Introduction to PyTorch OpenCL
PyTorch OpenCL is the low-level API and stands for Open Computing Language, which is used for heterogeneous computing. OpenCL is widely used and runs on GPUs that are CUDA powered. With the help of OpenCL API, we can develop and launch kernels of computers written using the programming language of C on GPU.
In this article, we will dive into the topic of PyTorch OpenCL. We will try to understand what PyTorch OpenCL is, how to use PyTorch OpenCL, porting code to OpenCL, PyTorch OpenCL backend in Progress, and a conclusion about the same.
What is PyTorch OpenCL?
OpenCL stands for open computing language and is licensed under Khronos and is used as a trademark in the company Apply Included. It is a completely loyalty-free and open-source standard platform that can be used for various Operating systems. It is specially used for parallel programming when we have multiple processors such as PCs, mobile phones, servers, and embedded platforms.
The main benefits we can reap from this are the increase in speed huge spectrum of domains where it can be used responsively, such as medical software, teaching, education, entertainment, scientific software, vision processing tools, neural network inferencing, and training and various other markets.
It is used as an alternative to CUDA. Its functionality, like AMD, is used inside the CPUs and GPUs for general-purpose in GPUs that are graphics processing units. OpenCL can be implemented in tensor as well as PyTorch.
How to use PyTorch OpenCL?
Torch 7 comes with the built-in support of OpenCL; when the base of PyTorch is the same as the Lua torch for backends, we can easily use it openly for working with integrated and modern graphics GPUs. You can find the official code on github from this link.
You can find it closed for the needed discussion, which can be found in labels though it has been open since 2017. The ticket says there is no planning regarding OpenCL work for now as AMD is moving from GPU open/ HIP with a CUDA transpiler. You can also go through this link, as there is a huge argument about whether CUDA is better or OpenCL.
To use and execute the OpenCL program, we need to perform the below-mentioned steps –
- Prepare the query for the devices and platforms of OpenCL that are available.
- For one or more devices of OpenCL, we will need to prepare the context inside the platform.
- In the created context, go for building and creating the programs of OpenCL.
- To execute the program, select the required kernel.
- To operate, create some of the objects of memory inside the kernel.
- To execute the commands on the OpenCL device, create some command queues.
- Whenever needed, you can go for the enqueuing process of data transfer commands to objects of memory.
- For execution, enqueue the kernels into the queue of commands.
- Whenever needed, you can go for enqueue commands creation that will transfer the required data back to the host device.
Porting code to OpenCL
There are various scenarios where we face this issue that it is necessary to port the existing software technology to OpenCL. For example, In the case of CUDA software, To make our application or software make runnable on the other architectures, including ARM Mali, Altera FPGA, AMD CPU, Imagination Power VR, Qualcomm Snapdragon, Xilinx FPGA, AMD GPU, Intel GPU, AMD APU, Intel CPU, and many other arising architectures.
OpenCL has the support of an extensive scale of vendors due to its Open standard nature. Moreover, it has that stability and security, which makes it capable of sustaining in the market other than any of the proprietary languages.
Until 2013, CUDA was a big hit in the market, and OpenCL was trying to come into the picture, but after that, OpenCL became a huge hit and had caught up in all the developers’ eyes. Hence, there arises a necessity of porting from CUDA to OpenCL.
There are various strategies that we can follow for porting to OpenCL. Let’s have a look at some of them –
- We can make use of events for synchronizing here. We must create various kernels, queues, and copy calls for a buffer, all of which run in parallel. Unless and until you don’t synchronize the calls with the help of certain events, we will get undefined behavior when we go for sharing all the write and read operations on the buffer while managing multiple queues. The consistency of global memory is guaranteed only after the execution of the kernel is completed.
- In each and every queue, we can accommodate any number of kernels. The execution will happen in order, and the kernels will be queued on the device as all the kernel’s execution calls are non-blocking.
- We will convert the kernel into a complete C programming language code sequentially and compile all of them into a single kernel’s work item. Along with that, we will also remove all the optimizations that are based on GPU and are present inside our code.
- Before doing so, it is necessary to know about Altera’s OpenCL and Khronos’s OpenCL standard docs, including their starting guide, best practices, and the guide for programming. Then, you can go through this case study for further reference for porting to OpenCL.
PyTorch OpenCL backend in Progress
To perform the backed builds, we can make the use of the following commands –
For running the test, execute the following command –
python mnist.py --device OpenCL:0
Note that you must load the library before executing the code. For the complete backend reference, you can refer to this link.
PyTorch OpenCL is the Open Computing Language used for cross-platform and parallel programming when multiple processors are installed on our system device.
This is a guide to PyTorch OpenCL. Here we discuss the topic of PyTorch OpenCL and will try to understand what PyTorch OpenCL is, how to use PyTorch OpenCL, porting code to OpenCL, and PyTorch OpenCL backend in Progress. You may also have a look at the following articles to learn more –