Merge tag 'drm-accel-2022-11-22' of...

.. SPDX-License-Identifier: GPL-2.0

====================

Compute Accelerators

====================

.. toctree::

   :maxdepth: 1

   introduction

.. only::  subproject and html

   Indices

   =======

   * :ref:`genindex`

.. SPDX-License-Identifier: GPL-2.0

============

Introduction

============

The Linux compute accelerators subsystem is designed to expose compute

accelerators in a common way to user-space and provide a common set of

functionality.

These devices can be either stand-alone ASICs or IP blocks inside an SoC/GPU.

Although these devices are typically designed to accelerate

Machine-Learning (ML) and/or Deep-Learning (DL) computations, the accel layer

is not limited to handling these types of accelerators.

Typically, a compute accelerator will belong to one of the following

categories:

- Edge AI - doing inference at an edge device. It can be an embedded ASIC/FPGA,

  or an IP inside a SoC (e.g. laptop web camera). These devices

  are typically configured using registers and can work with or without DMA.

- Inference data-center - single/multi user devices in a large server. This

  type of device can be stand-alone or an IP inside a SoC or a GPU. It will

  have on-board DRAM (to hold the DL topology), DMA engines and

  command submission queues (either kernel or user-space queues).

  It might also have an MMU to manage multiple users and might also enable

  virtualization (SR-IOV) to support multiple VMs on the same device. In

  addition, these devices will usually have some tools, such as profiler and

  debugger.

- Training data-center - Similar to Inference data-center cards, but typically

  have more computational power and memory b/w (e.g. HBM) and will likely have

  a method of scaling-up/out, i.e. connecting to other training cards inside

  the server or in other servers, respectively.

All these devices typically have different runtime user-space software stacks,

that are tailored-made to their h/w. In addition, they will also probably

include a compiler to generate programs to their custom-made computational

engines. Typically, the common layer in user-space will be the DL frameworks,

such as PyTorch and TensorFlow.

Sharing code with DRM

=====================

Because this type of devices can be an IP inside GPUs or have similar

characteristics as those of GPUs, the accel subsystem will use the

DRM subsystem's code and functionality. i.e. the accel core code will

be part of the DRM subsystem and an accel device will be a new type of DRM

device.

This will allow us to leverage the extensive DRM code-base and

collaborate with DRM developers that have experience with this type of

devices. In addition, new features that will be added for the accelerator

drivers can be of use to GPU drivers as well.

Differentiation from GPUs

=========================

Because we want to prevent the extensive user-space graphic software stack

from trying to use an accelerator as a GPU, the compute accelerators will be

differentiated from GPUs by using a new major number and new device char files.

Furthermore, the drivers will be located in a separate place in the kernel

tree - drivers/accel/.

The accelerator devices will be exposed to the user space with the dedicated

261 major number and will have the following convention:

- device char files - /dev/accel/accel*

- sysfs             - /sys/class/accel/accel*/

- debugfs           - /sys/kernel/debug/accel/accel*/

Getting Started

===============

First, read the DRM documentation at Documentation/gpu/index.rst.

Not only it will explain how to write a new DRM driver but it will also

contain all the information on how to contribute, the Code Of Conduct and

what is the coding style/documentation. All of that is the same for the

accel subsystem.

Second, make sure the kernel is configured with CONFIG_DRM_ACCEL.

To expose your device as an accelerator, two changes are needed to

be done in your driver (as opposed to a standard DRM driver):

- Add the DRIVER_COMPUTE_ACCEL feature flag in your drm_driver's

  driver_features field. It is important to note that this driver feature is

  mutually exclusive with DRIVER_RENDER and DRIVER_MODESET. Devices that want

  to expose both graphics and compute device char files should be handled by

  two drivers that are connected using the auxiliary bus framework.

- Change the open callback in your driver fops structure to accel_open().

  Alternatively, your driver can use DEFINE_DRM_ACCEL_FOPS macro to easily

  set the correct function operations pointers structure.

External References

===================

email threads

-------------

* `Initial discussion on the New subsystem for acceleration devices <https://lkml.org/lkml/2022/7/31/83>`_ - Oded Gabbay (2022)

* `patch-set to add the new subsystem <https://lkml.org/lkml/2022/10/22/544>`_ - Oded Gabbay (2022)

Conference talks

----------------

* `LPC 2022 Accelerators BOF outcomes summary <https://airlied.blogspot.com/2022/09/accelerators-bof-outcomes-summary.html>`_ - Dave Airlie (2022)

		  ...

		  255 = /dev/osd255	256th OSD Device

 261 char	Compute Acceleration Devices

		  0 = /dev/accel/accel0	First acceleration device

		  1 = /dev/accel/accel1	Second acceleration device

		    ...

 384-511 char	RESERVED FOR DYNAMIC ASSIGNMENT

		Character devices that request a dynamic allocation of major

		number will take numbers starting from 511 and downward,

   input/index

   hwmon/index

   gpu/index

   accel/index

   security/index

   sound/index

   crypto/index

F:	include/linux/vga*

F:	include/uapi/drm/drm*

DRM COMPUTE ACCELERATORS DRIVERS AND FRAMEWORK

M:	Oded Gabbay <ogabbay@kernel.org>

L:	dri-devel@lists.freedesktop.org

S:	Maintained

C:	irc://irc.oftc.net/dri-devel

T:	git https://git.kernel.org/pub/scm/linux/kernel/git/ogabbay/accel.git

F:	Documentation/accel/

F:	drivers/accel/

DRM DRIVERS FOR ALLWINNER A10

M:	Maxime Ripard <mripard@kernel.org>

M:	Chen-Yu Tsai <wens@csie.org>