111 строки
4.7 KiB
ReStructuredText
111 строки
4.7 KiB
ReStructuredText
.. SPDX-License-Identifier: GPL-2.0
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============
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Introduction
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============
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The Linux compute accelerators subsystem is designed to expose compute
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accelerators in a common way to user-space and provide a common set of
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functionality.
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These devices can be either stand-alone ASICs or IP blocks inside an SoC/GPU.
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Although these devices are typically designed to accelerate
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Machine-Learning (ML) and/or Deep-Learning (DL) computations, the accel layer
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is not limited to handling these types of accelerators.
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Typically, a compute accelerator will belong to one of the following
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categories:
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- Edge AI - doing inference at an edge device. It can be an embedded ASIC/FPGA,
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or an IP inside a SoC (e.g. laptop web camera). These devices
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are typically configured using registers and can work with or without DMA.
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- Inference data-center - single/multi user devices in a large server. This
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type of device can be stand-alone or an IP inside a SoC or a GPU. It will
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have on-board DRAM (to hold the DL topology), DMA engines and
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command submission queues (either kernel or user-space queues).
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It might also have an MMU to manage multiple users and might also enable
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virtualization (SR-IOV) to support multiple VMs on the same device. In
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addition, these devices will usually have some tools, such as profiler and
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debugger.
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- Training data-center - Similar to Inference data-center cards, but typically
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have more computational power and memory b/w (e.g. HBM) and will likely have
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a method of scaling-up/out, i.e. connecting to other training cards inside
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the server or in other servers, respectively.
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All these devices typically have different runtime user-space software stacks,
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that are tailored-made to their h/w. In addition, they will also probably
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include a compiler to generate programs to their custom-made computational
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engines. Typically, the common layer in user-space will be the DL frameworks,
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such as PyTorch and TensorFlow.
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Sharing code with DRM
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=====================
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Because this type of devices can be an IP inside GPUs or have similar
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characteristics as those of GPUs, the accel subsystem will use the
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DRM subsystem's code and functionality. i.e. the accel core code will
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be part of the DRM subsystem and an accel device will be a new type of DRM
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device.
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This will allow us to leverage the extensive DRM code-base and
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collaborate with DRM developers that have experience with this type of
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devices. In addition, new features that will be added for the accelerator
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drivers can be of use to GPU drivers as well.
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Differentiation from GPUs
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=========================
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Because we want to prevent the extensive user-space graphic software stack
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from trying to use an accelerator as a GPU, the compute accelerators will be
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differentiated from GPUs by using a new major number and new device char files.
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Furthermore, the drivers will be located in a separate place in the kernel
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tree - drivers/accel/.
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The accelerator devices will be exposed to the user space with the dedicated
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261 major number and will have the following convention:
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- device char files - /dev/accel/accel\*
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- sysfs - /sys/class/accel/accel\*/
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- debugfs - /sys/kernel/debug/accel/\*/
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Getting Started
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===============
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First, read the DRM documentation at Documentation/gpu/index.rst.
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Not only it will explain how to write a new DRM driver but it will also
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contain all the information on how to contribute, the Code Of Conduct and
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what is the coding style/documentation. All of that is the same for the
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accel subsystem.
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Second, make sure the kernel is configured with CONFIG_DRM_ACCEL.
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To expose your device as an accelerator, two changes are needed to
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be done in your driver (as opposed to a standard DRM driver):
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- Add the DRIVER_COMPUTE_ACCEL feature flag in your drm_driver's
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driver_features field. It is important to note that this driver feature is
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mutually exclusive with DRIVER_RENDER and DRIVER_MODESET. Devices that want
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to expose both graphics and compute device char files should be handled by
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two drivers that are connected using the auxiliary bus framework.
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- Change the open callback in your driver fops structure to accel_open().
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Alternatively, your driver can use DEFINE_DRM_ACCEL_FOPS macro to easily
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set the correct function operations pointers structure.
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External References
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===================
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email threads
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-------------
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* `Initial discussion on the New subsystem for acceleration devices <https://lkml.org/lkml/2022/7/31/83>`_ - Oded Gabbay (2022)
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* `patch-set to add the new subsystem <https://lkml.org/lkml/2022/10/22/544>`_ - Oded Gabbay (2022)
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Conference talks
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----------------
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* `LPC 2022 Accelerators BOF outcomes summary <https://airlied.blogspot.com/2022/09/accelerators-bof-outcomes-summary.html>`_ - Dave Airlie (2022)
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