LXD basic usage note


LXD is an extended version of LXC (LinuX Container). LXD prvoides REST API and easy-to-use interface to control containers. LXD is written by go and uses LXC share library (liblxc) directly. LXD can provoide live migration with criu (LXC can not).

The major purpose of LXD/ LXC is to provide VM-like environment. You can control container just like you control VM before. Unlike Docker, you can use systemd in the lxd container. LXD also provides well-defined networking interface.

Install lxd

I test the lxd in ubuntu 16.04 and 16.10. The versions of ubuntu 16.04 and 16.10 are 2.0.7 and 2.4.0. The latest version (2017/03/06) is 2.10. After LXD 2.3.0, it has new commands lxd network. You can get more details from the article. If you have need to use lxd network command, you can build lxd by yourself. It's not difficult to build. Otherwise you can just use package from package manager.

You can install lxc and lxc both since the two command line interfaces are independent. The commands of LXD are lxc and lxd. The commands of LXD is started wtih lxc-. It's uneeded to use them in the same time.

Install from package

  • In ubuntu 16.04 and 16.10
    sudo apt install -y lxd
    # sudo apt install -y criu  # Support live migration.
    sudo lxd init

Install from source

  • Environment: Ubuntu 16.04.2
  • Ref: LXD - build from source

  • Assume these environment variables and add to .bashrc or .zshrc

    export GOPATH=$HOME/usr/go
    export PATH=$PATH:$GOPATH/bin
    #export LXD_DIR=/home/yen3/usr/lxd/server
    #export LXD_CONF=/home/yen3/usr/lxd/config
  • Install prerequesties

    sudo apt-get install software-properties-common
    sudo add-apt-repository ppa:ubuntu-lxc/lxd-git-master
    sudo apt-get update
    sudo apt-get install acl dnsmasq-base git golang liblxc1 \
        lxc-dev make pkg-config rsync squashfs-tools \
        tar xz-utils
  • Build from source

    mkdir -p $GOPATH
    go get github.com/lxc/lxd
    cd $GOPATH/src/github.com/lxc/lxd
  • Save the script to /etc/init.d/launch-my-lxd

    # Provides:          launch-my-lxd
    # Required-Start:    $all
    # Required-Stop:     $all
    # Default-Start:     2 3 4 5
    # Default-Stop:      0 1 6
    # Short-Description: Start my lxd
    # Description:       Start the latest lxd build by myself
    set -e
    export GOPATH=/home/yen3/usr/go
    #export LXD_DIR=/home/yen3/usr/lxd/server
    #export LXD_CONF=/home/yen3/usr/lxd/config
    case "$1" in
          $GOPATH/bin/lxd --group lxd &
          /etc/init.d/launch-my-lxd stop
          /etc/init.d/launch-my-lxd start
          killall lxd
      *) echo "Usage: $0 {start|stop|restart}" >&2; exit 1 ;;
    exit 0
  • Start the lxd daemon

    /etc/init.d/launch-my-lxd start
  • Init the lxd

    $GOPATH/bin/lxd init
  • Check the lxd dameon executing successfully.

    $ lxc list
    $ lxc --version
  • (Optional) Lauch the lxd daemon after reboot automatically.

    sudo systemctl enable launch-my-lxd
  • (Memo) Stop the lxd daemon

    sudo systemctl stop launch-my-lxd


  • Command memo
    sudo lxd init                          # Init LXD environment
    lxc profile show default               # Show default profile
                                           # The current content of default profile is to define a network adapter
    lxc launch ubuntu:16.04 yen3-ubuntu    # Init and start the container named `yen3-ubuntu`.
    # lxc launch ubuntu:16.04 yen3-ubuntu -p default -p yen3
                                           # As the bellow but with `default` and `yen3` two profiles.
    lxc init ubuntu:16.04 yen3-ubuntu      # Init the container but not start
    lxc list                               # List the status of all containers.
    lxc start yen3-ubuntu                  # Start the container
    lxc stop yen3-ubuntu                   # Shutdown the container
    lxc stop yen3-ubuntu --stateful        # Stop the container and save the state (need to `sudo apt install -y criu`).
                                           # The function maybe not work.
    lxc restart yen3-ubuntu                # Restart the container
    lxc delete yen3-ubuntu                 # Delete the container
    lxc file push source.file target.file  # Copy host's file to the container
    lxc exec yen3-ubuntu -- apt update     # Run command under the container directly
    lxc info yen3-ubuntu                   # Show the container info
    lxc config show yen3-ubuntu            # Show config
    lxc config edit yen3-ubuntu            # Edit config
    lxc config show default                # Show default config.
    lxc config device add yen3-ubuntu homedir disk source=/home/yen3 path=/home/ubuntu
                                           # Share host's folder to container
    # The configuation document: https://github.com/lxc/lxd/blob/master/doc/configuration.md


Before use the lxd, I only have a little knowledge about network setting. If you are very familar with network setting, you can ignore the section. After LXD 2.3, LXD provoides lxd network command, you can read the article to get more details. I would not discuss how to use the command here. I just learn and write the basic usage.

The default networking setting of LXD container is bridge mode. Beside the mode, LXD provides physical, vlan and macvlan modes.

  • bridge (default): LXD create a NIC and connect the bridge (the bridge is created in sudo lxd init. It also run dnsmasq to prvoid DHCP service.)
  • physical: Use the host NIC directly.
  • vlan: I have no idea how to use vlan Xd.
  • macvlan: Simulate a different mac address based on a host NIC. The NIC conntects to host directly.

I take examples to use physical and macvlan.

  • physical mode

    • Assume the container's name is c1 and the enp0s8 NIC is unused in the host.
    • Add a NIC named eth1 to use enp0s8 and restart it.

      lxc launch ubuntu:16.04 c1
      lxc config device add c1 eth1 nic nictype=physical parent=enp0s8
      lxc restart c1
    • Exec lxc exec 1 -- ip addr to check the setting is successfully.

      3: eth1: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc pfifo_fast state UP group default qlen 1000
    • (Optional) Assume the outer network setting is DHCP, I just set eth1 with DHCP and restart networking service.

      • Add the code segement to /etc/network/interface

        auto eth1
        iface eth1 inet dhcp
      • Restart the networking service and check the NIC setting.

        sudo systemctl restart networking
        ip addr
  • macvlan mode

    • I setup a VirtualBox VM to practice the mode.
    • The NIC has to allow all connection or it is not valid after setting.


    • Assume the container's name is c1 and the enp0s3 is in the host.
    • Add a macvlan NIC for the container based on enp0s3

      lxc launch ubuntu:16.04 c1
      lxc config device add c1 eth1 nic nictype=macvlan parent=enp0s3
      lxc start c1
    • (Optional) We can also set the NIC with DHCP as the below to check the status of NIC.

      16: eth1@if3: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc noqueue state UP group default qlen 1000
      link/ether 00:16:3e:4f:38:d1 brd ff:ff:ff:  ff:ff:ff link-netnsid 0
      inet brd scope global eth1
         valid_lft forever preferred_lft forever
      inet6 fe80::216:3eff:fe4f:38d1/64 scope link
         valid_lft forever preferred_lft forever


local (unix socket)

  • API spec: rest_api.md
  • Get container list
    $ curl --unix-socket /var/lib/lxd/unix.socket \
        -H "Content-Type: application/json" \
        -X GET \
    # Pretty print with `python -m json.tool`
    $ curl --unix-socket /var/lib/lxd/unix.socket \
        -H "Content-Type: application/json" \
        -X GET \
        lxd/1.0/containers | python3 -m json.tool
      % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                     Dload  Upload   Total   Spent    Left  Speed
    100   197  100   197    0     0   9244      0 --:--:-- --:--:-- --:--:--  9380
        "type": "sync",
        "status": "Success",
        "status_code": 200,
        "operation": "",
        "error_code": 0,
        "error": "",
        "metadata": [

TCP (https)

  • Ref: Directly interacting with the LXD API
  • Run these command first time

    $ sudo apt install -y jq
    $ lxc config set core.trust_password <some_password>
    $ curl -s -k --cert ~/.config/lxc/client.crt --key ~/.config/lxc/client.key -X POST -d '{"type": "client", "password": "some_password"}'
    $ curl -s -k --cert ~/.config/lxc/client.crt --key ~/.config/lxc/client.key | jq .metadata.auth
  • List all containers

    $ curl -s -k --cert ~/.config/lxc/client.crt --key ~/.config/lxc/client.key | jq .
      "type": "sync",
      "status": "Success",
      "status_code": 200,
      "operation": "",
      "error_code": 0,
      "error": "",
      "metadata": [


Run aarch64 program through qemu

這篇的目的是利用 qemu & gdb 觀看 aarch64 program 的行為。

最近會有一直看 & 執行 assembly code 的需求,但是也不是很想一直看 x86-64 的 assembly code XD。剛好最近在 aarch64 的東西,索性以 aarch64 為目標。

aarch64 的 compiler 並不難找,可以使用 Linaro GCC,以後如果沒有什麼意外應該 會自己 build 一個 FSF GCC,以後如果有機會看 GCC 內部行為的時候會比較方便。

aarch64 的 qemu 的話,其實我對 qemu 不熟,我在 Ubuntu 16.04 用 sudo apt search qemu 好像找不到 aarch64 的支援,我也不是很確定,但是自己 build 一個 qemu 來用並不困難,所以我選擇自己 build 一個 qemu。

Build qemu for arm and aarch64

mkdir -p /home/yen3/tools/qemu/src
cd /home/yen3/tools/qemu/src
wget http://wiki.qemu-project.org/download/qemu-2.8.0.tar.bz2
tar xf qemu-2.8.0.tar.bz2
mkdir -p build_qemu
cd build_qemu
../qemu-2.8.0/configure --prefix=$HOME/tools/qemu  \
make install

Observe aarch64 through gdb and qemu

  1. 環境說明
    • 假設 aarch64 compiler 在 /home/yen3/tools/xc/gcc621/bin/aarch64-linux-gnu-gcc
    • 假設 qemu 在 /home/yen3/tools/qemu/bin/qemu-aarch64
  2. 編譯 main.c 變成 main.out (for aarch64)

    /home/yen3/tools/xc/gcc621/bin/aarch64-linux-gnu-gcc -g -O0 main.c -o main.out
  3. 利用 qemu 執行該程式 - 這邊需注意一點,需要透過 -L 設定 sysroot,如果是 抓 linaro gcc 的話,記得連 sysroot 的 tarball 也一並下載,如果是自己編的 話,應該會知道 sysroot 在那裡。

    /home/yen3/tools/qemu/bin/qemu-aarch64 \
      -L /home/yen3/tools/xc/gcc621 \
      -g 1234 \
  4. 利用 aarch64-linux-gnu-gdb 連到 gdb server 上

    /home/yen3/tools/xc/gcc621/bin/aarch64-linux-gnu-gdb -x test.gdb
    • test.gdb 內容如下
      target remote localhost:1234
      file ./main.out
      tui enable
      layout split
      b main
      set sysroot /home/yen3/tools/xc/gcc630/aarch64-linux-gnu

2017/01/26 Update

這幾天嘗試自己 build 了一個 debuggable aarch64-linux-gcc 出來,我把全部的 指令寫成一個 Makefile。

CC       = /home/yen3/tools/xc/gcc630/bin/aarch64-linux-gnu-gcc
GDB      = /home/yen3/tools/xc/gcc621/bin/aarch64-linux-gnu-gdb
QEMU_BIN = /home/yen3/tools/qemu/bin/qemu-aarch64
SYSROOT  = /home/yen3/tools/xc/gcc630/aarch64-linux-gnu

CFLAGS   = -ggdb -O0 -fno-builtin-printf
SRC      = main.c
BIN      = main.out

        $(CC) $(CFLAGS) ${SRC} -o ${BIN}
        QEMU_LD_PREFIX=$(SYSROOT) $(QEMU_BIN) -g 1234 ./${BIN} &
        $(GDB) -x test.gdb
        rm ${BIN}

也順便把 test.gdb 更新了一下,直接更新在上面。

不過忘了 build gdb,也無妨,現在是用 linaro gcc 的 gdb,過幾天有空再自己 build 一個。

A script to help tracing gcc source code

最近有 trace gcc source code 的需求。目前會用兩種方式來 trace gcc

  1. vim + ctags + cscope
  2. GCC Woboq Code Browser

但是直接用 ctags & cscope 生 tags & cscope file 會過大且對 trace code 沒有多大幫助,有太多雜訊,自己寫了一個簡單的 script 只對自己有興趣的檔案生 tags。




Instasll agda in Mac OSX


brew install ghc cabal-install
cabal install happy
cabal install alex
cabal install agda

A primer's note for parallel programming in Haskell

  • Functional Thursday #33
  • 2015.12.03
  • Yen3 (yen3rc@gmail.com)

About the slide

  • 這份投影片是 Parallel and Concurrent Programming in Haskell - Chatper 1 ~ 5 的筆記

  • 主題包含

    • Eval monad
    • Par monad
    • Repa
  • 今天的內容皆與 data-level parallelism 相關

  • 在 Haskell 中,一個較容易平行化 function 是 map,這份投影片會很常 討論到它。

Definition of Parallel

  • A parallel program is one that uses a multiplicity of computational hardware (e.g., several processor cores) to perform a computation more quickly.
  • Parallel programming in Haskell is deterministic: The parallel program always produces the same answer, regardless of how many processors are used to run it.

The status of value in ghc (1/)

  • There are three conditions of a value.
    • Unevaluated
    • Weak-Head Normal Form (WHNF) - evaluated with first constructor
    • Normal Form (NF) - fully evaluated

The status of value in ghc (2/)

  • sprint - prints a value without forcing its evaluation
  • seq: only far as the first constructor and doesn't evaluate any more of the structure. It evaluates first argument to WHNF.
seq :: a -> b -> b

The status of value in ghc (3/)

  • Example
Prelude> let x = 1 + 2 :: Int
Prelude> let y = x + 1
Prelude> :sprint x
x = _
Prelude> :sprint y
y = _
Prelude> seq y ()
Prelude> :sprint x
x = 3
Prelude> :sprint y
y = 4

The status of value in ghc (4/)

Prelude> let xs = map (+1) [1..10] :: [Int]
Prelude> :sprint xs
xs = _
Prelude> seq xs ()
Prelude> :sprint xs
xs = _ : _
Prelude> length xs
Prelude> :sprint xs
xs = [_,_,_,_,_,_,_,_,_,_]
Prelude> sum xs
Prelude> :sprint xs
xs = [2,3,4,5,6,7,8,9,10,11]

force function

  • force - fully evaluated it's argument and returns it. (WHNF into NF)
import Control.DeepSeq

class NFData a where
    rnf :: a -> ()      -- reduce to normal-form
    rnf a = a `seq` ()

deepseq :: NFData a => a -> b -> b
deepseq a b = rnf a `seq` b 

force :: NFData a => a -> a   
force x = x `deepseq` x
  • seq: only far as the first constructor and doesn't evaluate any more of the structure. It evaluates first argument to WHNF.

Eval monad

  • Decoupling of the algorithm from the parallelism
  • The type declaration for eval monad
data Eval a
instance Monad Eval

runEval :: Eval a -> a
rpar :: a -> Eval a   -- rpar :: Strategy a 
rseq :: a -> Eval a   -- rseq :: Strategy a
  • rpar - evaluate its argument in parallel.
  • rseq - Evaluate the argument and wait for the result.
    • evaluates its argument to WHNF.

Eval monad - simple example

  • Example
runEval $ do
    a <- rpar (f x)
    b <- rseq (f y)
    rseq a
    return (a, b)


Eval monad - Strategy

  • Strategy - modularize parllel code by separating the algorithm from the parallelism
    • use using function to add parallelism with the existing codes
    • withStrategy- a another version of using with the arguments flipped
type Strategy a = a -> Eval a

using :: a -> Strategy a -> a
x `using` s = runEval (s x)

withStrategy :: Strategy a -> a -> a
withStrategy s x = runEval (s x)

Eval monad - Strategy

  • The rpar, rseq are also Strategies.
rpar :: Strategy a
rseq :: Strategy a
  • You could write the algorithm first and add the parallelism code later ideally.

Eval monad - example for pair

  • Example
import Control.Parallel.Strategies
import Control.DeepSeq

evalPair :: Strategy a -> Strategy b -> Strategy (a,b)
evalPair sa sb (a,b) = do
    a' <- sa a
    b' <- sb b
    return (a',b')

Eval monad - example for pair

rparWith :: Strategy a -> Strategy a
rparWith s a =
        ra <- rpar a
        sa <- s ra
        return sa 

(+) 1 2             -- (1-1)  
((+) 1 2, (+) 3 4)  -- (1-2)

(+) 1 2 `using` rpar   -- (2-1)
<!--((+) 1 2, (+) 3 4) `using` evalPair (rparWith rseq) (rparWith rseq)  -- (2-2)-->
((+) 1 2, (+) 3 4) `using` evalPair (rparWith rseq) (rparWith rseq)  -- (2-2)
  • (1-1), (1-2) - sequential version
  • (2-1), (2-2) - parallel version and reduce the value to WHNF
wzxhzdk:10 - (3-1), (3-2) - parallel version and reduce the value to NF - `parTuple2` and `evalPair` functions are the same -->

Eval monad - some help functions (1/)

  • About some helper function
    • rdeepseq - evaluates the argument to NF
rdeepseq :: NFData a => Strategy a
rdeepseq x = rseq (force x)
- `rparWith` - wraps the Strategy s in an `rpar`
rparWith :: Strategy a -> Strategy a 

Eval monad - some help functions (2/)

  • The code reduced to NF in previous slide could also be written as
-- NF 
(+) 1 2 `using` rparWith rdeepseq 
((+) 1 2, (+) 3 4) `using`
    evalPair (rparWith rdeepseq) (rparWith rdeepseq)

Eval monad - parallelize map

parMap :: (a -> b) -> [a] -> [b]
parMap f xs = map f x `using` parList rseq 

evalList :: Strategy a -> Strategy [a]
evalList start [] = return []
evalList start (x:xs) = do
    x' <- start x
    xs' <- evalList start xs
    return (x': xs')

parList :: Strategy a -> Strategy [a]
parList start = evalList (rparWith start)
  • parMap will calculate its list to WHNF
  • parList - evaluate the list element in parallel

Eval monad

  • Example
import Control.Parallel.Strategies
import Control.DeepSeq

map (+1) [1..100]  -- (1) 
map (+1) [1..100] `using` parList rseq -- (2)
map (+1) [1..100] `using` parList rdeepseq  -- (3)
  • (1) sequential version
  • (2) parallelize version reduce value to WHNF
  • (3) parallelize version reduce value to NF

Example - Mandelbrot set

  • You could get more details from my blog post.

  • some type define

type Point = (Double, Double)
type Range = (Double, Double)
type Plane = (Range, Range)
  • sequential version
planePoints :: Plane -> V.Vector Point

mandelSet :: Plane -> V.Vector Point
mandelSet = planeToMandelPoints

Example - Mandelbrot set

  • basic parallel version with parList
splitPlane :: Integer -> Plane -> [Plane]

mandelSetStart :: Integer -> Plane -> V.Vector Point
mandelSetStart size p = V.concat
    (map planeToMandelPoints (splitPlane size p)
     `using` parList rseq)
  • In 2010 late MBP15 (Intel Core i5 2.4 Ghz, 8Gb)
    • sequential - about 45 secs
    • run in 2 cores - about 25 secs (./Mandelbrot par 100 +RTS -N2 -s)

Par Monad

  • Goal
    • be more explicit about granularity and data dependences
    • Avoid the reliance on lazy evaluation, but without sacrificing the determinism that we value for parallel programming.
    • The parallel computations are pure (and deterministic)
  • The library is implemented entirely as a Haskell library
    • You can accommodate alternative scheduling strategies.

Par Monad

  • Par monad - a monad for parallel computation
newtype Par a

instance Applicative Par
instance Monad Par

runPar :: Par a -> a    -- produce a pure result.
fork :: Par () -> Par () -- the way to create parallel tasks
  • IVar - results are communicated through IVars
data IVar a 

new :: Par (IVar a)
put :: NFData a => IVar a -> a -> Par ()
get :: IVar a -> Par a

Par Monad

  • IVar
data IVar a 

new :: Par (IVar a)
put :: NFData a => IVar a -> a -> Par ()
get :: IVar a -> Par a
  • IVar -- as a box that stars empty
  • put -- store a value in the box
    • All values communicated through IVars are fully evaluated. There is a head-strict version put_
  • get -- read the value. If the box is empty, it waits until the box is filled by put. The get operation does not remove the value from the box. Once the box is full. It stays the state constantly.

Par Monad

  • Example
runPar $ do
    i <- new
    j <- new
    fork (put i (fib n))
    fork (put j (fib m))
    a <- get i
    b <- get j
    return (a+b)


Par Monad

  • spawn - Like fork, but returns a IVar that can be used to query the result of the forked computation. Therefore spawn provides futures or promises.
  • parMap - parallel version map implemented with par monad
spawn :: NFData a => Par a -> Par (IVar a)
spawn p = do
    i <- new
    fork (do x <- p; put i x)
    return i

parMap :: NFData b => (a -> b) -> [a] -> Par [b]
parMap f as = do
    ibs <- mapM (spawn . return . f) as
    mapM get ibs

Example - prime number

  • Example
    • primeIntVector - Eval monad
    • primeIntVector' - Par monad
primeIntVector :: Int -> VU.Vector Int
primeIntVector n =
        ls = genNumberRange 0 n 100
        VU.concat (map (uncurry primes) ls `using` parList rseq)

primeIntVector' :: Int -> VU.Vector Int
primeIntVector' n =
        ls = genNumberRange 0 n 100
        VU.concat $ Par.runPar $ Par.parMap (uncurry primes) ls

Difference between Par and Eval

  • Par Monad
    1. Always evaluate its value to normal form. It avoids the problem about the weak-normal form
    2. The cost of calling runPar function is bigger then runEval
    3. Easy to redefine the scheduling strategy

Difference between Par and Eval

  • Eval Monad

    1. Need use force function to evaluate its value from weak-head normal form to normal form. It’s suitable for lazy data structure
    2. The cost of calling runEval function is free
    3. Provide the speculative parallelism
    4. Eval Monad has more diagnostics in ThreadScope compared Par Monad.
  • Reference


  • Repa - REgular PArallel arrays
  • Goal
    • efficient numerical array computations in Haskell and run them in parallel
  • It could provides efficient unboxed data computation, but not Par monad and Strategy monad
    • Repa also support boxed data.

Repa - type

  • The array type
data Array r sh e
  • r - representation type
  • e - element type
  • sh - the shape of array (the dimension(s) of array)
data Z = Z    -- scalar data
data tail :. head = tail :. head

type DIM0 = Z
type DIM1 = DIM0 :. Int
type DIM2 = DIM1 :. Int

Repa - array

  • how to create an array with Array type ?
    • fromListUnboxed - from list of unboxed type
    • fromUnboxed - from the vector with Data.Vector.Unboxed type
    • fromFunction - from the shape information to generate the array
    • ... etc
fromListUnboxed :: (Shape sh, Unbox a) => sh -> [a] -> Array U sh a
fromFunction :: sh -> (sh -> a) -> Array D sh a
fromUnboxed :: (Shape sh, Data.Vector.Unboxed e) :: sh -> e -> Array U sh e

Repa - create array example

  • Example - create an array
Prelude> import Data.Array.Repa as R
Prelude R> let a = R.fromListUnboxed (Z :. 10) [1..10] :: Array U DIM1 Int
Prelude R> :t a 
a :: Array U DIM1 Int

Prelude R> let b =  R.fromFunction (Z :. 10) (\(Z :. i) -> i + 1 :: Int)
Prelude R> :t b
b :: Array D (Z :. Int) Int

Prelude R > import qualified Data.Vector.Unboxed as VU
Prelude R VU> let v = VU.enumFromN 1 10 :: VU.Vector Int
Prelude R VU> let c = R.fromUnboxed (Z :. (VU.length v)) v
Prelude R VU> :t c
c :: Array U (Z :. Int) Int

Repa - array computation

  • All array will transfer to a delayed array type (ex: Array D sh e) after array computations (ex: Repa.map)
Repa.map :: (Shape sh, Source r a) =>
     (a -> b) -> Array r sh a -> Array D sh b

(+^) :: (Num c, Shape sh, Source r1 c, Source r2 c) =>
     Array r1 sh c -> Array r2 sh c -> Array D sh c

Repa - compute

  • computeS - calculate the array operations in sequentially.
  • computeP - the same as computeS but in parallel.
    • the purpose of the monad is only to ensure that computeP operations are performed in sequence and not nested.
      • the simplest way to reduce the monad effect -- runIdentity
      • See page p.94 to get more information
computeS :: (Load r1 sh e, Target r2 e) => Array r1 sh e -> Array r2 sh e
computeP :: (Monad m, Source r2 e, Target r2 e, Load r1 sh e) =>
    Array r1 sh e -> m (Array r2 sh e)

Repa - array computation example

  • calculate $e^x = \sum^{\infty}_{n=0}\frac{x^n}{n!} \forall x$
import Data.Array.Repa as R
import Control.Monad.Identity

fact x = foldr (*) 1 [1..x]

enumN :: Int -> Array D DIM1 Double
enumN n = R.fromFunction (Z :. n) (\(Z :. i) -> fromIntegral i)  

exp' :: Int -> Double
exp' x = let
             ns = enumN 100
             ys = R.map (\n -> (((fromIntegral x)**n) / (fact n)))
             runIdentity . R.sumAllP $ ys
wzxhzdk:32 -->

Repa - example

  • Example - prime numbers
primeArray :: Int -> VU.Vector Int
primeArray n = let
                   a = genArray n
                   ps = runIdentity . Repa.computeUnboxedP . primeArrayCheck $
                        a :: Array U DIM1 Int
                   VU.filter (/=0) (Repa.toUnboxed ps)


  • The simplest parallel method - parallel map

    • use parMap or parList
  • Repa is useful especially for numeric calculation.

  • The remaining part of the book is about.

  • Bool unbxoed type ?

murmur (10) - mkd & LaTeX

其實這個 blog 壞掉很久了,因為 nikola 更新之後會強制把整個 output folder 重刷,我之前在上面硬幹用 git 上傳到 github 的方式就不能用了,因為 nikola 已經提供了 nikola github_deploy 的指令,但是暫時沒有東西想寫所以也不理它,今天突然想寫點廢話的時候 ... 覺得還是要修一修了 XD 照著 nikola handbook 的說明,倒也是很快就修好了,而且這個方式也比我之前用的好的多,也可以利用 github 備份整個 source,算是很方便的方式,這樣子我也不用研究 Travis CI 了 XD。

近一兩年來大部分寫筆記的方式都是使用 markdown (mkd),但是最近應該會重建 LaTeX 的寫作環境,專門拿來做筆記用 (其實我也沒有多少筆記要做 XD),倒也不是說 mkd 不好,而是自己的龜毛病發作 XD。我自己數學不好,所以其實也沒有多少數學式要寫,用 LaTeX 單純只是圖一個精準而己。寫 mkd 的時候,每個軟體 render 出來的結果不盡相同 (試試 subitem 配合 code block),暫時解法是以 MacDown 的顯示為基準。目前的想法是速記還是以 mkd 為主,如果要寫長一點的筆記還是會回歸到 LaTeX (XeLaTeX) 上。

2015 年的 LaTeX 中文處理使用 xeCJK 處理起來應該都不會有太大的問題。在 Mac OS X 上的 LaTeX 編輯環境 TeXShop 仍是第一首選。不過因為愛用 vim 的緣故,參考 XOO 的 blog 設定 (OS X 的 LATEX 寫作環境, OS X 上自動編譯 LATEX 與自動更新),使用上亦相當順暢。

今天也利用空閒時間小小的修改 TeXShop 的 article 及 beamer template,主要是加上 xeCJK support 及 minted package 的 syntax highlighting (終於不是 verbatim 了~!)。重新開始的原因是之前的版面設定檔案隨著硬碟洗掉而消失了,放在自己的 github repo 上也算是幫自己做備份。

看看自己可以撐多久 XD

用了 Kindle Paperwhite 2015 數日感想

  1. 如果你有看大量網頁長文章的需求 (e.g. wiki or 教學文件),可以利用 send to kindle 這個 chrome plugin 來閱讀。在不考慮排版的情況下,KPW 會比螢幕舒服很多很多。我在 iOS 上是透過第三方軟體達成把看到的網頁經由 send to kindle 這條路送到 kindle 上 XD。
  2. 中文雜書還是很少,不過基本上我也不太看 XD 目前是以好讀網站 (比較老版權問題不大的書) 及潑墨書坊為主。如果對簡體中文不排斥的話,其實對岸會有很多嘿嘿嘿的 epub 可以使用 kindlegen 轉成 mobi 在 KPW 上閱讀的,對岸還有多看軟體可以使用,不過我暫時還沒有去嘗試 XD。
  3. 英文的雜書的話,基本上是一個練習英文很好的載體,不過不能支持有聲書,但是如果是在 amazon 上買的話,可以用 android or iphone 進行同步。一開始看英文雜書誠心建議從短篇看起。如何利用 KPW 幫你看英文,這其實需要花很多時間說明,在此略過。
  4. 英文電腦書,如果買的是正版英文電腦書,基本上都會有 pdf, epub 及 mobi 可供選擇,如果要循序讀的時候,用 kindle 讀 mobi 是最佳的選擇之一。如果想要搜尋或跳著讀的時候,使用 iPad 或電腦是不錯的選擇。我曾經有考慮過 SONY DPT-S1,但是價格太貴,目前買不起。
  5. 對於 pdf 的話,如果是滿版 A4 的 pdf 的話,用 KPW 的橫向模式還是太小了 (btw 用 KPW 的橫向看東西有在打電動的感覺 XD),雙欄論文的話,基本上局部放大是勉強可看的,如果你有很多 pdf 的話,建議還是留一台 iPad 或者是買一台 SONY DPT-S1 在身上 (或者是心一橫直接印出來) 會比較好。
  6. KPW 其實是一個很輕很小的載體 (6 吋,204 克),一本英文小說大約為 105 g 左右,一本中文雜書大抵是 200 ~ 300 g 左右,英文工具書就更不用說了 XD,其實 KPW 會比很多書來的更好攜帶,只是閱讀習慣需要做出適度的改變。

murmur (9) - Just for fun - Add from 0 to n in parallel

I stuided the repa package today. I discover it supports parallel computation for both boxed type and unboxed type rather then only for unboxed type. It reminds me that I have to read the manual carefully.

Repa supports doing sum computation in parallel (see sumP in repa doc). I just write a parallel sum function for fun.

import Data.Array.Repa as Repa
import qualified Data.Vector.Unboxed as VU
import Control.Monad.Identity

sumR :: Int -> Double
sumR n = let
            xs = VU.enumFromN 0 n
            runIdentity (Repa.sumAllP (Repa.fromUnboxed (Z :. VU.length xs) xs))

main = putStrLn $ show . sumR $ 100000000

Tomorrow's goal: Accelerate package.

[Haskell Practice] Cairo package

That's I tried to use cairo package to draw something.

The picture is drawed by the l-system formula. You can get more details from the wiki page L-system: Example 4: Koch curve.

The souce code is not released until I am satisfied with what I write XD.

Practice for L-system

Update: add practice for drgaon curve

Practice for dragon curve

murmur (8) - Keep going

No news is good news. That's what gwchen told me. How about me in the recent life ? Just back to the begining for learning something.

I have left my last job for four monthes. In these days, I have

  • Learned
    • basic Haskell programming (including basic Monad)
    • basic parallel and concurrent programming
    • basic linux system programming through reading TLPI
  • Practiced English
    • Watch CNN Student news for one and half month
    • Read/ Listen several Time articles in one month

What's the next step ? I don't know. The only thing I know is that I will following my mind.