AccelerEyes announced in December 2012 that it works with Mathworks on the GPU code and has discontinued its product Jacket for MATLAB:

[http://blog.accelereyes.com/blog/2012/12/12/exciting-updates-from-accelereyes/][1]

Unfortunately they do not sell Jacket licences anymore.

As far as I understand, the Jacket GPU Array solution based on ArrayFire was much faster than the gpuArray solution provided by MATLAB. 

I started working with gpuArray, but I see that many functions are implemented poorly. For example a simple 

    myArray(:) = 0 

is very slow. I have written some custom CUDA-Kernels, but the poorly-implemented standard MATLAB functionality adds a lot of overhead, even if working with gpuArrays consistently throughout the code. I fixed some issues by replacing MATLAB code with hand written CUDA code - but I do not want to reimplement the MATLAB standard functionality.

Another feature I am missing is sparse GPU matrices.

So my questions are:

How do is speed up the badly implemented default GPU implementations provided by MATLAB? In particular, how do I speed up sparse matrix operations in MATLAB using the GPU?


  [1]: http://blog.accelereyes.com/blog/2012/12/12/exciting-updates-from-accelereyes/

Accelerating MATLAB code using GPUs?

I want to do some timing comparisons between CPU &amp; GPU as well as some profiling and would like to know if there&#39;s a way to tell [tag:PyTorch] to not use the GPU and instead use the CPU only? I realize I could install another CPU-only [tag:PyTorch], but hoping there&#39;s an easier way.

How to tell PyTorch to not use the GPU?

Do graphic cards have instruction sets of their own?
I assume they do, but I have been wondering if they are proprietary or if there is some sort of open standard.

Is every GPU instruction preceded by a CPU instruction or is it seamless?

That is, does OpenGL or DirectX call on the driver layer via the CPU which then sends a GPU instruction down the bus or is it more elaborate?




Do graphic cards have instruction sets of their own?

In Google Collab you can choose your notebook to run on cpu or gpu environment. Now I have a laptop with NVDIA Cuda Compatible GPU 1050, and latest anaconda. How to have similiar feature to the collab one where I can simply make my python to run on GPU?

How to make Jupyter Notebook to run on GPU?

I&#39;ve recently read a lot about software (mostly scientific/math and encryption related) that moves part of their calculation onto the GPU which causes a 100-1000 (!) fold increase in speed for supported operations.

Is there a library, API or other way to run something on the GPU via C#? I&#39;m thinking of simple Pi calculation. I have a GeForce 8800 GTX if that&#39;s relevant at all (would prefer card independent solution though).

C#: Perform Operations on GPU, not CPU (Calculate Pi)

I am completely new to terms related to HPC computing, but I just saw that EC2 released its new type of instance on AWS that&#39;s powered by the new Nvidia Tesla V100, which has both kinds of &quot;cores&quot;: Cuda Cores (5,120) and Tensor Cores (640). 
What is the difference between both?


What is the difference between cuda vs tensor cores?

I am currently working on a project in python, and I would like to make use of the GPU for some calculations. 

At first glance it seems like there are many tools available; at second glance, I feel like im missing something.

Copperhead looks awesome but has not yet been released. It would appear that im limited to writing low-level CUDA or openCL kernels; no thrust, no cudpp. If id like to have something sorted, im going to have to do it myself.

That doesnt seem quite right to me. Am I indeed missing something? Or is this GPU-scripting not quite living up to the hype yet?

Edit: GPULIB seems like it might be what I need. Documentation is rudimentary, and the python bindings are mentioned only in passing, but im applying for a download link right now. Anyone has experience with that, or links to similar free-for-academic-use GPU libraries? ReEdit: ok, python bindings are infact nonexistant.

Edit2: So I guess my best bet is to write something in C/CUDA and call that from python?

Python GPU programming

I&#39;ve trained 3 models and am now running code that loads each of the 3 checkpoints in sequence and runs predictions using them. I&#39;m using the GPU.

When the first model is loaded it pre-allocates the entire GPU memory (which I want for working through the first batch of data). But it doesn&#39;t unload memory when it&#39;s finished. When the second model is loaded, using both `tf.reset_default_graph()` and `with tf.Graph().as_default()` the GPU memory still is fully consumed from the first model, and the second model is then starved of memory.

Is there a way to resolve this, other than using Python subprocesses or multiprocessing to work around the problem (the only solution I&#39;ve found on via google searches)?

Clearing Tensorflow GPU memory after model execution

assuming the texture, vertex, and shader data are already on the graphics card, you don&#39;t need to send much data to the card. there&#39;s a few bytes to identify the data, and presumably a 4x4 matrix, and some assorted other parameters.

so where is all of the overhead coming from? do the operations require a handshake of some sort with the gpu?

why is sending a single mesh containing a bunch of small models, calculated on the CPU, often faster than sending the vertex id and transformation matrices? (the second option looks like there should be less data sent, unless the models are smaller than a 4x4 matrix)

why are draw calls expensive?

I have a few basic questions about using Numpy with GPU (nvidia GTX 1080 Ti).  I&#39;m new to GPU, and would like to make sure I&#39;m properly using the GPU to accelerate Numpy/Python.  I searched on the internet for a while, but didn&#39;t find a simple tutorial that addressed my questions.  I&#39;d appreciate it if someone can give me some pointers:

1) Does Numpy/Python automatically detect the presence of GPU and utilize it to speed up matrix computation (e.g. numpy.multiply, numpy.linalg.inv, ... etc)?  Or do I have code in a specific way to exploit the GPU for fast computation?

2) Can someone recommend a good tutorial/introductory material on using Numpy/Python with GPU (nvidia&#39;s)?

Thanks a lot!

Does Numpy automatically detect and use GPU?

I am reading [an awesome OpenGL tutorial][1]. It&#39;s really great, trust me. The topic I am currently at is Z-buffer. Aside from explaining what&#39;s it all about, the author mentions that we can perform custom depth tests, such as GL_LESS, GL_ALWAYS, etc. He also explains that the actual meaning of depth values (which is top and which isn&#39;t) can also be customized. I understand so far. And then the author says something unbelievable:

&gt; The range zNear can be greater than the range zFar; if it is, then the
&gt; window-space values will be reversed, in terms of what constitutes
&gt; closest or farthest from the viewer.
&gt; 
&gt; Earlier, it was said that the window-space Z value of 0 is closest and
&gt; 1 is farthest. However, if our clip-space Z values were negated, the
&gt; depth of 1 would be closest to the view and the depth of 0 would be
&gt; farthest. Yet, if we flip the direction of the depth test (GL_LESS to
&gt; GL_GREATER, etc), we get the exact same result. So it&#39;s really just a
&gt; convention. ***Indeed, flipping the sign of Z and the depth test was once
&gt; a vital performance optimization for many games.***

If I understand correctly, performance-wise, flipping the sign of Z and the depth test is nothing but changing a `&lt;` comparison to a `&gt;` comparison. So, if I understand correctly and the author isn&#39;t lying or making things up, then changing `&lt;` to `&gt;` used to be ***a vital optimization*** for many games.

Is the author making things up, am I misunderstanding something, or is it indeed the case that once `&lt;` was slower (*vitally*, as the author says) than `&gt;`?

Thanks for clarifying this quite curious matter!

&lt;sub&gt;Disclaimer: I am fully aware that algorithm complexity is the primary source for optimizations. Furthermore, I suspect that nowadays it definitely wouldn&#39;t make any difference and I am not asking this to optimize anything. I am just extremely, painfully, maybe prohibitively curious.&lt;/sub&gt;

  [1]: https://paroj.github.io/gltut/Positioning/Tut05%20Overlap%20and%20Depth%20Buffering.html

Once upon a time, when &gt; was faster than &lt; ... Wait, what?

I have a plan to use distributed TensorFlow, and I saw TensorFlow can use GPUs for training and testing. In a cluster environment, each machine could have 0 or 1 or more GPUs, and I want to run my TensorFlow graph into GPUs on as many machines as possible.

I found that when running `tf.Session()` TensorFlow gives information about GPU in the log messages like below:

    I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 
    I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y 
    I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -&gt; (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)

My question is how do I get information about current available GPU from TensorFlow? I can get loaded GPU information from the log, but I want to do it in a more sophisticated, programmatic way.
I also could restrict GPUs intentionally using the CUDA_VISIBLE_DEVICES environment variable, so I don&#39;t want to know a way of getting GPU information from OS kernel.

In short, I want a function like `tf.get_available_gpus()` that will return `[&#39;/gpu:0&#39;, &#39;/gpu:1&#39;]` if there are two GPUs available in the machine. How can I implement this?


How to get current available GPUs in tensorflow?

Everyone has this huge massively parallelized supercomputer on their desktop in the form of a graphics card GPU.

  - What is the &quot;hello world&quot; equivalent of the GPU community?
  - What do I do, where do I go, to get started programming the GPU for the major GPU vendors?

-Adam

Intro to GPU programming

I&#39;m working on a business project that is done in Java, and it needs huge computation power to compute business markets. Simple math, but with huge amount of data.

We ordered some CUDA GPUs to try it with and since Java is not supported by CUDA, I&#39;m wondering where to start. Should I build a JNI interface? Should I use JCUDA or are there other ways?

I don’t have experience in this field and I would like if someone could direct me to something so I can start researching and learning.




Using Java with Nvidia GPUs (CUDA)

I am trying to get more processing power out of my grid.

I am using all cpus/cores, is it possible to utilize the GPU with C#.

Anyone know any libraries or got any sample code?


Utilizing the GPU with c#

This is a question about how to determine the CUDA grid, block and thread sizes. This is an additional question to the one posted [here][1].

Following this link, the answer from talonmies contains a code snippet (see below). I don&#39;t understand the comment &quot;value usually chosen by tuning and hardware constraints&quot;.

I haven&#39;t found a good explanation or clarification that explains this in the CUDA documentation. In summary, my question is how to determine the optimal `blocksize` (number of threads) given the following code:


    const int n = 128 * 1024;
    int blocksize = 512; // value usually chosen by tuning and hardware constraints
    int nblocks = n / nthreads; // value determine by block size and total work
    madd&lt;&lt;&lt;nblocks,blocksize&gt;&gt;&gt;mAdd(A,B,C,n);


  [1]: https://stackoverflow.com/a/5643838/1292251

How do I choose grid and block dimensions for CUDA kernels?

update: this question is related to Google Colab&#39;s &quot;Notebook settings: Hardware accelerator: GPU&quot;. This question was written before the &quot;TPU&quot; option was added.

Reading multiple excited announcements about Google Colaboratory providing free Tesla K80 GPU, I tried to run [fast.ai][1] lesson on it for it to never complete - quickly running out of memory. I started investigating of why.

The bottom line is that “free Tesla K80” is not &quot;free&quot; for all - for some only a small slice of it is &quot;free&quot;. 

I connect to Google Colab from West Coast Canada and I get only 0.5GB of what supposed to be a 24GB GPU RAM. Other users get access to 11GB of GPU RAM.

Clearly 0.5GB GPU RAM is insufficient for most ML/DL work.

If you&#39;re not sure what you get, here is little debug function I scraped together (only works with the GPU setting of the notebook):

    # memory footprint support libraries/code
    !ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi
    !pip install gputil
    !pip install psutil
    !pip install humanize
    import psutil
    import humanize
    import os
    import GPUtil as GPU
    GPUs = GPU.getGPUs()
    # XXX: only one GPU on Colab and isn’t guaranteed
    gpu = GPUs[0]
    def printm():
     process = psutil.Process(os.getpid())
     print(&quot;Gen RAM Free: &quot; + humanize.naturalsize( psutil.virtual_memory().available ), &quot; | Proc size: &quot; + humanize.naturalsize( process.memory_info().rss))
     print(&quot;GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB&quot;.format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))
    printm()
    
Executing it in a jupyter notebook before running any other code gives me:

    Gen RAM Free: 11.6 GB  | Proc size: 666.0 MB
    GPU RAM Free: 566MB | Used: 10873MB | Util  95% | Total 11439MB

The lucky users who get access to the full card will see:

    Gen RAM Free: 11.6 GB  | Proc size: 666.0 MB
    GPU RAM Free: 11439MB | Used: 0MB | Util  0% | Total 11439MB

Do you see any flaw in my calculation of the GPU RAM availability, borrowed from GPUtil?

Can you confirm that you get similar results if you run this code on Google Colab notebook?

If my calculations are correct, is there any way to get more of that GPU RAM on the free box?

update: I&#39;m not sure why some of us get 1/20th of what other users get. e.g. the person who helped me to debug this is from India and he gets the whole thing!

**note**: please don&#39;t send any more suggestions on how to kill the potentially stuck/runaway/parallel notebooks that might be consuming parts of the GPU. No matter how you slice it, if you are in the same boat as I and were to run the debug code you&#39;d see that you still get a total of 5% of GPU RAM (as of this update still).

  [1]: http://course.fast.ai/

Google Colaboratory: misleading information about its GPU (only 5% RAM available to some users)

Question: Is there an emulator for a Geforce card that would allow me to program and test CUDA without having the actual hardware?

___

Info:

I&#39;m looking to speed up a few simulations of mine in CUDA, but my problem is that I&#39;m not always around my desktop for doing this development. I would like to do some work on my netbook instead, but my netbook doesn&#39;t have a GPU. Now as far as I know, you need a CUDA capable GPU to run CUDA. Is there a way to get around this? It would seem like the only way is a GPU emulator (which obviously would be painfully slow, but would work). But whatever way there is to do this I would like to hear.

I&#39;m programming on Ubuntu 10.04 LTS.

GPU Emulator for CUDA programming without the hardware

I have a very simple Toshiba Laptop with i3 processor. Also, I do not have any expensive graphics card. In the display settings, I see **Intel(HD) Graphics** as display adapter. I am planning to learn some cuda programming. But, I am not sure, if I can do that on my laptop as it does not have any nvidia&#39;s cuda enabled GPU.

In fact, I doubt, if I even have a GPU o_o

So, I would appreciate if someone can tell me if I can do CUDA programming with the current configuration and if possible also let me know what does **Intel(HD) Graphics** mean? 

Can I run CUDA on Intel&#39;s integrated graphics processor?

I&#39;d like to extend my skill set into GPU computing. I am familiar with raytracing and realtime graphics(OpenGL), but the next generation of graphics and high performance computing seems to be in GPU computing or something like it.

I currently use an AMD HD 7870 graphics card on my home computer. Could I write CUDA code for this? (my intuition is no, but since Nvidia released the compiler binaries I might be wrong).

A second more general question is, Where do I start with GPU computing? I&#39;m certain this is an often asked question, but the best I saw was from 08&#39; and I figure the field has changed quite a bit since then. 

Is it possible to run CUDA on AMD GPUs?

I understand how to write OpenGL/DirectX programs, and I know the maths and the conceptual stuff behind it, but I&#39;m curious how the GPU-CPU communication works on a low level.

Say I&#39;ve got an OpenGL program written in C that displays a triangle and rotates the camera by 45 degrees. When I compile this program, will it be turned into a series of ioctl-calls, and the gpu driver then sends the appropriate commands to the gpu, where all the logic of rotating the triangle and setting the appropriate pixels in the appropriate color is wired in? Or will the program be compiled into a &quot;gpu program&quot; which is loaded onto the gpu and computes the rotation etc.? Or something completely different?

**Edit**:
A few days later I found this article series, which basically answers the question:
http://fgiesen.wordpress.com/2011/07/01/a-trip-through-the-graphics-pipeline-2011-part-1/

How does OpenGL work at the lowest level?

I know that `nvidia-smi -l 1` will give the GPU usage every one second (similarly to the following). However, I would appreciate an explanation on what `Volatile GPU-Util` really means. Is that the number of used SMs over total SMs, or the occupancy, or something else?

    +-----------------------------------------------------------------------------+
    | NVIDIA-SMI 367.48                 Driver Version: 367.48                    |
    |-------------------------------+----------------------+----------------------+
    | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
    | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
    |===============================+======================+======================|
    |   0  Tesla K20c          Off  | 0000:03:00.0     Off |                    0 |
    | 30%   41C    P0    53W / 225W |      0MiB /  4742MiB |     96%      Default |
    +-------------------------------+----------------------+----------------------+
    |   1  Tesla K20c          Off  | 0000:43:00.0     Off |                    0 |
    | 36%   49C    P0    95W / 225W |   4516MiB /  4742MiB |     63%      Default |
    +-------------------------------+----------------------+----------------------+
    
    +-----------------------------------------------------------------------------+
    | Processes:                                                       GPU Memory |
    |  GPU       PID  Type  Process name                               Usage      |
    |=============================================================================|
    |    1      5193    C   python                                        4514MiB |
    +-----------------------------------------------------------------------------+

nvidia-smi Volatile GPU-Utilization explanation?

When I run `nvidia-smi`, I get the following message:

&gt; Failed to initialize NVML: Driver/library version mismatch

An hour ago I received the same message and uninstalled my CUDA library and I was able to run `nvidia-smi`, getting the following result:

[![nvidia-smi-result][1]][1]

After this I downloaded `cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb` from [the official NVIDIA page][2] and then simply:

```lang-none
sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda
export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
```

Now I have CUDA installed, but I get the mentioned mismatch error.
_______________________________________________________

Some potentially useful information:

Running `cat /proc/driver/nvidia/version` I get:

```lang-none
NVRM version: NVIDIA UNIX x86_64 Kernel Module  378.13  Tue Feb  7 20:10:06 PST 2017
GCC version:  gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.4)
```

I&#39;m running Ubuntu 16.04.2 LTS (Xenial Xerus).

The kernel release is 4.4.0-66-generic.

  [1]: https://i.stack.imgur.com/RyAeQ.png
  [2]: https://developer.nvidia.com/cuda-downloads






Nvidia NVML Driver/library version mismatch

I have installed tensorflow in my ubuntu 16.04 using the second answer [here][1] with ubuntu&#39;s builtin apt cuda installation.

Now my question is how can I test if tensorflow is really using gpu? I have a gtx 960m gpu. When I `import tensorflow` this is the output

    I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
    I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
    I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
    I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
    I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally

Is this output enough to check if tensorflow is using gpu ? 
  [1]: https://devtalk.nvidia.com/default/topic/936429/-solved-tensorflow-with-gpu-in-anaconda-env-ubuntu-16-04-cuda-7-5-cudnn-/

How to tell if tensorflow is using gpu acceleration from inside python shell?

when is calling to the `cudaDeviceSynchronize` function  really needed?. 

As far as I understand from the CUDA documentation, CUDA kernels are asynchronous, so it seems that we should call `cudaDeviceSynchronize` after each kernel launch.   However, I have tried the same code (training neural networks) with and without any `cudaDeviceSynchronize`, except one before the time measurement. I have found that I get the same result but with a  speed up between 7-12x (depending on the matrix sizes).  

So, the question is if there are any reasons to use `cudaDeviceSynchronize` apart of time measurement. 

For example:

- Is it needed before copying data from the GPU back to the host with `cudaMemcpy`?

- If I do matrix multiplications like


        C = A * B
        D = C * F

should I put `cudaDeviceSynchronize` between both? 

From my experiment It seems that I don&#39;t.

Why does `cudaDeviceSynchronize` slow the program so much?

When to call cudaDeviceSynchronize?

I am a newbie to GPU programming. I have a laptop with NVIDIA GeForce GT 640 card. I am faced with 2 dilemmas, suggestions are most welcome.

 1. **If I go for CUDA -- Ubuntu or Windows** Clearly CUDA is more suitable to windows while it can be a severe issue to install on Ubuntu. I have seen some blogposts which claim to have installed CUDA 5 on [Ubuntu 11.10][1] and [Ubuntu 12.04][2]. However, I have not been able to get them to work. Also, [standard CUDA textbooks][3] prefer to work in the windows domain and are more or less silent in concern with Unix/Ubuntu installation and working.

 2. **[CUDA or OpenCL][4]** -- Now this is probably more trickier than my first question ! I have mostly come across GPGPU projects using CUDA/Nvidia but OpenCL is probably is the next best option in open source and installing in Ubuntu probably will not be an issue, though some suggestions here will be most useful. Am I sacrificing any functionality if I go for OpenCL and NOT CUDA ? 

Any help or suggestions ? 

  [1]: http://www.r-tutor.com/gpu-computing/cuda-installation/cuda5.0-ubuntu
  [2]: http://sn0v.wordpress.com/2012/12/07/installing-cuda-5-on-ubuntu-12-04/
  [3]: http://www.amazon.com/CUDA-Example-Introduction-General-Purpose-Programming/dp/0131387685
  [4]: http://wiki.tiker.net/CudaVsOpenCL
  [5]: http://devblogs.nvidia.com/parallelforall/cudacasts-episode-5-install-cuda-55-linux-package-manager/
  [6]: https://gist.github.com/rmcgibbo/6314452
  [7]: http://mhr3.blogspot.in/2013/06/opencl-on-ubuntu-1304.html
  [8]: http://heather.cs.ucdavis.edu/parprocbook

GPU Programming, CUDA or OpenCL?

How are GPUs more faster then CPUs? I&#39;ve read articles that talk about how GPU&#39;s are much faster in breaking passwords than CPUs. If thats the case then why can&#39;t CPUs be designed in the same way as GPUs to be even in speed?

Why are GPUs more powerful than CPUs

Trying to plot the output of some `Data.Array.Accelerate` computations with `gnuplot` I encountered a strange problem. When run via the interpreter everything is fine, as is plotting straight Haskell data or just printing the Accelerate values, however trying to plot the Accelerate data fails. The error given is `forkOS_entry: interrupted`.

I&#39;ve since realised I should just be more UNIXy and do one thing (well) in each program. But I&#39;m interested in finding out why this fails. I include a minimum code sample below.

    import Prelude hiding (zip,zipWith)
    
    import Graphics.Gnuplot.Simple
    import Data.Array.Accelerate
    -- import Data.Array.Accelerate.Interpreter
    import Data.Array.Accelerate.CUDA
    
    f :: Vector Float -&gt; Vector Float -&gt; Acc (Vector Float)
    f xs ys = let xs&#39; = use xs
                  ys&#39; = use ys
              in
                 (zipWith (*) xs&#39; ys&#39;)
    n=10::Int
    
    points = toList.run $ f (fromList (Z:.n) [1..10]) (fromList (Z:.n) [-5..4])
    
    main = plotList [] points

**update 2014/09/11**

Based on `user2141650`&#39;s suggestion (thanks!) changing the last line to

    plotList [] $! points

fixes the problem. In fact it makes the plot actually appear, whereas without this the program would finish with or without error but would never actually display the plot. I suppose I&#39;d accept this as an answer if it was written up, but it would still be nice to know what&#39;s going on.

Possibly related:

 - https://stackoverflow.com/questions/17554008/forkos-entry-interrupted-error-what-is-this

(By the way, please stop trying to edit for grammar. There isn&#39;t anything wrong with the question as is, I am a native speaker and write exactly what I mean. Thank you for your contribution though.)

Haskell Array.Accelerate - forkOS error

Is there any way or tool to apply GPU acceleration on compiling programs with GCC compiler? Right now I have created a program to compile the given list of programs iteratively. It takes a few minutes. I know of a few programs like Pyrit which helps to apply GPU acceleration for precomputing hashes.

If there are no such tools available, Please advice on whether to use OpenCL or anything else to reprogram my code.

Is it possible to use GPU acceleration on compiling multiple programs on a gcc compiler?

How do I check if `pytorch` is using the GPU? It&#39;s possible to detect with `nvidia-smi` if there is any activity from the GPU during the process, but I want something written in a `python` script.

How to check if pytorch is using the GPU?

I am applying transfer-learning on a pre-trained network using the GPU version of keras. I don&#39;t understand how to define the parameters **`max_queue_size`**, **`workers`**, and **`use_multiprocessing`**. If I change these parameters (primarily to speed-up learning), I am unsure whether all data is still seen per epoch.

**`max_queue_size`**:
    
 + maximum size of the internal training queue which is used to &quot;precache&quot; samples from the generator 

 + *Question:* Does this refer to how many batches are prepared on CPU? How is it related to `workers`? How to define it optimally?


**`workers`**: 

 + number of threads generating batches in parallel. Batches are computed in parallel on the CPU and passed on the fly onto the GPU for neural network computations 

 + *Question:* How do I find out how many batches my CPU can/should generate in parallel?


**`use_multiprocessing`**: 

 + whether to use process-based threading

 + *Question:* Do I have to set this parameter to true if I change `workers`? Does it relate to CPU usage?

**Related questions** can be found here:

 + [Detailed explanation of model.fit_generator() parameters: queue size, workers and use_multiprocessing][1]
 + [https://stackoverflow.com/questions/51790943/what-does-worker-mean-in-fit-generator-in-keras][2]

 + [What is the parameter “max_q_size” used for in “model.fit_generator”?](https://stackoverflow.com/questions/36986815/what-is-the-parameter-max-q-size-used-for-in-model-fit-generator/36989864#36989864)

 + [A detailed example of how to use data generators with Keras](https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly).

I am using `fit_generator()` as follows:
```
    history = model.fit_generator(generator=trainGenerator,
                                  steps_per_epoch=trainGenerator.samples//nBatches,     # total number of steps (batches of samples)
                                  epochs=nEpochs,                   # number of epochs to train the model
                                  verbose=2,                        # verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch
                                  callbacks=callback,               # keras.callbacks.Callback instances to apply during training
                                  validation_data=valGenerator,     # generator or tuple on which to evaluate the loss and any model metrics at the end of each epoch
                                  validation_steps=
                                  valGenerator.samples//nBatches,   # number of steps (batches of samples) to yield from validation_data generator before stopping at the end of every epoch
                                  class_weight=classWeights,                # optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function
                                  max_queue_size=10,                # maximum size for the generator queue
                                  workers=1,                        # maximum number of processes to spin up when using process-based threading
                                  use_multiprocessing=False,        # whether to use process-based threading
                                  shuffle=True,                     # whether to shuffle the order of the batches at the beginning of each epoch
                                  initial_epoch=0)   
```


The specs of my machine are:

    CPU : 2xXeon E5-2260 2.6 GHz
    Cores: 10
    Graphic card: Titan X, Maxwell, GM200
    RAM: 128 GB
    HDD: 4TB
    SSD: 512 GB


  [1]: https://github.com/keras-team/keras/issues/8540
  [2]: https://stackoverflow.com/questions/51790943/what-does-worker-mean-in-fit-generator-in-keras

How to define max_queue_size, workers and use_multiprocessing in keras fit_generator()?

How do I use **TensorFlow GPU** version instead of **CPU** version in Python 3.6 x64?

    import tensorflow as tf

Python is using my **CPU** for calculations. &lt;br/&gt;
I can notice it because I have an error:

&gt; Your CPU supports instructions that this TensorFlow binary was not
&gt; compiled to use: AVX2

I have installed tensorflow and tensorflow-gpu.



How do I switch to GPU version? 


  

How do I use TensorFlow GPU?

I have access to Tesla K20c, I am running ResNet50 on CIFAR10 dataset... 
Then I get the error as:

```lang-none
THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1524584710464/work/aten/src/THC/generated/../generic/THCTensorMathPointwise.cu line=265 error=59 : device-side assert triggered
Traceback (most recent call last):
  File &quot;main.py&quot;, line 109, in &lt;module&gt;
    train(loader_train, model, criterion, optimizer)
  File &quot;main.py&quot;, line 54, in train
    optimizer.step()
  File &quot;/usr/local/anaconda35/lib/python3.6/site-packages/torch/optim/sgd.py&quot;, line 93, in step
    d_p.add_(weight_decay, p.data)
RuntimeError: cuda runtime error (59) : device-side assert triggered at /opt/conda/conda-bld/pytorch_1524584710464/work/aten/src/THC/generated/../generic/THCTensorMathPointwise.cu:265
```

How to resolve this error?

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