distinct for a reason. Several GPUs have specialized DMA hardware for doing copies to/from device-local memory. Such hardware will typically expose a queue family that only allows transfer operations, and those transfer operations may be restricted in their byte alignment compared to transfers done on graphics-capable queues.
Note that these are general rules. Sometimes queues within a family do execute on different hardware, and sometimes queues between families use much of the same hardware. The API and implementations don't always make this clear, so you may have to benchmark different circumstances.
QUESTION
- Does OpenCL local memory really exist on Mali/Adreno GPU or they only exist in some special mobile phones?
- If they exist, in which case should we use local memory, such as GEMM/Conv or other cl kernel?
ANSWER
Answered 2022-Mar-24 at 15:27Interesting question. OpenCL defines a number of conceptual memories including local memory, constant memory, global memory, and private memory. And physically as you know, the hardware implementation of these memories is hardware dependent. For instance, some may emulate local memory using cache or system memory instead of having physical memory.
AFAIK, ARM Mali GPU does not have local memory, whereas Qualcomm Adreno GPU does have local memory.
For instance below table shows the definition of each memory in OpenCL and their relative latency and physical locations in Adreno GPU cited from OpenCL Optimization and Best Practices for Qualcomm Adreno GPUs∗
Answer updated:
as commented by SK-logic below, Mali6xx have a local memory (shared with cache).
QUESTION
I use JavaFX with Java 8 and i set this properties before launching my app
System.setProperty("prism.forceGPU","true");
System.setProperty("prism.order","d3d,sw");
The verbose mode for prism gives me this :
ANSWER
Answered 2022-Mar-09 at 05:23For those who are trying to solve a similar issue, it might be coming from the java.exe executable not using the gpu you want as a default device, you can change that in Windows' settings.
QUESTION
I'm trying to setup a Google Kubernetes Engine cluster with GPU's in the nodes loosely following these instructions, because I'm programmatically deploying using the Python client.
For some reason I can create a cluster with a NodePool that contains GPU's
...But, the nodes in the NodePool don't have access to those GPUs.
I've already installed the NVIDIA DaemonSet with this yaml file: https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nvidia-driver-installer/cos/daemonset-preloaded.yaml
You can see that it's there in this image:
For some reason those 2 lines always seem to be in status "ContainerCreating" and "PodInitializing". They never flip green to status = "Running". How can I get the GPU's in the NodePool to become available in the node(s)?
Update:Based on comments I ran the following commands on the 2 NVIDIA pods; kubectl describe pod POD_NAME --namespace kube-system.
To do this I opened the UI KUBECTL command terminal on the node. Then I ran the following commands:
gcloud container clusters get-credentials CLUSTER-NAME --zone ZONE --project PROJECT-NAME
Then, I called kubectl describe pod nvidia-gpu-device-plugin-UID --namespace kube-system and got this output:
ANSWER
Answered 2022-Mar-03 at 08:30According the docker image that the container is trying to pull (gke-nvidia-installer:fixed), it looks like you're trying use Ubuntu daemonset instead of cos.
You should run kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nvidia-driver-installer/cos/daemonset-preloaded.yaml
This will apply the right daemonset for your cos node pool, as stated here.
In addition, please verify your node pool has the https://www.googleapis.com/auth/devstorage.read_only scope which is needed to pull the image. You can should see it in your node pool page in GCP Console, under Security -> Access scopes (The relevant service is Storage).
QUESTION
my computer has only 1 GPU.
Below is what I get the result by entering someone's code
...ANSWER
Answered 2021-Oct-12 at 08:52For the benefit of community providing solution here
This problem is because when keras run with gpu, it uses almost all
vram. So we needed to givememory_limitfor each notebook as shown below
QUESTION
I've run into an issue while attempting to use SSBOs as follows:
...ANSWER
Answered 2022-Feb-10 at 13:25GLSL structs and C++ structs have different rules on alignment. For structs, the spec states:
If the member is a structure, the base alignment of the structure is N, where N is the largest base alignment value of any of its members, and rounded up to the base alignment of a vec4. The individual members of this substructure are then assigned offsets by applying this set of rules recursively, where the base offset of the first member of the sub-structure is equal to the aligned offset of the structure. The structure may have padding at the end; the base offset of the member following the sub-structure is rounded up to the next multiple of the base alignment of the structure.
Let's analyze the struct:
QUESTION
Consider the following kernel, which reduces along the rows of a 2-D matrix
...ANSWER
Answered 2022-Jan-21 at 18:57Here is the code:
QUESTION
I'm working on Convolution Tasnet, model size I made is about 5.05 million variables.
I want to train this using custom training loops, and the problem is,
...ANSWER
Answered 2022-Jan-07 at 11:08Gradient tape triggers automatic differentiation which requires tracking gradients on all your weights and activations. Autodiff requires multiple more memory. This is normal. You'll have to manually tune your batch size until you find one that works, then tune your LR. Usually, the tune just means guess & check or grid search. (I am working on a product to do all of that for you but I'm not here to plug it).
QUESTION
I've installed Windows 10 21H2 on both my desktop (AMD 5950X system with RTX3080) and my laptop (Dell XPS 9560 with i7-7700HQ and GTX1050) following the instructions on https://docs.nvidia.com/cuda/wsl-user-guide/index.html:
- Install CUDA-capable driver in Windows
- Update WSL2 kernel in PowerShell:
wsl --update - Install CUDA toolkit in Ubuntu 20.04 in WSL2 (Note that you don't install a CUDA driver in WSL2, the instructions explicitly tell that the CUDA driver should not be installed.):
ANSWER
Answered 2021-Nov-18 at 19:20Turns out that Windows 10 Update Assistant incorrectly reported it upgraded my OS to 21H2 on my laptop.
Checking Windows version by running winver reports that my OS is still 21H1.
Of course CUDA in WSL2 will not work in Windows 10 without 21H2.
After successfully installing 21H2 I can confirm CUDA works with WSL2 even for laptops with Optimus NVIDIA cards.
QUESTION
I tried to train a model using PyTorch on my Macbook pro. It uses the new generation apple M1 CPU. However, PyTorch couldn't recognize my GPUs.
...ANSWER
Answered 2021-Nov-18 at 03:08It looks like PyTorch support for the M1 GPU is in the works, but is not yet complete.
From @soumith on GitHub:
So, here's an update. We plan to get the M1 GPU supported. @albanD, @ezyang and a few core-devs have been looking into it. I can't confirm/deny the involvement of any other folks right now.
So, what we have so far is that we had a prototype that was just about okay. We took the wrong approach (more graph-matching-ish), and the user-experience wasn't great -- some operations were really fast, some were really slow, there wasn't a smooth experience overall. One had to guess-work which of their workflows would be fast.
So, we're completely re-writing it using a new approach, which I think is a lot closer to your good ole PyTorch, but it is going to take some time. I don't think we're going to hit a public alpha in the next ~4 months.
We will open up development of this backend as soon as we can.
That post: https://github.com/pytorch/pytorch/issues/47702#issuecomment-965625139
TL;DR: a public beta is at least 4 months out.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install gvdb-voxels
Install [CMake 3.10](https://cmake.org/download/) or later.
Install [CUDA Toolkit 10.2](https://developer.nvidia.com/cuda-downloads) or later.
In CMake, configure and generate the build system using gvdb-voxels/CMakeLists.txt, then build the BUILD_ALL target in your IDE (such as Visual Studio). You can also build a specific sample or the GVDB library this way by building its target in this project. Additionally, you can collect a build of GVDB and its samples into a redistributable package by building the INSTALL target. (Wondering what the GVDB_BUILD_OPTIX_SAMPLES, GVDB_BUILD_OPENVDB, GVDB_BUILD_OPENVDB_SAMPLES, GVDB_BUILD_NANOVDB, and GVDB_BUILD_NANOVDB checkboxes in the CMake GUI do? See "To build the OptiX samples", "To build GVDB with OpenVDB", and "To build the NanoVDB sample" below.).
In CMake, configure and generate the build system using gvdb-voxels/CMakeLists.txt, then build the BUILD_ALL target in your IDE (such as Visual Studio).
In CMake, configure and generate the build system using gvdb-voxels/source/gvdb_library/CMakeLists.txt, then build the gvdb target. As above, you can create a redistributable build of the NVIDIA GVDB Voxels library by building the INSTALL target.
In CMake, configure and generate the build system using gvdb-voxels/source/gvdb_library/CMakeLists.txt, then build the gvdb target.
In CMake, configure and generate the build system using gvdb-voxels/source/[your sample name here]/CMakeLists.txt, then build your sample’s target. That’s it! In Visual Studio, you can also run a sample by right-clicking it inside Visual Studio, selecting "Set as StartUp Project", and then pressing F5 or clicking the green triangle in the toolbar. For some samples, you’ll see targets named things like gSample, gSampleApp, and gSamplePTX. In this case, you’ll want to build and run gSample; the other two targets compile the application and its PTX files, while the gSample target collects everything together. Building a sample will also automatically build GVDB, so you no longer need to build and install GVDB before building a sample.
In CMake, configure and generate the build system using gvdb-voxels/source/[your sample name here]/CMakeLists.txt, then build your sample’s target.
Install OptiX 6.5 from https://developer.nvidia.com/designworks/optix/download. On Windows, check GVDB_BUILD_OPTIX_SAMPLES in the CMake GUI, or add -DGVDB_BUILD_OPTIX_SAMPLES=ON to the CMake command line. On Linux, check GVDB_BUILD_OPTIX_SAMPLES in the CMake GUI, and also add an entry, OPTIX_ROOT_DIR, pointing to the path to the OptiX SDK (the folder containing OptiX’s lib64 directory). Or if you’re using the CMake command line, add -DGVDB_BUILD_OPTIX_SAMPLES=ON -DOPTIX_ROOT_DIR=<path to OptiX SDK>, replacing <path to OptiX SDK> with the correct path. Finally, generate and build the CMake project.
Install OptiX 6.5 from https://developer.nvidia.com/designworks/optix/download.
On Windows, check GVDB_BUILD_OPTIX_SAMPLES in the CMake GUI, or add -DGVDB_BUILD_OPTIX_SAMPLES=ON to the CMake command line.
On Linux, check GVDB_BUILD_OPTIX_SAMPLES in the CMake GUI, and also add an entry, OPTIX_ROOT_DIR, pointing to the path to the OptiX SDK (the folder containing OptiX’s lib64 directory). Or if you’re using the CMake command line, add -DGVDB_BUILD_OPTIX_SAMPLES=ON -DOPTIX_ROOT_DIR=<path to OptiX SDK>, replacing <path to OptiX SDK> with the correct path.
Finally, generate and build the CMake project.
Install OpenVDB: On Windows, one of the easiest ways to install OpenVDB is to use Microsoft’s [vcpkg](https://github.com/microsoft/vcpkg); install vcpkg, then run vcpkg install openvdb[tools]:x64-windows. Make sure vcpkg is using the same compiler you’ll use to compile GVDB!
Work around a temporary issue in vcpkg: If you plan to build GVDB in release mode, go to your vcpkg/installed/x64-windows-debug/bin folder and copy openvdb_d.dll to openvdb.dll. This works around an issue where a build system copies debug-mode openvdb.lib and openvdb.dll to openvdb_d.lib and openvdb_d.dll respectively, but doesn’t update the DLL reference in openvdb_d.lib.
Configure CMake:
If you’re using the CMake GUI, delete the cache, then click the "Configure" button, specify your generator and platform, check "Specify toolchain file for cross-compiling", and click "Next". Then specify the path to vcpkg/scripts/buildsystems/vcpkg.cmake, and click Finish. Then check GVDB_BUILD_OPENVDB (and if you’d like to build the gImportVDB sample as well, check GVDB_BUILD_OPENVDB_SAMPLES) and click "Configure" again.
If you’re using the CMake command line, you can also do this by specifying -DCMAKE_TOOLCHAIN_FILE=<path to vcpkg.cmake> -DGVDB_BUILD_OPENVDB=ON -DGVDB_BUILD_OPENVDB_SAMPLES=ON.
Alternatively, if you’re not using vcpkg, you can also specify GVDB_OPENVDB_INCLUDE_DIR, GVDB_OPENVDB_LIB_RELEASE_DIR, GVDB_OPENVDB_LIB_DEBUG_DIR, and GVDB_OPENVDB_DLL_RELEASE_DIR and copy in OpenVDB’s DLLs using any method - see gvdb_library/CMakeLists.txt for more information.
Finally, generate and build the CMake project. Now you can run GVDB with OpenVDB!
Install OpenVDB 6.1+: On Linux, we recommend building OpenVDB from source using the instructions on [OpenVDB’s Developer Quick Start](https://github.com/AcademySoftwareFoundation/openvdb/#linux), unless OpenVDB 6.1+ is available through your distro’s package manager (6.1 introduced a new CMake build system in OpenVDB that we rely upon). Note that you may have to add -DCMAKE_NO_SYSTEM_FROM_IMPORTED:BOOL=TRUE if you run into OpenVDB [Issue 144](https://github.com/AcademySoftwareFoundation/openvdb/issues/144#issuecomment-508984426).
Configure CMake:
If you’re using the CMake GUI, check GVDB_BUILD_OPENVDB and GVDB_BUILD_OPENVDB_SAMPLES and click "Configure" again.
If you’re using the CMake command line, you can also do this by specifying -DGVDB_BUILD_OPENVDB=ON -DGVDB_BUILD_OPENVDB_SAMPLES=ON.
Finally, generate and build the CMake project. Now you can run GVDB with OpenVDB!
Download NanoVDB from the OpenVDB repository at https://github.com/AcademySoftwareFoundation/openvdb/tree/feature/nanovdb/nanovdb. Since NanoVDB is a header-only library, there’s no need to build OpenVDB. In CMake, set GVDB_NANOVDB_INCLUDE_DIR to the path to NanoVDB (this folder contains a nanovdb folder which contains NanoVDB.h), set GVDB_BUILD_NANOVDB to ON, and set GVDB_BUILD_NANOVDB_SAMPLES to ON.
Download NanoVDB from the OpenVDB repository at https://github.com/AcademySoftwareFoundation/openvdb/tree/feature/nanovdb/nanovdb. Since NanoVDB is a header-only library, there’s no need to build OpenVDB.
In CMake, set GVDB_NANOVDB_INCLUDE_DIR to the path to NanoVDB (this folder contains a nanovdb folder which contains NanoVDB.h), set GVDB_BUILD_NANOVDB to ON, and set GVDB_BUILD_NANOVDB_SAMPLES to ON.
Finally, generate and build the CMake project. Now you can run the gNanoVDB sample!
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