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.
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/*/
First, read the DRM documentation at GPU Driver Developer's Guide. 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.
Initial discussion on the New subsystem for acceleration devices - Oded Gabbay (2022)
patch-set to add the new subsystem - Oded Gabbay (2022)
LPC 2022 Accelerators BOF outcomes summary - Dave Airlie (2022)