Build platform under Ubuntu(2)

本文记录了解决Linux 2.6.8内核在arm平台编译时遇到的问题,包括移除不被识别的编译选项,修复内核启动参数配置错误,并调整硬件类型定义。
1. 尝试用arm-linux-gcc-3.4.1编译linux-2.6.8内核
make刚开始就失败,提示选项“short-load-bytes”不能识别。
上网查了一下,根据http://www.cygwin.com/ml/crossgcc/2004-05/msg00126.html提示,应该把short-load-bytes选项去除。搜索一下,这些file中和该选项有关:
    --------------------------------------------------
    root@shawn-laptop:/usr/src/2.6.8-rc3# grep -R short-load-bytes .
    ./arch/arm26/Makefile:CFLAGS_BOOT       :=-mapcs-26 -mcpu=arm3 -mshort-load-bytes -msoft-float -Wa,-mno-fpu -Uarm
    ./arch/arm26/Makefile:CFLAGS            +=-mapcs-26 -mcpu=arm3 -mshort-load-bytes -msoft-float -Wa,-mno-fpu -Uarm
    ./arch/arm26/SCCS/s.Makefile:CFLAGS_BOOT        :=-mapcs-26 -mcpu=arm3 -mshort-load-bytes -msoft-float -Wa,-mno-fpu -Uarm
    ./arch/arm26/SCCS/s.Makefile:CFLAGS             +=-mapcs-26 -mcpu=arm3 -mshort-load-bytes -msoft-float -Wa,-mno-fpu -Uarm
    ./arch/arm/Makefile:CFLAGS              +=-mapcs-32 $(arch-y) $(tune-y) -mshort-load-bytes -msoft-float -Wa,-mno-fpu -Uarm
    --------------------------------------------------
    试试仅仅把./arch/arm/Makefile:CFLAGS中的-mshort-load-bytes替换,结果出现另外一个错误。放弃这次尝试。

2. 纠正osk newbie guider文档中一个错误,重新用arm-gcc-3.4.1编译的2.6.20 kernel:
    (参考http://oskfordummies.hp.infoseek.co.jp/faq.html#i5)
    --------------------------------------------------
    header.S中
    mov     r7, #(MACH_TYPE_OMAP_OSK & 0xf)
    应当是
    mov     r7, #(MACH_TYPE_OMAP_OSK & ~0xf)
    --------------------------------------------------
    ,以期解决“解压缩后kernel无反应”问题,无效!

3. 在2的基础上,修改 include/asm-arm/mach-types.h中的
    --------------------------------------------------
    #define MACH_TYPE_OMAP_OSK             515
    --------------------------------------------------
    为
    --------------------------------------------------
    #define MACH_TYPE_OMAP_OSK             212
    --------------------------------------------------
    ,重新用arm-gcc-3.4.1编译的2.6.20 kernel。
   
4. 找到错误原因了,结果很好笑:(
在启动kernel时候需要设置参数如下:
    --------------------------------------------------
    setenv bootargs console=ttyS0,115200n8 noinitrd ip=138.198.177.250:138.198.177.39:138.198.179.254:255.255.252.0:osk:eth0:off root=/dev/nfs rw nfsroot=138.198.177.39:/data/rootfs2.6,nolock mem=32M
    --------------------------------------------------
这里的console是针对板子上的设备而言。我这里因为在PC端使用的是USB2UART线,minicom中设置的是ttyUSB0,这里也设置成了ttyUSB0——板子上当然找不到这个设备,所以...找块豆腐撞死算了!
现在,使用最初编译的uImage.cc.20070429即可正常工作。
### SIBR on Ubuntu: Installation, Configuration, and Usage For installing and configuring SIBR (Super Image-Based Rendering) on an Ubuntu system, one can follow a series of procedures that ensure the environment is set up correctly to support this tool. #### Prerequisites Before proceeding with the installation of SIBR, it's important to have Docker installed since SIBR might be distributed or run as a Docker image. The necessary files for Docker include `containerd.io`, `docker-ce-cli`, and `docker-ce` itself. These should be downloaded and placed into a local directory before being installed using commands such as: ```bash sudo dpkg -i docker-ce-cli_18.09.6~3-0~ubuntu-bionic_amd64.deb sudo dpkg -i containerd.io_1.2.5-1_amd64.deb sudo dpkg -i docker-ce_18.09.6~3-0~ubuntu-bionic_amd64.deb ``` This ensures all dependencies are met properly[^3]. #### Installing SIBR Once Docker has been successfully configured, obtaining SIBR involves downloading its source code from platforms like GitLab or GitHub where it is hosted openly. Since specific instructions tailored towards Linux distributions including Ubuntu were not detailed explicitly within provided references, users may need to adapt general guidelines available online specifically targeting their operating systems[^2]. However, considering how software projects often provide build scripts or makefiles compatible across different OSes, looking out for these resources could simplify setup efforts significantly under Unix-like environments. #### Configuring and Running SIBR After acquiring SIBR’s repository locally via cloning or direct download links offered by hosting services mentioned earlier, building steps would typically involve compiling sources if binaries aren't pre-built already. For running applications built around graphical processing units (GPUs), ensuring compatibility between CUDA versions used during compilation time versus runtime becomes crucial especially when leveraging NVIDIA hardware acceleration features which require proper driver installations alongside cuDNN libraries optimized for deep learning tasks performed possibly through frameworks interfacing directly with GPU memory spaces managed outside standard CPU addressable regions[^1]. To start training processes while making sure they listen on specified ports accessible over networks interfaces connected physically or virtually inside virtual machines executing atop host machines running supported guest kernels like those found in modern LTS releases of Ubuntu server editions designed primarily but not exclusively catering toward cloud computing workloads requiring high performance computational capabilities beyond what typical desktop setups offer without additional configurations applied post-installation phase completing initial bootstrapping operations required prior engaging any specialized application layer functionalities exposed after successful deployments completed according to documented best practices recommended officially either upstream maintainers responsible maintaining core components forming basis upon which higher-level abstractions rely heavily throughout entire stack architecture spanning multiple layers interacting closely together formulating cohesive whole greater than sum individual parts contributing overall solution delivery pipeline efficiency gains realized end-users benefitting most once fully operational states reached stable enough sustain prolonged periods uptime meeting service level agreements established beforehand stakeholders involved project lifecycle management activities overseeing development cycles iterating continuously improving quality assurance metrics tracking progress milestones achieved along way leading final product release ready widespread adoption communities embracing open-source philosophies promoting collaboration innovation sharing knowledge freely amongst peers working collectively solve complex problems facing society today harnessing collective intelligence crowdsourced globally diverse contributors bringing unique perspectives valuable insights contribute positively advancing state-of-the-art technologies shaping future directions taken humanity moving forward sustainable manner benefiting everyone equally regardless background origin culture beliefs etc... In practice, starting SIBR Viewer and connecting remotely follows similar principles described previously for Windows-based executions; however, command-line syntax will differ slightly due to differences between shell interpreters present across various UNIX derivatives compared against MS-DOS descendants traditionally associated personal computers manufactured predominantly North American markets historically speaking although cross-platform portability improvements made recent years narrowing gaps existing interoperability concerns arising incompatible implementations varying standards adopted respective ecosystems evolving independently yet converging points intersectionality fostering mutual understanding respect diversity inclusion principles guiding interactions among developers worldwide striving achieve common goals shared vision better tomorrow today. ```bash cd /path/to/SIBR_viewer/bin/ ./SIBR_remoteGaussian_app --ip <remote-ip> --port 6009 --path /data/source_data/ --rendering-size 1920x1080 --load_images ```
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