在qemu virt平台运行xen, guest OS Dom0 Mini-OS

kvm、qemu、libvirt介绍 1、KVM 是一个开源的系统虚拟化模块。它使用Linux自身的调度器进行管理。 •KVM的虚拟化需要硬件支持(如Intel VT技术或者AMD V技术)。是基于硬件的完全虚拟化。 •可允许运行多个虚拟机,包括 Linux 和 Windows操作系统。 KVM的结构如下图所示 •KVM从存在形式看,是两个内核模块kvm.ko和kvm_intel.ko(对AMD处理器来说,就是kvm_amd.ko)... 阅读详情

1)编译mini-os

        到github上去下载合适的mini-os源文件,或者使用博主下载的mini-os。

注:博主已修改过一些东西,不需要的则可自行下载。

编译生成可执行文件:

make MINIOS_COMPILE_ARCH=arm64 CROSS_COMPILE=aarch64-linux-gnu-

编译生成文件mini-os.img,将其拷贝至跟xen同一目录下。

2)运行 Qemu Xen Dom0 Mini-OS

qemu-system-aarch64 -machine virt,gic_version=3 -machine virtualization=true -cpu cortex-a57 -machine type=virt -m 2048 -smp 4 -bios u-boot.bin -device loader,file=xen,force-raw=on,addr=0x49000000 -device loader,file=mini-os.img,addr=0x47000000 -device loader,file=virt-gicv3.dtb,addr=0x44000000 -nographic -no-reboot -chardev socket,id=qemu-monitor,host=localhost,port=7777,server,nowait,telnet -mon qemu-monitor,mode=readline

U-Boot 2019.01 (Aug 25 2021 - 02:57:47 -0700)

DRAM:  2 GiB
Flash: 128 MiB
*** Warning - bad CRC, using default environment

In:    pl011@9000000
Out:   pl011@9000000
Err:   pl011@9000000
Net:   No ethernet found.
Hit any key to stop autoboot:  0 
=> 

配置设备树:

setenv xen_bootargs 'dom0_mem=512M'
fdt addr 0x44000000
fdt resize
fdt set /chosen \#address-cells <1>
fdt set /chosen \#size-cells <1>
fdt mknod /chosen module@0
fdt set /chosen/module@0 compatible "xen,linux-zimage" "xen,multiboot-module"
fdt set /chosen/module@0 reg <0x47000000 0x29388>
booti 0x49000000 - 0x44000000

mini-os运行结果:

U-Boot 2019.01 (Aug 25 2021 - 02:57:47 -0700)

DRAM:  2 GiB
Flash: 128 MiB
*** Warning - bad CRC, using default environment

In:    pl011@9000000
Out:   pl011@9000000
Err:   pl011@9000000
Net:   No ethernet found.
Hit any key to stop autoboot:  0 
=> setenv xen_bootargs 'dom0_mem=512M'
=> fdt addr 0x44000000
=> fdt resize
=> fdt set /chosen \#address-cells <1>
=> fdt set /chosen \#size-cells <1>
=> fdt mknod /chosen module@0
=> fdt set /chosen/module@0 compatible "xen,linux-zimage" "xen,multiboot-module"
=> fdt set /chosen/module@0 reg <0x47000000 0x29388>
=> booti 0x49000000 - 0x44000000
## Flattened Device Tree blob at 44000000
   Booting using the fdt blob at 0x44000000
   reserving fdt memory region: addr=44000000 size=2000
   Loading Device Tree to 00000000bedf6000, end 00000000bedfafff ... OK

Starting kernel ...

 Xen 4.16-unstable
(XEN) Xen version 4.16-unstable (hdwei@) (aarch64-linux-gnu-gcc (Linaro GCC 7.2-2017.11) 7.2.1 20171011) debug=y Thu Aug 26 01:38:18 PDT 2021
(XEN) Latest ChangeSet: Fri Aug 20 16:18:12 2021 +0200 git:93713f4
(XEN) build-id: 8378c443d4d2f5c70e34b7b0a987e652a6270b9f
(XEN) Processor: 00000000411fd070: "ARM Limited", variant: 0x1, part 0xd07,rev 0x0
(XEN) 64-bit Execution:
(XEN)   Processor Features: 0000000001000222 0000000000000000
(XEN)     Exception Levels: EL3:No EL2:64+32 EL1:64+32 EL0:64+32
(XEN)     Extensions: FloatingPoint AdvancedSIMD GICv3-SysReg
(XEN)   Debug Features: 0000000010305106 0000000000000000
(XEN)   Auxiliary Features: 0000000000000000 0000000000000000
(XEN)   Memory Model Features: 0000000000001124 0000000000000000
(XEN)   ISA Features:  0000000000011120 0000000000000000
(XEN) 32-bit Execution:
(XEN)   Processor Features: 0000000000000131:0000000010011001
(XEN)     Instruction Sets: AArch32 A32 Thumb Thumb-2 Jazelle
(XEN)     Extensions: GenericTimer
(XEN)   Debug Features: 0000000003010066
(XEN)   Auxiliary Features: 0000000000000000
(XEN)   Memory Model Features: 0000000010101105 0000000040000000
(XEN)                          0000000001260000 0000000002102211
(XEN)   ISA Features: 0000000002101110 0000000013112111 0000000021232042
(XEN)                 0000000001112131 0000000000011142 0000000000011121
(XEN) Using SMC Calling Convention v1.0
(XEN) Using PSCI v0.2
(XEN) SMP: Allowing 4 CPUs
(XEN) enabled workaround for: ARM erratum 832075
(XEN) enabled workaround for: ARM erratum 834220
(XEN) enabled workaround for: ARM erratum 1319367
(XEN) Generic Timer IRQ: phys=30 hyp=26 virt=27 Freq: 62500 KHz
(XEN) GICv3 initialization:
(XEN)       gic_dist_addr=0x00000008000000
(XEN)       gic_maintenance_irq=25
(XEN)       gic_rdist_stride=0
(XEN)       gic_rdist_regions=1
(XEN)       redistributor regions:
(XEN)         - region 0: 0x000000080a0000 - 0x00000009000000
(XEN) GICv3: 256 lines, (IID 0000043b).
(XEN) GICv3: CPU0: Found redistributor in region 0 @000000004001c000
(XEN) XSM Framework v1.0.0 initialized
(XEN) Initialising XSM SILO mode
(XEN) Using scheduler: SMP Credit Scheduler rev2 (credit2)
(XEN) Initializing Credit2 scheduler
(XEN)  load_precision_shift: 18
(XEN)  load_window_shift: 30
(XEN)  underload_balance_tolerance: 0
(XEN)  overload_balance_tolerance: -3
(XEN)  runqueues arrangement: socket
(XEN)  cap enforcement granularity: 10ms
(XEN) load tracking window length 1073741824 ns
(XEN) Allocated console ring of 32 KiB.
(XEN) CPU0: Guest atomics will try 1 times before pausing the domain
(XEN) Bringing up CPU1
(XEN) GICv3: CPU1: Found redistributor in region 0 @000000004003c000
(XEN) CPU1: Guest atomics will try 1 times before pausing the domain
(XEN) CPU 1 booted.
(XEN) Bringing up CPU2
(XEN) GICv3: CPU2: Found redistributor in region 0 @000000004005c000
(XEN) CPU2: Guest atomics will try 1 times before pausing the domain
(XEN) CPU 2 booted.
(XEN) Bringing up CPU3
(XEN) GICv3: CPU3: Found redistributor in region 0 @000000004007c000
(XEN) CPU3: Guest atomics will try 2 times before pausing the domain
(XEN) CPU 3 booted.
(XEN) Brought up 4 CPUs
(XEN) I/O virtualisation disabled
(XEN) P2M: 44-bit IPA with 44-bit PA and 8-bit VMID
(XEN) P2M: 4 levels with order-0 root, VTCR 0x0000000080043594
(XEN) Scheduling granularity: cpu, 1 CPU per sched-resource
(XEN) Adding cpu 0 to runqueue 0
(XEN)  First cpu on runqueue, activating
(XEN) Adding cpu 1 to runqueue 0
(XEN) Adding cpu 2 to runqueue 0
(XEN) Adding cpu 3 to runqueue 0
(XEN) alternatives: Patching with alt table 00000000002dc408 -> 00000000002dcc18
(XEN) **** No support for ARM_SMCCC_ARCH_WORKAROUND_1. ****
(XEN) **** Please update your firmware.                ****
(XEN) **** No support for ARM_SMCCC_ARCH_WORKAROUND_1. ****
(XEN) **** Please update your firmware.                ****
(XEN) **** No support for ARM_SMCCC_ARCH_WORKAROUND_1. ****
(XEN) **** Please update your firmware.                ****
(XEN) **** This CPU is affected by the errata 832075.                      ****
(XEN) **** Guests without CPU erratum workarounds can deadlock the system! ****
(XEN) **** Only trusted guests should be used.                             ****
(XEN) *** LOADING DOMAIN 0 ***
(XEN) Loading d0 kernel from boot module @ 0000000047000000
(XEN) Allocating 1:1 mappings totalling 512MB for dom0:
(XEN) BANK[0] 0x00000060000000-0x00000080000000 (512MB)
(XEN) Grant table range: 0x00000049000000-0x00000049040000
(XEN) Allocating PPI 16 for event channel interrupt
(XEN) Loading zImage from 0000000047000000 to 0000000060000000-0000000060029388
(XEN) Loading d0 DTB to 0x0000000068000000-0x0000000068001c88
(XEN) Initial low memory virq threshold set at 0x4000 pages.
(XEN) Scrubbing Free RAM in background
(XEN) Std. Loglevel: All
(XEN) Guest Loglevel: All
(XEN) ***************************************************
(XEN) PLEASE SPECIFY dom0_mem PARAMETER - USING 512M FOR NOW
(XEN) ***************************************************
(XEN) 3... 2... 1... 
(XEN) *** Serial input to DOM0 (type 'CTRL-a' three times to switch input)
(XEN) Freed 336kB init memory.
- Mini-OS booting -
- Setup CPU -
- Setup booting pagetable -
- MMU on -
- Setup stack -
- Jumping to C entry -
Checking DTB at ffffffbfffe00000...
map_console, phys = 0x0
Console is on port 0
Console ring is at mfn ffffffbfffc00000
MM: Init
    _text: ffffffc000000000(VA)
    _etext: ffffffc0000182a0(VA)
    _erodata: ffffffc00001e000(VA)
    _edata: ffffffc000029388(VA)
    stack start: ffffffc000025000(VA)
    _end: ffffffc0000327d8(VA)
Found memory at 0x60000000 (len 0x20000000)
Using pages 393267 to 524288 as free space for heap.
MM: Initialise page allocator for ffffffc000033000(60033000)-ffffffc020000000(80000000)
MM: done
Initialising timer interface
Virtual Count register is d61fdcf, freq = 62500000 Hz
Initialising console ... done.
Initialising scheduler
Thread "Idle": pointer: 0xffffffc000043020, stack: 0xffffffc000050000
Thread "xenstore": pointer: 0xffffffc000043080, stack: 0xffffffc000060000
xenbus initialised on irq 0 mfn 0x0
Thread "shutdown": pointer: 0xffffffc0000430e0, stack: 0xffffffc000070000
start mini-os 
hello world!!! 
hello world!!! 
hello world!!! 
hello world!!! 

已编译好的可执行文件

qemu-virtio基本原理 virtio比较零散抽象,看了几回也没看懂怎么回事,记录一下自己的理解,不知道怎样组织内容,就以问答的形式吧。 virtio整体流程是怎样的? 盗用网上的一张图: 一开始我也看不明白这里面都画了些啥东西,后面慢慢抽丝剥茧吧。 virtio是如何实现共享内存的? 大家都知道virtio以共享内存的零拷贝实现高性能的,理解virtio内存共享是理解virtio的基础。 virtio实现共享内存基于两个基本的条件: qemu或者说host可以访问guest的任意内存地址的内容。 qemu知道guest中物理地 阅读详情

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B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; 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