I've created a small sample animation with SkiaSharp and it runs with 60FPS.
On a real Pixel 4 it has the following perfomance:
SKGLView (GPU Renderer):
average Frametimes 0.5ms
SKCanvasView (CPU Renderer):
average frametimes: 5ms
If the gpu renderer is so much faster:
What's the usecase/benefit for using the cpu renderer?
Not quite sure, but I suppose that back then when Skia was created, not all devices might have had a graphics chip. Think of something like embedded devices running linux but on hardware lacking a GPU.
Related
I'm on an Ubuntu 19.10 machine (with KDE desktop environment) with 8GB of RAM, an i5 8250u and an MX130 gpu (2GB VRAM), running a Jupyter Notebook with tensorflow-gpu.
I was just training some models to test their memory usage, and I can't see any sense in what I'm looking at. I used KSysGUARD and NVIDIA System Monitor (https://github.com/congard/nvidia-system-monitor) to monitor my system during training.
As I hit "train", on NVIDIA S.M. show me that memory usage is 100% (or near 100% like 95/97%) the GPU usage is fine.
Always in NVIDIA S.M., I look at the processes list and "python" occupies only around 60MB of vram space.
In KSysGUARD, python's memory usage is always around 700mb.
There might be some explanation for that, the problem is that the gpu's memory usage hits 90% with a model with literally 2 neurons (densely connected of course xD), just like a model with 200million parameters does. I'm using a batch size of 128.
I thought around that mess, and if I'm not wrong, a model with 200million parameters should occupy 200000000*4bytes*128 bytes, which should be 1024gb.
That means I'm definitely wrong on something, but I'm too selfless to keep that riddle for me, so I decided to give you the chance to solve this ;D
PS: English is not my main language.
Tensorflow by default allocates all available VRAM in the target GPU. There is an experimental feature called memory growth that let's you control that, basically stops the initialization process from allocating all VRAM and does it when there is a need for it.
https://www.tensorflow.org/api_docs/python/tf/config/experimental/set_memory_growth
I want to train googles object detection with faster_rcnn_with resnet101 using mscoco datasetcode. I used only 10,000 images for training purpose.I used graphics: GeForce 930M/PCIe/SSE2. NVIDIA Driver Version:384.90. here is the picture of my GeForce.
And I have 8Gb RAM but in tensorflow gpu it is showed 1.96 Gb.
. Now How can I extend my PGU's RAM. I want to use full system memory.
You can train on the cpu to take advantage of the RAM on your machine. However, to run something on the gpu it has to be loaded to the gpu first. Now you can swap memory in and out, because not all the results are needed at any step. However, you pay with a very long training time and I would rather advise you to reduce the batch size. Nevertheless, details about this process and implementation can be found here: https://medium.com/#Synced/how-to-train-a-very-large-and-deep-model-on-one-gpu-7b7edfe2d072.
I want to assemble a new computer mainly for CUDA applications. When it comes to CPU I have to choose between AMD and Intel.
Most of the AMD's processors don't have integrated gpu while Intel's processors do.
My question is:
If the nvidia gpu would be the only graphic processing unit in the whole PC (without integrated one),
would its efficiency for CUDA programs be worse as it has to produce some graphics on a desktop (while using for example Matlab)?
The anwer is yes, efficiency would be slightly lower due to the GPU doing display tasks, like moving the cursor around or scrolling a display in a .pdf browser.
however if you are aiming for a reasonably mid-to-high-end GPU, the loss of efficiency is marginal. If you have enough money, you will buy dedicated GPU, but if not, then just don't bother. It might be like 1% or less.
A bigger problem is that the display takes up RAM, that (a) becomes unavailable to CUDA applications and (b) the CUDA manual states that the display driver is allowed to dis-own the CUDA application from it's memory at any time without warning (!).
If you ask me if that does really happen (display driver taking over the CUDA app memory), then yes, I have experienced it, with the prime example being when you change the resolution of your display.
So definetely don't do any banking with GPUs or you might see your accounts being randomly infused with millions :-)
That's why 'proffesional' CUDA cards (the tesla variety) have no display outputs - just in case.
I'm preparing an acceptance test for a new machine with Nvidia graphics cards and I'd like a simple CUDA program that will fully exercise the GPU for a full day. The intent is to generate large amounts of heat and ensure the new machine is stable under the load. I'd like the code to be very easy to compile and run (no dependencies, no large input data sets), and also very easy to verify (small amounts of output). Also, I'd like it to be command-line only, no GUI (the test will have to be automated).
I was originally thinking of repeatedly running Vector Dot Products of large vectors. However, that's mostly memory-intensive. So if the GPUs are constantly waiting on memory accesses, then they probably aren't generating as much heat as they could.
I'm running on a CentOS Linux machine.
Does anyone have any suggestions?
You didn't mention which OS you are on.
Ideally, you would want to stress the floating point units, the logic/integer units, the GPU memory, the GPU voltage regulators (VRMs) and the main PSU. I don't think there is any single utility out there that does that.
Memory:
http://sourceforge.net/projects/cudagpumemtest/
Integer (?):
http://sourceforge.net/projects/cudalucas/
PSU and VRMs (In the past, this program could cause GPUs to run out-of-spec, breaking the card. I don't think that's the case anymore):
http://www.ozone3d.net/benchmarks/fur/
Dear CUDA users I am reposting a question from nvidia boards:
I am currently doing image processing on GPU and I have one kernel that takes something like 500 to 700 milliseconds when running on big images. It used to work perfectly on smaller images but now the problem is that the whole display and even the mouse cursor are getting laggy (OS=win7)
My idea was to split my kernel in 4 or 8 kernel launches, hoping that the driver could refresh more often (between each kernel launch).
Unfortunately it does not help at all, so what else could I try to avoid this freezing display effect? I was suggested to add a cudaStreamQuery(0) call between each kernel to avoid packing by the driver.
Note: I am prepared to trade performances for smoothness!
The GPU is not (yet) designed to context switch between kernel launches, which is why your long-running kernel is causing a laggy display. Breaking the kernel into multiple launches probably would help on platforms other than Windows Vista/Windows 7. On those platforms, the Windows Display Driver Model requires an expensive user->kernel transition ("kernel thunk") every time the CUDA driver wants to submit work to the GPU.
To amortize the cost of the kernel thunk, the CUDA driver queues up GPU commands and submits them in batches. The driver uses a heuristic to trade off the performance hit from the kernel thunk against the increased latency of not immediately submitting work. What's happening with your multiple-kernels solution is that the driver's submitting your kernel or series of kernels to the GPU all at once.
Have you tried the cudaStreamQuery(0) suggestion? The reason that might help is because it forces the CUDA driver to submit work to the GPU, even if very little work is pending.