1 .. SPDX-License-Identifier: GPL-2.0 2 3 ============ 4 Introduction 5 ============ 6 7 The Linux compute accelerators subsystem is designed to expose compute 8 accelerators in a common way to user-space and provide a common set of 9 functionality. 10 11 These devices can be either stand-alone ASICs or IP blocks inside an SoC/GPU. 12 Although these devices are typically designed to accelerate 13 Machine-Learning (ML) and/or Deep-Learning (DL) computations, the accel layer 14 is not limited to handling these types of accelerators. 15 16 Typically, a compute accelerator will belong to one of the following 17 categories: 18 19 - Edge AI - doing inference at an edge device. It can be an embedded ASIC/FPGA, 20 or an IP inside a SoC (e.g. laptop web camera). These devices 21 are typically configured using registers and can work with or without DMA. 22 23 - Inference data-center - single/multi user devices in a large server. This 24 type of device can be stand-alone or an IP inside a SoC or a GPU. It will 25 have on-board DRAM (to hold the DL topology), DMA engines and 26 command submission queues (either kernel or user-space queues). 27 It might also have an MMU to manage multiple users and might also enable 28 virtualization (SR-IOV) to support multiple VMs on the same device. In 29 addition, these devices will usually have some tools, such as profiler and 30 debugger. 31 32 - Training data-center - Similar to Inference data-center cards, but typically 33 have more computational power and memory b/w (e.g. HBM) and will likely have 34 a method of scaling-up/out, i.e. connecting to other training cards inside 35 the server or in other servers, respectively. 36 37 All these devices typically have different runtime user-space software stacks, 38 that are tailored-made to their h/w. In addition, they will also probably 39 include a compiler to generate programs to their custom-made computational 40 engines. Typically, the common layer in user-space will be the DL frameworks, 41 such as PyTorch and TensorFlow. 42 43 Sharing code with DRM 44 ===================== 45 46 Because this type of devices can be an IP inside GPUs or have similar 47 characteristics as those of GPUs, the accel subsystem will use the 48 DRM subsystem's code and functionality. i.e. the accel core code will 49 be part of the DRM subsystem and an accel device will be a new type of DRM 50 device. 51 52 This will allow us to leverage the extensive DRM code-base and 53 collaborate with DRM developers that have experience with this type of 54 devices. In addition, new features that will be added for the accelerator 55 drivers can be of use to GPU drivers as well. 56 57 Differentiation from GPUs 58 ========================= 59 60 Because we want to prevent the extensive user-space graphic software stack 61 from trying to use an accelerator as a GPU, the compute accelerators will be 62 differentiated from GPUs by using a new major number and new device char files. 63 64 Furthermore, the drivers will be located in a separate place in the kernel 65 tree - drivers/accel/. 66 67 The accelerator devices will be exposed to the user space with the dedicated 68 261 major number and will have the following convention: 69 70 - device char files - /dev/accel/accel\* 71 - sysfs - /sys/class/accel/accel\*/ 72 - debugfs - /sys/kernel/debug/accel/\*/ 73 74 Getting Started 75 =============== 76 77 First, read the DRM documentation at Documentation/gpu/index.rst. 78 Not only it will explain how to write a new DRM driver but it will also 79 contain all the information on how to contribute, the Code Of Conduct and 80 what is the coding style/documentation. All of that is the same for the 81 accel subsystem. 82 83 Second, make sure the kernel is configured with CONFIG_DRM_ACCEL. 84 85 To expose your device as an accelerator, two changes are needed to 86 be done in your driver (as opposed to a standard DRM driver): 87 88 - Add the DRIVER_COMPUTE_ACCEL feature flag in your drm_driver's 89 driver_features field. It is important to note that this driver feature is 90 mutually exclusive with DRIVER_RENDER and DRIVER_MODESET. Devices that want 91 to expose both graphics and compute device char files should be handled by 92 two drivers that are connected using the auxiliary bus framework. 93 94 - Change the open callback in your driver fops structure to accel_open(). 95 Alternatively, your driver can use DEFINE_DRM_ACCEL_FOPS macro to easily 96 set the correct function operations pointers structure. 97 98 External References 99 =================== 100 101 email threads 102 ------------- 103 104 * `Initial discussion on the New subsystem for acceleration devices <https://lore.kernel.org/lkml/CAFCwf11=9qpNAepL7NL+YAV_QO=Wv6pnWPhKHKAepK3fNn+2Dg@mail.gmail.com/">https://lore.kernel.org/lkml/CAFCwf11=9qpNAepL7NL+YAV_QO=Wv6pnWPhKHKAepK3fNn+2Dg@mail.gmail.com/>`_ - Oded Gabbay (2022) 105 * `patch-set to add the new subsystem <https://lore.kernel.org/lkml/20221022214622.18042-1-ogabbay@kernel.org/">https://lore.kernel.org/lkml/20221022214622.18042-1-ogabbay@kernel.org/>`_ - Oded Gabbay (2022) 106 107 Conference talks 108 ---------------- 109 110 * `LPC 2022 Accelerators BOF outcomes summary <https://airlied.blogspot.com/2022/09/accelerators-bof-outcomes-summary.html>`_ - Dave Airlie (2022)
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