Saturday, 4 March 2017

Links I want to maintain

Nice Motivational Video

link to article on how to print string using assembly : 

Friday, 17 February 2017

Creating loop device on fedora

We can create a loop device in Linux which we can use as a virtual device for experiment. Following steps will show how to create loop device and use it for creation of file system. 

  • Add new device
 # losetup -f

  • Create a file which will serve as virtual device.

# dd if=/dev/urandom of=/bhushan bs=1M count=128
128+0 records in
128+0 records out
134217728 bytes (134 MB, 128 MiB) copied, 12.9207 s, 10.4 MB/s

  • Setup up the mapping between loop device and new file 

# losetup /dev/loop0 /bhushan

  • Use new virtual device for file system creation

# mkfs -t ext4 /dev/loop0
mke2fs 1.43.3 (04-Sep-2016)
Discarding device blocks: done                          
Creating filesystem with 131072 1k blocks and 32768 inodes
Filesystem UUID: 7be2cbaf-a2a5-4319-a7e9-51b749c69184
Superblock backups stored on blocks:
8193, 24577, 40961, 57345, 73729

Allocating group tables: done                          
Writing inode tables: done                          
Creating journal (4096 blocks): done
Writing superblocks and filesystem accounting information: done

# mount -t ext4 /dev/loop0 /mnt

# df -h
Filesystem               Size  Used Avail Use% Mounted on
/dev/loop0               120M  1.6M  110M   2% /mnt1

# umount /mnt1

  • Detach a file from loop device

# losetup -d /dev/loop0

Tuesday, 31 January 2017

.config changes for KGDB

Change following parameter for custom kernel development during KGDB setup :

CONFIG_DEBUG_RODATA=n => kernel hacking => write protect kernel read only data strucuture

Reference :

Convert Fedora 25 login from command line to GUI

#dnf install @gnome-desktop
#dnf distro-sync
#dnf groupinstall gnome
#dnf groupinstall x-base
#systemctl start gdm
#systemctl set-default

Saturday, 31 December 2016

Assignment on 32 bit and 64 bit components in Processor to Application stack

Due to back word compatibility with 32 bit component like OS , application is allowed to run on 64 bit component, but vice versa is not allowed.

Inner component can be 32 or 64 bit if outer is 32
But inner component must be 64 if outer is 64 bit
Any level of miss match will result in problem.

Not applicable for device driver. Cannot run 32 bit driver on 64 bit machine (as per backward compatibility rule).

How to check whether binary is 32bit or 64 bit?

# file <filename>
[root@localhost driver]# file a.out
a.out: ELF 32-bit LSB executable, Intel 80386, version 1 (SYSV), dynamically linked (uses shared libs), for GNU/Linux 2.6.32, BuildID[sha1]=740131a4dacce9424b1db8fc53406040406d41bd, not stripped

How to check Whether OS  is 32 or 64 bit?

#uname –a or uname -m
[root@localhost ~]$ uname -a
Linux localhost.localdomain 3.15.10-201.fc20.i686 #1 SMP Wed Aug 27 21:33:30 UTC 2014 i686 i686 i386 GNU/Linux

[root@localhost ~]$ uname -m
Its 32 bit. If you have a 64-bit OS, instead of i686, you have x86_64 or ia64 in the output of uname –a.

Check machine is 32 or 64 bit:

Use grep flags /proc/cpuinfo or lscpu

[root@localhost ~]$ grep flags /proc/cpuinfo
flags                       : fpu vme de pse tsc msr mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 rdtscp constant_tsc pni monitor ssse3

If this content lm flag then it is 64 bit.

 [root@localhost ~]$ lscpu
Architecture:          i686

CPU op-mode(s):        32-bit

Friday, 30 December 2016

Machine Learning - Basics and classification

  • Machine Learning is giving ability to machines to learn/program on it own.
  • Machine learning is not restricted to Artificial Intelligence. It is having many applications in various filed, Recommendations System raging from data mining, rating system to automation of house hold activities.
  • Machine is said to be 'learning', if its performance improves with experience. 

Classification of Machine Learning (ML):
  1. Supervised ML.
  2. Unsupervised ML.
1. Supervised ML
  • Known Sample data sets are given. (Test Passed / Failed )
  • Regression : Continuous value .
  • Classification : Discrete value
2. Unsupervised ML
  • Data set don't have any label (like Passed /Failed)  
  • Just data set is given and algorithm will divide it in cluster.
  • As we are not specifying what data belong to what segment/ cluster its unsupervised learning.
  • We have to derive structure from unknown data.