1 .. SPDX-License-Identifier: GPL-2.0 1 .. SPDX-License-Identifier: GPL-2.0
2 2
3 ============ 3 ============
4 Introduction 4 Introduction
5 ============ 5 ============
6 6
7 The Linux compute accelerators subsystem is de 7 The Linux compute accelerators subsystem is designed to expose compute
8 accelerators in a common way to user-space and 8 accelerators in a common way to user-space and provide a common set of
9 functionality. 9 functionality.
10 10
11 These devices can be either stand-alone ASICs 11 These devices can be either stand-alone ASICs or IP blocks inside an SoC/GPU.
12 Although these devices are typically designed 12 Although these devices are typically designed to accelerate
13 Machine-Learning (ML) and/or Deep-Learning (DL 13 Machine-Learning (ML) and/or Deep-Learning (DL) computations, the accel layer
14 is not limited to handling these types of acce 14 is not limited to handling these types of accelerators.
15 15
16 Typically, a compute accelerator will belong t 16 Typically, a compute accelerator will belong to one of the following
17 categories: 17 categories:
18 18
19 - Edge AI - doing inference at an edge device. 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 camer 20 or an IP inside a SoC (e.g. laptop web camera). These devices
21 are typically configured using registers and 21 are typically configured using registers and can work with or without DMA.
22 22
23 - Inference data-center - single/multi user de 23 - Inference data-center - single/multi user devices in a large server. This
24 type of device can be stand-alone or an IP i 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) 25 have on-board DRAM (to hold the DL topology), DMA engines and
26 command submission queues (either kernel or 26 command submission queues (either kernel or user-space queues).
27 It might also have an MMU to manage multiple 27 It might also have an MMU to manage multiple users and might also enable
28 virtualization (SR-IOV) to support multiple 28 virtualization (SR-IOV) to support multiple VMs on the same device. In
29 addition, these devices will usually have so 29 addition, these devices will usually have some tools, such as profiler and
30 debugger. 30 debugger.
31 31
32 - Training data-center - Similar to Inference 32 - Training data-center - Similar to Inference data-center cards, but typically
33 have more computational power and memory b/w 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 34 a method of scaling-up/out, i.e. connecting to other training cards inside
35 the server or in other servers, respectively 35 the server or in other servers, respectively.
36 36
37 All these devices typically have different run 37 All these devices typically have different runtime user-space software stacks,
38 that are tailored-made to their h/w. In additi 38 that are tailored-made to their h/w. In addition, they will also probably
39 include a compiler to generate programs to the 39 include a compiler to generate programs to their custom-made computational
40 engines. Typically, the common layer in user-s 40 engines. Typically, the common layer in user-space will be the DL frameworks,
41 such as PyTorch and TensorFlow. 41 such as PyTorch and TensorFlow.
42 42
43 Sharing code with DRM 43 Sharing code with DRM
44 ===================== 44 =====================
45 45
46 Because this type of devices can be an IP insi 46 Because this type of devices can be an IP inside GPUs or have similar
47 characteristics as those of GPUs, the accel su 47 characteristics as those of GPUs, the accel subsystem will use the
48 DRM subsystem's code and functionality. i.e. t 48 DRM subsystem's code and functionality. i.e. the accel core code will
49 be part of the DRM subsystem and an accel devi 49 be part of the DRM subsystem and an accel device will be a new type of DRM
50 device. 50 device.
51 51
52 This will allow us to leverage the extensive D 52 This will allow us to leverage the extensive DRM code-base and
53 collaborate with DRM developers that have expe 53 collaborate with DRM developers that have experience with this type of
54 devices. In addition, new features that will b 54 devices. In addition, new features that will be added for the accelerator
55 drivers can be of use to GPU drivers as well. 55 drivers can be of use to GPU drivers as well.
56 56
57 Differentiation from GPUs 57 Differentiation from GPUs
58 ========================= 58 =========================
59 59
60 Because we want to prevent the extensive user- 60 Because we want to prevent the extensive user-space graphic software stack
61 from trying to use an accelerator as a GPU, th 61 from trying to use an accelerator as a GPU, the compute accelerators will be
62 differentiated from GPUs by using a new major 62 differentiated from GPUs by using a new major number and new device char files.
63 63
64 Furthermore, the drivers will be located in a 64 Furthermore, the drivers will be located in a separate place in the kernel
65 tree - drivers/accel/. 65 tree - drivers/accel/.
66 66
67 The accelerator devices will be exposed to the 67 The accelerator devices will be exposed to the user space with the dedicated
68 261 major number and will have the following c 68 261 major number and will have the following convention:
69 69
70 - device char files - /dev/accel/accel\* 70 - device char files - /dev/accel/accel\*
71 - sysfs - /sys/class/accel/accel\* 71 - sysfs - /sys/class/accel/accel\*/
72 - debugfs - /sys/kernel/debug/accel/ 72 - debugfs - /sys/kernel/debug/accel/\*/
73 73
74 Getting Started 74 Getting Started
75 =============== 75 ===============
76 76
77 First, read the DRM documentation at Documenta 77 First, read the DRM documentation at Documentation/gpu/index.rst.
78 Not only it will explain how to write a new DR 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 contribu 79 contain all the information on how to contribute, the Code Of Conduct and
80 what is the coding style/documentation. All of 80 what is the coding style/documentation. All of that is the same for the
81 accel subsystem. 81 accel subsystem.
82 82
83 Second, make sure the kernel is configured wit 83 Second, make sure the kernel is configured with CONFIG_DRM_ACCEL.
84 84
85 To expose your device as an accelerator, two c 85 To expose your device as an accelerator, two changes are needed to
86 be done in your driver (as opposed to a standa 86 be done in your driver (as opposed to a standard DRM driver):
87 87
88 - Add the DRIVER_COMPUTE_ACCEL feature flag in 88 - Add the DRIVER_COMPUTE_ACCEL feature flag in your drm_driver's
89 driver_features field. It is important to no 89 driver_features field. It is important to note that this driver feature is
90 mutually exclusive with DRIVER_RENDER and DR 90 mutually exclusive with DRIVER_RENDER and DRIVER_MODESET. Devices that want
91 to expose both graphics and compute device c 91 to expose both graphics and compute device char files should be handled by
92 two drivers that are connected using the aux 92 two drivers that are connected using the auxiliary bus framework.
93 93
94 - Change the open callback in your driver fops 94 - Change the open callback in your driver fops structure to accel_open().
95 Alternatively, your driver can use DEFINE_DR 95 Alternatively, your driver can use DEFINE_DRM_ACCEL_FOPS macro to easily
96 set the correct function operations pointers 96 set the correct function operations pointers structure.
97 97
98 External References 98 External References
99 =================== 99 ===================
100 100
101 email threads 101 email threads
102 ------------- 102 -------------
103 103
104 * `Initial discussion on the New subsystem for< !! 104 * `Initial discussion on the New subsystem for acceleration devices <https://lkml.org/lkml/2022/7/31/83>`_ - Oded Gabbay (2022)
105 * `patch-set to add the new subsystem <https:// !! 105 * `patch-set to add the new subsystem <https://lkml.org/lkml/2022/10/22/544>`_ - Oded Gabbay (2022)
106 106
107 Conference talks 107 Conference talks
108 ---------------- 108 ----------------
109 109
110 * `LPC 2022 Accelerators BOF outcomes summary 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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