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             LWN.NET WEEKLY EDITION FOR AUGUST 30, 2018            

  

  o Reference: 0000763252
  o News link: https://lwn.net/Articles/763252/
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    [1]Welcome  to  the  LWN.net Weekly Edition for August 30, 2018
    This edition contains the following feature content:
    
    [2]An  introduction  to the Julia language, part 1 : Julia is a
    language  designed  for  intensive numerical calculations; this
    article gives an overview of its core features.
    
    [3]C  considered  dangerous  :  a Linux Security Summit talk on
    what is being done to make the use of C in the kernel safer.
    
    [4]The  second  half  of  the  4.19  merge  window  : the final
    features  merged (or not merged) before the merge window closed
    for this cycle.
    
    [5]Measuring  (and fixing) I/O-controller throughput loss : the
    kernel's   I/O   controllers   can   provide  useful  bandwidth
    guarantees, but at a significant cost in throughput.
    
    [6]KDE's  onboarding initiative, one year later : what has gone
    right  in  KDE's  effort  to make it easier for contributors to
    join the project, and what remains to be done.
    
    [7]Sharing  and  archiving  data  sets with Dat : an innovative
    approach to addressing and sharing data on the net.
    
    This week's edition also includes these inner pages:
    
    [8]Brief   items   :  Brief  news  items  from  throughout  the
    community.
    
    [9]Announcements  : Newsletters, conferences, security updates,
    patches, and more.
    
    Please  enjoy  this  week's  edition, and, as always, thank you
    for supporting LWN.net.
    
    [10]Comments (none posted)
    
    [11]An introduction to the Julia language, part 1
    
    August 28, 2018
    
    This article was contributed by Lee Phillips
    
    [12]Julia  is  a  young  computer language aimed at serving the
    needs  of  scientists,  engineers,  and  other practitioners of
    numerically   intensive  programming.  It  was  first  publicly
    released   in   2012.  After  an  intense  period  of  language
    development,  version 1.0 was [13]released on August 8. The 1.0
    release  promises  years  of  language  stability; users can be
    confident  that  developments  in the 1.x series will not break
    their  code.  This  is  the  first  part  of a two-part article
    introducing  the  world  of  Julia.  This  part  will introduce
    enough  of  the  language syntax and constructs to allow you to
    begin  to write simple programs. The following installment will
    acquaint  you  with the additional pieces needed to create real
    projects, and to make use of Julia's ecosystem.
    
    Goals and history
    
    The  Julia  project  has ambitious goals. It wants the language
    to  perform  about  as  well  as  Fortran  or  C  when  running
    numerical  algorithms,  while  remaining as pleasant to program
    in  as Python. I believe the project has met these goals and is
    poised  to  see  increasing  adoption by numerical researchers,
    especially now that an official, stable release is available.
    
    The  Julia  project  maintains  a [14]micro-benchmark page that
    compares  its  numerical  performance  against  both statically
    compiled   languages   (C,   Fortran)   and  dynamically  typed
    languages  (R,  Python). While it's certainly possible to argue
    about  the relevance and fairness of particular benchmarks, the
    data  overall  supports  the Julia team's contention that Julia
    has   generally   achieved  parity  with  Fortran  and  C;  the
    benchmark source code is available.
    
    Julia  began  as  research  in  computer  science  at  MIT; its
    creators  are  Alan  Edelman,  Stefan Karpinski, Jeff Bezanson,
    and  Viral  Shah.  These  four  remain active developers of the
    language.  They, along with Keno Fischer, co-founder and CTO of
    [15]Julia  Computing , were kind enough to share their thoughts
    with  us  about the language. I'll be drawing on their comments
    later  on;  for now, let's get a taste of what Julia code looks
    like.
    
    Getting started
    
    To   explore   Julia   initially,   start   up   its   standard
    [16]read-eval-print   loop   (REPL)  by  typing  julia  at  the
    terminal,  assuming  that  you have installed it. You will then
    be  able  to  interact with what will seem to be an interpreted
    language  —  but,  behind  the scenes, those commands are being
    compiled  by  a  just-in-time  (JIT)  compiler  that  uses  the
    [17]LLVM   compiler   framework  .  This  allows  Julia  to  be
    interactive,  while  turning the code into fast, native machine
    instructions.   However,  the  JIT  compiler  passes  sometimes
    introduce  noticeable delays at the REPL, especially when using
    a function for the first time.
    
    To  run  a  Julia  program non-interactively, execute a command
    like: $ julia script.jl
    
    Julia  has  all  the  usual data structures: numbers of various
    types     (including    complex    and    rational    numbers),
    multidimensional    arrays,    dictionaries,    strings,    and
    characters.  Functions  are  first-class: they can be passed as
    arguments  to other functions, can be members of arrays, and so
    on.
    
    Julia  embraces  Unicode. Strings, which are enclosed in double
    quotes,  are  arrays  of Unicode characters, which are enclosed
    in  single  quotes.  The  " * " operator is used for string and
    character  concatenation.  Thus 'a' and 'β' are characters, and
    'aβ'  is  a syntax error. "a" and "β" are strings, as are "aβ",
    'a' * 'β', and "a" * "β" — all evaluate to the same string.
    
    Variable  and  function names can contain non-ASCII characters.
    This,   along  with  Julia's  clever  syntax  that  understands
    numbers  prepended  to variables to mean multiplication, goes a
    long  way  to  allowing  the  numerical scientist to write code
    that  more  closely resembles the compact mathematical notation
    of the equations that usually lie behind it.  julia ε₁ = 0.01
    
    0.01
    
    julia ε₂ = 0.02
    
    0.02
    
    julia 2ε₁ + 3ε₂
    
    0.08
    
    And  where  does  Julia come down on the age-old debate of what
    do  about  1/2  ? In Fortran and Python 2, this will get you 0,
    since  1  and 2 are integers, and the result is rounded down to
    the  integer  0. This was deemed inconsistent, and confusing to
    some,  so  it  was changed in Python 3 to return 0.5 — which is
    what you get in Julia, too.
    
    While  we're  on  the  subject  of  fractions, Julia can handle
    rational  numbers,  with  a special syntax: 3//5 + 2//3 returns
    19//15  ,  while  3/5  + 2/3 gets you the floating-point answer
    1.2666666666666666.  Internally,  Julia  thinks  of  a rational
    number  in  its  reduced  form,  so the expression 6//8 == 3//4
    returns true , and numerator(6//8) returns 3 .
    
    Arrays
    
    Arrays  are  enclosed  in  square  brackets and indexed with an
    iterator  that  can  contain a step value:  julia a = [1, 2, 3,
    4, 5, 6]
    
    6-element Array{Int64,1}:
    
    1
    
    2
    
    3
    
    4
    
    5
    
    6
    
    julia a[1:2:end]
    
    3-element Array{Int64,1}:
    
    1
    
    3
    
    5
    
    As  you  can  see,  indexing  starts at one, and the useful end
    index  means  the  obvious thing. When you define a variable in
    the  REPL,  Julia  replies  with  the  type  and  value  of the
    assigned  data;  you  can  suppress  this output by ending your
    input line with a semicolon.
    
    Since  arrays  are  such a vital part of numerical computation,
    and  Julia makes them easy to work with, we'll spend a bit more
    time with them than the other data structures.
    
    To  illustrate  the  syntax,  we  can start with a couple of 2D
    arrays, defined at the REPL:  julia a = [1 2 3; 4 5 6]
    
    2×3 Array{Int64,2}:
    
    1 2 3
    
    4 5 6
    
    julia z = [-1 -2 -3; -4 -5 -6];
    
    Indexing is as expected:  julia a[1, 2]
    
    2
    
    You can glue arrays together horizontally:  julia [a z]
    
    2×6 Array{Int64,2}:
    
    1 2 3 -1 -2 -3
    
    4 5 6 -4 -5 -6
    
    And vertically:  julia [a; z]
    
    4×3 Array{Int64,2}:
    
    1  2  3
    
    4  5  6
    
    -1 -2 -3
    
    -4 -5 -6
    
    Julia  has  all  the  usual  operators for handling arrays, and
    [18]linear  algebra  functions  that  work  with  matrices  (2D
    arrays).  The  linear  algebra  functions  are  part of Julia's
    standard  library,  but need to be imported with a command like
    "  using  LinearAlgebra  ",  which is a detail omitted from the
    current  documentation.  The  functions  include such things as
    determinants,  matrix  inverses,  eigenvalues and eigenvectors,
    many  kinds  of  matrix  factorizations,  etc.  Julia  has  not
    reinvented  the  wheel  here,  but  wisely  uses the [19]LAPACK
    Fortran library of battle-tested linear algebra routines.
    
    The  extension  of  arithmetic  operators  to arrays is usually
    intuitive:  julia a + z
    
    2×3 Array{Int64,2}:
    
    0 0 0
    
    0 0 0
    
    And  the  numerical  prepending  syntax works with arrays, too:
    julia 3a + 4z
    
    2×3 Array{Int64,2}:
    
    -1 -2 -3
    
    -4 -5 -6
    
    Putting  a  multiplication  operator  between two matrices gets
    you matrix multiplication:  julia a * transpose(a)
    
    2×2 Array{Int64,2}:
    
    14 32
    
    32 77
    
    You  can  "broadcast"  numbers  to cover all the elements in an
    array  by prepending the usual arithmetic operators with a dot:
    julia 1 .+ a
    
    2×3 Array{Int64,2}:
    
    2 3 4
    
    5 6 7
    
    Note  that the language only actually requires the dot for some
    operators,  but  not  for  others,  such  as  "*"  and "/". The
    reasons  for this are arcane, and it probably makes sense to be
    consistent  and  use  the dot whenever you intend broadcasting.
    Note   also   that   the   current   version  of  the  official
    documentation  is  incorrect  in claiming that you may omit the
    dot from "+" and "-"; in fact, this now gives an error.
    
    You  can  use  the  dot  notation to turn any function into one
    that   operates   on   each   element   of  an  array:    julia
    round.(sin.([0, π/2, π, 3π/2, 2π]))
    
    5-element Array{Float64,1}:
    
    0.0
    
    1.0
    
    0.0
    
    -1.0
    
    -0.0
    
    The  example  above  illustrates  chaining two dotted functions
    together.  The  Julia compiler turns expressions like this into
    "fused"  operations:  instead of applying each function in turn
    to  create a new array that is passed to the next function, the
    compiler   combines   the  functions  into  a  single  compound
    function  that  is  applied  once  over  the  array, creating a
    significant optimization.
    
    You  can  use  this  dot  notation with any function, including
    your  own, to turn it into a version that operates element-wise
    over arrays.
    
    Dictionaries  (associative  arrays) can be defined with several
    syntaxes. Here's one:  julia d1 = Dict("A"=1, "B"=2)
    
    Dict{String,Int64} with 2 entries:
    
    "B" = 2
    
    "A" = 1
    
    You  may  have  noticed  that the code snippets so far have not
    included  any  type  declarations.  Every  value in Julia has a
    type,  but  the  compiler  will  infer  types  if  they are not
    specified.  It  is generally not necessary to declare types for
    performance,   but  type  declarations  sometimes  serve  other
    purposes,  that  we'll  return  to  later. Julia has a deep and
    sophisticated  type  system,  including  user-defined types and
    C-like  structs. Types can have behaviors associated with them,
    and  can  inherit  behaviors  from  other types. The best thing
    about  Julia's  type system is that you can ignore it entirely,
    use  just  a  few  pieces  of  it,  or spend weeks studying its
    design.
    
    Control flow
    
    Julia  code  is organized in blocks, which can indicate control
    flow,  function  definitions,  and other code units. Blocks are
    terminated  with  the  end  keyword,  and  indentation  is  not
    significant.  Statements  are separated either with newlines or
    semicolons.
    
    Julia  has the typical control flow constructs; here is a while
    block:  julia i = 1;
    
    julia while i 5
    
    print(i)
    
    global i = i + 1
    
    end
    
    1234
    
    Notice  the  global  keyword.  Most blocks in Julia introduce a
    local  scope for variables; without this keyword here, we would
    get an error about an undefined variable.
    
    Julia  has  the  usual if statements and for loops that use the
    same  iterators that we introduced above for array indexing. We
    can  also  iterate  over collections:  julia for i ∈ ['a', 'b',
    'c']
    
    println(i)
    
    end
    
    a
    
    b
    
    c
    
    In  place of the fancy math symbol in this for loop, we can use
    "  =  "  or " in ". If you want to use the math symbol but have
    no  convenient  way  to type it, the REPL will help you: type "
    \in  "  and  the  TAB key, and the symbol appears; you can type
    many [20]LaTeX expressions into the REPL in this way.
    
    Development of Julia
    
    The   language   is   developed   on   GitHub,  with  over  700
    contributors.  The  Julia  team  mentioned in their email to us
    that  the decision to use GitHub has been particularly good for
    Julia,  as  it  streamlined  the  process  for  many  of  their
    contributors,  who  are scientists or domain experts in various
    fields, rather than professional software developers.
    
    The  creators  of  Julia  have  [21]published  [PDF] a detailed
    “mission  statement”  for  the  language, describing their aims
    and  motivations.  A  key issue that they wanted their language
    to  solve  is what they called the "two-language problem." This
    situation  is familiar to anyone who has used Python or another
    dynamic  language on a demanding numerical problem. To get good
    performance,   you  will  wind  up  rewriting  the  numerically
    intensive  parts  of  the program in C or Fortran, dealing with
    the  interface  between  the  two  languages,  and may still be
    disappointed  in  the overhead presented by calling the foreign
    routines from your original code.
    
    For  Python,  [22]NumPy and SciPy wrap many numerical routines,
    written  in Fortran or C, for efficient use from that language,
    but  you  can  only  take advantage of this if your calculation
    fits  the  pattern  of  an  available  routine; in more general
    cases,  where you will have to write a loop over your data, you
    are  stuck with Python's native performance, which is orders of
    magnitude  slower.  If  you  switch  to  an alternative, faster
    implementation  of  Python,  such  as  [23]PyPy , the numerical
    libraries  may  not  be  compatible; NumPy became available for
    PyPy only within about the past year.
    
    Julia  solves  the  two-language problem by being as expressive
    and  simple  to  program  in  as  a dynamic scripting language,
    while  having  the  native  performance  of  a static, compiled
    language.  There  is  no need to write numerical libraries in a
    second  language,  but  C  or  Fortran  library routines can be
    called   using  a  facility  that  Julia  has  built-in.  Other
    languages,  such as [24]Python or [25]R , can also interoperate
    easily with Julia using external packages.
    
    Documentation
    
    There  are  many  resources  to  turn to to learn the language.
    There   is  an  extensive  and  detailed  [26]manual  at  Julia
    headquarters,  and  this may be a good place to start. However,
    although  the first few chapters provide a gentle introduction,
    the  material soon becomes dense and, at times, hard to follow,
    with  references to concepts that are not explained until later
    chapters.  Fortunately,  there  is a [27]"learning" link at the
    top  of  the Julia home page, which takes you to a long list of
    videos,  tutorials,  books,  articles,  and  classes both about
    Julia  and that use Julia in teaching subjects such a numerical
    analysis.  There  is also a fairly good [28]cheat-sheet [PDF] ,
    which was just updated for v. 1.0.
    
    If  you're  coming  from  Python,  [29]this  list of noteworthy
    differences  between  Python  and Julia syntax will probably be
    useful.
    
    Some  of  the  linked  tutorials are in the form of [30]Jupyter
    notebooks  — indeed, the name "Jupyter" is formed from "Julia",
    "Python",  and  "R",  which  are  the  three original languages
    supported  by  the  interface. The [31]Julia kernel for Jupyter
    was  recently upgraded to support v. 1.0. Judicious sampling of
    a  variety  of  documentation  sources,  combined  with liberal
    experimentation,  may be the best way of learning the language.
    Jupyter  makes this experimentation more inviting for those who
    enjoy  the  web-based  interface,  but the REPL that comes with
    Julia  helps  a  great  deal  in  this regard by providing, for
    instance,  TAB  completion and an extensive help system invoked
    by simply pressing the "?" key.
    
    Stay tuned
    
    The  [32]next  installment in this two-part series will explain
    how   Julia  is  organized  around  the  concept  of  "multiple
    dispatch".  You  will  learn  how  to create functions and make
    elementary  use  of  Julia's  type  system.  We'll  see  how to
    install  packages  and  use  modules,  and  how to make graphs.
    Finally,  Part  2  will  briefly survey the important topics of
    macros and distributed computing.
    
    [33]Comments (80 posted)
    
    [34]C considered dangerous
    
    By Jake Edge
    
    August 29, 2018
    
    [35]LSS NA
    
    At  the  North  America  edition of the [36]2018 Linux Security
    Summit  (LSS  NA),  which was held in late August in Vancouver,
    Canada,  Kees  Cook  gave a presentation on some of the dangers
    that  come  with  programs  written  in  C.  In  particular, of
    course,  the  Linux  kernel is mostly written in C, which means
    that  the security of our systems rests on a somewhat dangerous
    foundation.  But there are things that can be done to help firm
    things  up  by  " Making C Less Dangerous " as the title of his
    talk suggested.
    
    He  began  with  a brief summary of the work that he and others
    are  doing  as  part  of the [37]Kernel Self Protection Project
    (KSPP).  The  goal  of the project is to get kernel protections
    merged  into  the  mainline. These protections are not targeted
    at  protecting user-space processes from other (possibly rogue)
    processes,  but  are, instead, focused on protecting the kernel
    from  user-space  code.  There  are around 12 organizations and
    ten  individuals  working  on roughly 20 different technologies
    as  part  of the KSPP, he said. The progress has been "slow and
    steady", he said, which is how he thinks it should go.  [38]
    
    One  of  the  main  problems is that C is treated mostly like a
    fancy  assembler.  The  kernel  developers do this because they
    want  the  kernel to be as fast and as small as possible. There
    are   other   reasons,   too,   such   as   the   need   to  do
    architecture-specific  tasks that lack a C API (e.g. setting up
    page tables, switching to 64-bit mode).
    
    But   there   is   lots   of  undefined  behavior  in  C.  This
    "operational   baggage"   can  lead  to  various  problems.  In
    addition,  C  has a weak standard library with multiple utility
    functions  that  have  various  pitfalls.  In C, the content of
    uninitialized  automatic  variables  is  undefined,  but in the
    machine  code that it gets translated to, the value is whatever
    happened  to  be  in  that  memory  location  before.  In  C, a
    function  pointer can be called even if the type of the pointer
    does  not  match the type of the function being called—assembly
    doesn't care, it just jumps to a location, he said.
    
    The  APIs  in  the standard library are also bad in many cases.
    He  asked:  why is there no argument to memcpy() to specify the
    maximum  destination  length?  He  noted a recent [39]blog post
    from  Raph  Levien  entitled "With Undefined Behavior, Anything
    is  Possible".  That  obviously  resonated  with  Cook,  as  he
    pointed  out  his  T-shirt—with  the title and artwork from the
    post.
    
    Less danger
    
    He  then  moved on to some things that kernel developers can do
    (and  are  doing) to get away from some of the dangers of C. He
    began  with variable-length arrays (VLAs), which can be used to
    overflow  the  stack to access data outside of its region. Even
    if  the  stack  has a guard page, VLAs can be used to jump past
    it  to  write into other memory, which can then be used by some
    other  kind  of  attack. The C language is "perfectly fine with
    this".  It  is  easy  to find uses of VLAs with the -Wvla flag,
    however.
    
    But  it  turns  out  that  VLAs  are  [40]not  just  bad from a
    security   perspective   ,   they   are   also   slow.   In   a
    micro-benchmark  associated with a [41]patch removing a VLA , a
    13%  performance  boost  came from using a fixed-size array. He
    dug  in  a  bit  further and found that much more code is being
    generated  to  handle a VLA, which explains the speed increase.
    Since  Linus  Torvalds  has  [42]declared  that  VLAs should be
    removed  from  the  kernel because they cause security problems
    and also slow the kernel down; Cook said "don't use VLAs".
    
    Another  problem area is switch statements, in particular where
    there  is  no  break  for  a  case  .  That could mean that the
    programmer  expects  and wants to fall through to the next case
    or  it could be that the break was simply forgotten. There is a
    way  to  get a warning from the compiler for fall-throughs, but
    there  needs  to be a way to mark those that are truly meant to
    be  that way. A special fall-through "statement" in the form of
    a   comment   is   what   has   been   agreed   on  within  the
    static-analysis  community.  He  and  others  have  been  going
    through  each  of  the  places  where  there is no break to add
    these  comments  (or  a break ); they have "found a lot of bugs
    this way", he said.
    
    Uninitialized  local variables will generate a warning, but not
    if  the  variable is passed in by reference. There are some GCC
    plugins  that  will  automatically  initialize these variables,
    but  there are also patches for both GCC and Clang to provide a
    compiler  option  to  do  so. Neither of those is upstream yet,
    but  Torvalds has praised the effort so the kernel would likely
    use  the  option.  An  interesting  side effect that came about
    while   investigating   this   was   a  warning  he  got  about
    unreachable  code  when  he  enabled  the  auto-initialization.
    There  were  two  variables  declared  just after a switch (and
    outside of any case ), where they would never be reached.
    
    Arithmetic  overflow  is  another  undefined behavior in C that
    can  cause various problems. GCC can check for signed overflow,
    which  performs  well  (the overhead is in the noise, he said),
    but  adding warning messages for it does grow the kernel by 6%;
    making  the  overflow abort, instead, only adds 0.1%. Clang can
    check  for  both  signed and unsigned overflow; signed overflow
    is  undefined,  while  unsigned  overflow is defined, but often
    unexpected.  Marking places where unsigned overflow is expected
    is  needed;  it would be nice to get those annotations put into
    the kernel, Cook said.
    
    Explicit   bounds   checking   is   expensive.   Doing  it  for
    copy_{to,from}_user()  is  a  less than 1% performance hit, but
    adding  it  to  the strcpy() and memcpy() families are around a
    2%  hit. Pre-Meltdown that would have been a totally impossible
    performance  regression  for  security, he said; post-Meltdown,
    since  it  is less than 5%, maybe there is a chance to add this
    checking.
    
    Better  APIs would help as well. He pointed to the evolution of
    strcpy()  ,  through  str  n  cpy()  and str l cpy() (each with
    their  own bounds flaws) to str s cpy() , which seems to be "OK
    so  far".  He  also mentioned memcpy() again as a poor API with
    respect to bounds checking.
    
    Hardware  support  for  bounds  checking  is  available  in the
    application  data  integrity  (ADI)  feature  for  SPARC and is
    coming  for  Arm; it may also be available for Intel processors
    at  some point. These all use a form of "memory tagging", where
    allocations  get a tag that is stored in the high-order byte of
    the  address.  An offset from the address can be checked by the
    hardware  to  see if it still falls within the allocated region
    based on the tag.
    
    Control-flow  integrity  (CFI)  has  become  more  of  an issue
    lately  because much of what attackers had used in the past has
    been  marked  as  "no  execute"  so  they  are turning to using
    existing  code  "gadgets"  already  present  in  the  kernel by
    hijacking  existing indirect function calls. In C, you can just
    call  pointers  without  regard  to  the type as it just treats
    them  as  an  address  to  jump  to.  Clang  has a CFI-sanitize
    feature  that  enforces  the function prototype to restrict the
    calls  that  can  be  made.  It  is  done at runtime and is not
    perfect,  in  part  because  there are lots of functions in the
    kernel  that  take  one  unsigned  long parameter and return an
    unsigned long.
    
    Attacks  on  CFI  have both a "forward edge", which is what CFI
    sanitize  tries  to  handle,  and  a "backward edge" that comes
    from  manipulating  the  stack  values,  the  return address in
    particular.  Clang  has  two  methods  available to prevent the
    stack  manipulation.  The first is the "safe stack", which puts
    various   important  items  (e.g.  "safe"  variables,  register
    spills,   and   the   return  address)  on  a  separate  stack.
    Alternatively,  the  "shadow  stack" feature creates a separate
    stack just for return addresses.
    
    One  problem  with  these  other  stacks is that they are still
    writable,  so  if an attacker can find them in memory, they can
    still  perform  their attacks. Hardware-based protections, like
    Intel's     Control-Flow    Enforcement    Technology    (CET),
    [43]provides   a   read-only   shadow  call  stack  for  return
    addresses.   Another   hardware   protection   is   [44]pointer
    authentication  for  Arm, which adds a kind of encrypted tag to
    the return address that can be verified before it is used.
    
    Status and challenges
    
    Cook  then  went  through  the current status of handling these
    different  problems  in  the kernel. VLAs are almost completely
    gone,  he  said,  just a few remain in the crypto subsystem; he
    hopes  those  VLAs will be gone by 4.20 (or whatever the number
    of  the  next  kernel  release  turns  out  to  be).  Once that
    happens,  he  plans  to  turn  on -Wvla for the kernel build so
    that none creep back in.
    
    There  has  been  steady  progress made on marking fall-through
    cases  in  switch  statements. Only 745 remain to be handled of
    the  2311  that  existed  when  this  work  started;  each  one
    requires  scrutiny  to  determine  what the author's intent is.
    Auto-initialized  local  variables  can  be done using compiler
    plugins,  but  that  is "not quite what we want", he said. More
    compiler   support  would  be  helpful  there.  For  arithmetic
    overflow,  it  would  be  nice  to  see GCC get support for the
    unsigned  case,  but  memory allocations are now doing explicit
    overflow checking at this point.
    
    Bounds  checking has seen some "crying about performance hits",
    so  we  are  waiting impatiently for hardware support, he said.
    CFI  forward-edge  protection  needs [45]link-time optimization
    (LTO)  support  for  Clang  in  the kernel, but it is currently
    working  on  Android.  For  backward-edge mitigation, the Clang
    shadow   call   stack   is  working  on  Android,  but  we  are
    impatiently waiting for hardware support for that too.
    
    There  are a number of challenges in doing security development
    for  the  kernel,  Cook said. There are cultural boundaries due
    to  conservatism  within  the  kernel  community; that requires
    patiently  working  and reworking features in order to get them
    upstream.  There  are,  of course, technical challenges because
    of  the complexity of security changes; those kinds of problems
    can  be solved. There are also resource limitations in terms of
    developers,  testers,  reviewers, and so on. KSPP and the other
    kernel  security  developers  are  still  making that "slow but
    steady" progress.
    
    Cook's  [46]slides  [PDF] are available for interested readers;
    before  long,  there should be a video available of the talk as
    well.
    
    [I  would  like  to  thank  LWN's  travel  sponsor,  the  Linux
    Foundation,  for travel assistance to attend the Linux Security
    Summit in Vancouver.]
    
    [47]Comments (70 posted)
    
    [48]The second half of the 4.19 merge window
    
    By Jonathan Corbet
    
    August  26,  2018    By  the  time  Linus Torvalds [49]released
    4.19-rc1  and  closed  the  merge  window  for this development
    cycle,  12,317  non-merge  changesets  had found their way into
    the  mainline;  about  4,800  of  those  landed  after [50]last
    week's  summary  was  written.  As tends to be the case late in
    the  merge  window,  many  of  those changes were fixes for the
    bigger  patches  that  went  in  early,  but  there were also a
    number  of  new  features  added.  Some of the more significant
    changes include:
    
    Core kernel
    
    The  full  set of patches adding [51]control-group awareness to
    the  out-of-memory  killer  has  not been merged due to ongoing
    disagreements,  but  one  piece  of  it  has:  there  is  a new
    memory.oom.group  control  knob  that  will cause all processes
    within  a  control  group  to  be  killed  in  an out-of-memory
    situation.
    
    A  new set of protections has been added to prevent an attacker
    from  fooling  a  program  into  writing to an existing file or
    FIFO.  An  open  with  the  O_CREAT flag to a file or FIFO in a
    world-writable,  sticky directory (e.g. /tmp ) will fail if the
    owner  of  the  opening  process is not the owner of either the
    target   file  or  the  containing  directory.  This  behavior,
    disabled    by    default,    is    controlled   by   the   new
    protected_regular and protected_fifos sysctl knobs.
    
    Filesystems and block layer
    
    The  dm-integrity  device-mapper  target can now use a separate
    device for metadata storage.
    
    EROFS,  the  "enhanced read-only filesystem", has been added to
    the  staging  tree. It is " a lightweight read-only file system
    with    modern   designs   (eg.   page-sized   blocks,   inline
    xattrs/data,  etc.)  for  scenarios which need high-performance
    read-only  requirements,  eg.  firmwares  in  mobile  phone  or
    LIVECDs "
    
    The  new  "metadata  copy-up"  feature  in overlayfs will avoid
    copying   a   file's   contents   to   the  upper  layer  on  a
    metadata-only change. See [52]this commit for details.
    
    Hardware support
    
    Graphics : Qualcomm Adreno A6xx GPUs.
    
    Industrial    I/O    :    Spreadtrum    SC27xx    series   PMIC
    analog-to-digital    converters,    Analog    Devices    AD5758
    digital-to-analog  converters, Intersil ISL29501 time-of-flight
    sensors,  Silicon  Labs  SI1133  UV  index/ambient light sensor
    chips, and Bosch Sensortec BME680 sensors.
    
    Miscellaneous   :  Generic  ADC-based  resistive  touchscreens,
    Generic  ASIC  devices  via  the  Google [53]Gasket framework ,
    Analog  Devices  ADGS1408/ADGS1409  multiplexers,  Actions Semi
    Owl  SoCs  DMA  controllers,  MEN  16Z069 watchdog timers, Rohm
    BU21029   touchscreen   controllers,   Cirrus   Logic  CS47L35,
    CS47L85,  CS47L90,  and  CS47L91  codecs,  Cougar  500k  gaming
    keyboards,   Qualcomm   GENI-based   I2C  controllers,  Actions
    Semiconductor  Owl  I2C  controllers,  ChromeOS  EC-based USBPD
    chargers, and Analog Devices ADP5061 battery chargers.
    
    USB  :  Nuvoton  NPCM7XX on-chip EHCI USB controllers, Broadcom
    Stingray PCIe PHYs, and Renesas R-Car generation 3 PCIe PHYs.
    
    There  is  also  a  new  subsystem  for the abstraction of GNSS
    (global  navigation  satellite  systems  —  GPS,  for  example)
    receivers  in  the  kernel.  To  date,  such  devices have been
    handled  with  an  abundance of user-space drivers; the hope is
    to  bring  some  order  in  this  area.  Support for u-blox and
    SiRFstar receivers has been added as well.
    
    Kernel internal
    
    The  __deprecated  marker,  used to mark interfaces that should
    no  longer  be  used,  has been deprecated and removed from the
    kernel  entirely.  [54]Torvalds  said  : " They are not useful.
    They  annoy  everybody,  and  nobody  ever  does anything about
    them,  because  it's  always 'somebody elses problem'. And when
    people  start  thinking  that  warnings  are  normal, they stop
    looking  at  them, and the real warnings that mean something go
    unnoticed. "
    
    The  minimum  version  of  GCC  required by the kernel has been
    moved up to 4.6.
    
    There  are  a  couple of significant changes that failed to get
    in  this  time around, including the [55]XArray data structure.
    The  patches are thought to be ready, but they had the bad luck
    to  be  based  on  a  tree  that  failed to be merged for other
    reasons,  so  Torvalds  [56]didn't even look at them . That, in
    turn,   blocks  another  set  of  patches  intended  to  enable
    migration of slab-allocated objects.
    
    The  other  big  deferral  is  the  [57]new system-call API for
    filesystem  mounting  . Despite ongoing [58]concerns about what
    happens  when  the  same  low-level  device is mounted multiple
    times  with  conflicting  options,  Al  Viro  sent  [59]a  pull
    request  to  send  this  work  upstream. The ensuing discussion
    made  it  clear  that  there  is  still not a consensus in this
    area,  though,  so  it  seems  that  this  work has to wait for
    another cycle.
    
    Assuming  all  goes  well,  the  kernel will stabilize over the
    coming  weeks  and  the  final  4.19  release  will  happen  in
    mid-October.
    
    [60]Comments (1 posted)
    
    [61]Measuring (and fixing) I/O-controller throughput loss
    
    August 29, 2018
    
    This article was contributed by Paolo Valente
    
    Many  services,  from  web hosting and video streaming to cloud
    storage,  need  to  move  data  to  and from storage. They also
    often  require  that  each  per-client I/O flow be guaranteed a
    non-zero   amount  of  bandwidth  and  a  bounded  latency.  An
    expensive  way to provide these guarantees is to over-provision
    storage  resources,  keeping  each  resource underutilized, and
    thus  have  plenty of bandwidth available for the few I/O flows
    dispatched  to  each  medium.  Alternatively one can use an I/O
    controller.  Linux provides two mechanisms designed to throttle
    some  I/O  streams  to allow others to meet their bandwidth and
    latency  requirements.  These mechanisms work, but they come at
    a  cost:  a  loss  of  as  much  as  80% of total available I/O
    bandwidth.  I  have run some tests to demonstrate this problem;
    some   upcoming  improvements  to  the  [62]bfq  I/O  scheduler
    promise to improve the situation considerably.
    
    Throttling  does  guarantee control, even on drives that happen
    to  be highly utilized but, as will be seen, it has a hard time
    actually  ensuring  that  drives are highly utilized. Even with
    greedy  I/O  flows,  throttling  easily  ends  up  utilizing as
    little  as  20%  of the available speed of a flash-based drive.
    Such   a  speed  loss  may  be  particularly  problematic  with
    lower-end   storage.   On   the   opposite   end,  it  is  also
    disappointing  with  high-end  hardware, as the Linux block I/O
    stack  itself  has  been  [63]redesigned  from the ground up to
    fully  utilize  the  high  speed  of  modern,  fast storage. In
    addition,   throttling   fails   to   guarantee   the  expected
    bandwidths  if  I/O  contains  both  reads  and  writes,  or is
    sporadic in nature.
    
    On  the  bright  side,  there  now  seems  to  be  an effective
    alternative  for controlling I/O: the proportional-share policy
    provided  by  the  bfq  I/O  scheduler.  It enables nearly 100%
    storage  bandwidth  utilization,  at  least  with  some  of the
    workloads  that  are  problematic  for  throttling. An upcoming
    version  of  bfq may be able to achieve this result with almost
    all  workloads.  Finally,  bfq  guarantees  bandwidths with all
    workloads.  The current limitation of bfq is that its execution
    overhead  becomes  significant  at  speeds  above  400,000  I/O
    operations per second on commodity CPUs.
    
    Using  the  bfq  I/O  scheduler,  Linux  can  now guarantee low
    latency  to  lightweight  flows containing sporadic, short I/O.
    No  throughput  issues arise, and no configuration is required.
    This  capability benefits important, time-sensitive tasks, such
    as  video  or audio streaming, as well as executing commands or
    starting  applications.  Although  benchmarks are not available
    yet,  these  guarantees  might  also  be  provided by the newly
    proposed  [64]I/O latency controller . It allows administrators
    to  set target latencies for I/O requests originating from each
    group  of  processes,  and  favors  the  groups with the lowest
    target latency.
    
    The testbed
    
    I  ran  the  tests with an ext4 filesystem mounted on a PLEXTOR
    PX-256M5S  SSD,  which  features  a  peak rate of ~160MB/s with
    random  I/O,  and  of  ~500MB/s  with  sequential  I/O.  I used
    blk-mq,  in  Linux  4.18. The system was equipped with a 2.4GHz
    Intel  Core  i7-2760QM  CPU  and  1.3GHz  DDR3  DRAM. In such a
    system,  a  single  thread  doing  synchronous  reads reaches a
    throughput of 23MB/s.
    
    For  the purposes of these tests, each process is considered to
    be  in  one of two groups, termed "target" and "interferers". A
    target  is  a  single-process,  I/O-bound  group  whose  I/O is
    focused  on.  In  particular,  I  measure  the  I/O  throughput
    enjoyed  by  this  group to get the minimum bandwidth delivered
    to  the group. An interferer is single-process group whose role
    is  to  generate additional I/O that interferes with the I/O of
    the  target.  The  tested  workloads  contain  one  target  and
    multiple interferers.
    
    The  single  process  in  each  group  either  reads or writes,
    through  asynchronous  (buffered)  operations,  to  one  file —
    different  from the file read or written by any other process —
    after  invalidating  the  buffer cache for the file. I define a
    reader  or  writer  process as either "random" or "sequential",
    depending  on  whether  it  reads  or writes its file at random
    positions  or  sequentially.  Finally, an interferer is defined
    as  being either "active" or "inactive" depending on whether it
    performs  I/O during the test. When an interferer is mentioned,
    it is assumed that the interferer is active.
    
    Workloads  are  defined  so as to try to cover the combinations
    that,  I believe, most influence the performance of the storage
    device  and of the I/O policies. For brevity, in this article I
    show results for only two groups of workloads:
    
    Static  sequential  :  four  synchronous  sequential readers or
    four   asynchronous  sequential  writers,  plus  five  inactive
    interferers.
    
    Static  random  :  four  synchronous random readers, all with a
    block size equal to 4k, plus five inactive interferers.
    
    To  create  each  workload,  I  considered,  for  each  mix  of
    interferers  in the group, two possibilities for the target: it
    could  be  either  a random or a sequential synchronous reader.
    In  [65]a  longer version of this article [PDF] , you will also
    find   results  for  workloads  with  varying  degrees  of  I/O
    randomness,  and for dynamic workloads (containing sporadic I/O
    sources).  These extra results confirm the losses of throughput
    and I/O control for throttling that are shown here.
    
    I/O policies
    
    Linux  provides  two I/O-control mechanisms for guaranteeing (a
    minimum)  bandwidth, or at least fairness, to long-lived flows:
    the   throttling  and  proportional-share  I/O  policies.  With
    throttling,  one  can  set  a  maximum  bandwidth  limit — "max
    limit"  for brevity — for the I/O of each group. Max limits can
    be  used,  in an indirect way, to provide the service guarantee
    at  the  focus  of  this  article.  For  example,  to guarantee
    minimum  bandwidths  to  I/O flows, a group can be guaranteed a
    minimum  bandwidth by limiting the maximum bandwidth of all the
    other groups.
    
    Unfortunately,  max  limits  have  two  drawbacks  in  terms of
    throughput.  First,  if  some groups do not use their allocated
    bandwidth,  that  bandwidth cannot be reclaimed by other active
    groups.  Second,  limits  must comply with the worst-case speed
    of  the  device,  namely, its random-I/O peak rate. Such limits
    will  clearly  leave  a lot of throughput unused with workloads
    that  otherwise  would  drive  the  device to higher throughput
    levels.  Maximizing  throughput  is  simply  not  a goal of max
    limits.  So,  for brevity, test results with max limits are not
    shown  here.  You  can find these results, plus a more detailed
    description  of  the  above  drawbacks,  in the long version of
    this article.
    
    Because  of  these  drawbacks,  a  new, still experimental, low
    limit  has  been  added to the throttling policy. If a group is
    assigned  a low limit, then the throttling policy automatically
    limits  the  I/O of the other groups in such a way to guarantee
    to  the  group  a  minimum  bandwidth equal to its assigned low
    limit.  This  new  throttling  mechanism  throttles no group as
    long  as  every  group is getting at least its assigned minimum
    bandwidth.  I  tested  this mechanism, but did not consider the
    interesting  problem  of guaranteeing minimum bandwidths while,
    at the same time, enforcing maximum bandwidths.
    
    The  other  I/O  policy available in Linux, proportional share,
    provides  weighted  fairness.  Each group is assigned a weight,
    and   should   receive   a  portion  of  the  total  throughput
    proportional  to  its  weight.  This  scheme guarantees minimum
    bandwidths  in  the  same way that low limits do in throttling.
    In  particular, it guarantees to each group a minimum bandwidth
    equal  to  the  ratio  between the weight of the group, and the
    sum  of the weights of all the groups that may be active at the
    same time.
    
    The  actual implementation of the proportional-share policy, on
    a  given drive, depends on what flavor of the block layer is in
    use  for  that  drive.  If  the drive is using the legacy block
    interface,  the policy is implemented by the cfq I/O scheduler.
    Unfortunately,   cfq   fails   to   control   bandwidths   with
    flash-based  storage,  especially  on  drives featuring command
    queueing.  This  case  is  not  considered in these tests. With
    drives  using  the  multiqueue interface, proportional share is
    implemented  by  bfq. This is the combination considered in the
    tests.
    
    To  benchmark  both  throttling  (low  limits) and proportional
    share,  I  tested,  for  each workload, the combinations of I/O
    policies  and  I/O  schedulers  reported in the table below. In
    the  end,  there  are  three  test  cases for each workload. In
    addition,  for some workloads, I considered two versions of bfq
    for the proportional-share policy.
    
    Name
    
    I/O policy
    
    Scheduler
    
    Parameter for target
    
    Parameter for each of the four active interferers
    
    Parameter for each of the five inactive interferers
    
    Sum of parameters
    
    low-none
    
    Throttling with low limits
    
    none
    
    10MB/s
    
    10MB/s (tot: 40)
    
    20MB/s (tot: 100)
    
    150MB/s
    
    prop-bfq
    
    Proportional share
    
    bfq
    
    300
    
    100 (tot: 400)
    
    200 (tot: 1000)
    
    1700
    
    For  low  limits,  I  report  results with only none as the I/O
    scheduler,  because  the  results  are  the same with kyber and
    mq-deadline.
    
    The  capabilities of the storage medium and of low limits drove
    the policy configurations. In particular:
    
    The  configuration  of the target and of the active interferers
    for  low-none  is  the one for which low-none provides its best
    possible  minimum-bandwidth  guarantee  to  the target: 10MB/s,
    guaranteed  if  all interferers are readers. Results remain the
    same  regardless of the values used for target latency and idle
    time;  I  set them to 100µs and 1000µs, respectively, for every
    group.
    
    Low  limits  for  inactive  interferers  are  set  to twice the
    limits  for active interferers, to pose greater difficulties to
    the policy.
    
    I  chose weights for prop-bfq so as to guarantee about the same
    minimum  bandwidth  as  low-none  to  the  target,  in the same
    only-reader  worst  case  as  for  low-none  and  to  preserve,
    between  the  weights  of  active and inactive interferers, the
    same  ratio  as  between  the low limits of active and inactive
    interferers.
    
    Full  details  on  configurations  can  be  found  in  the long
    version of this article.
    
    Each  workload  was  run  ten  times  for each policy, plus ten
    times   without  any  I/O  control,  i.e.,  with  none  as  I/O
    scheduler  and  no  I/O policy in use. For each run, I measured
    the  I/O  throughput of the target (which reveals the bandwidth
    provided  to  the target), the cumulative I/O throughput of the
    interferers,  and  the  total  I/O throughput. These quantities
    fluctuated  very  little  during  each  run,  as well as across
    different  runs. Thus in the graphs I report only averages over
    per-run  average throughputs. In particular, for the case of no
    I/O  control,  I  report only the total I/O throughput, to give
    an  idea of the throughput that can be reached without imposing
    any control.
    
    Results
    
    This  plot  shows  throughput results for the simplest group of
    workloads: the static-sequential set.
    
    With  a  random reader as the target against sequential readers
    as  interferers,  low-none  does  guarantee  the configured low
    limit   to  the  target.  Yet  it  reaches  only  a  low  total
    throughput.  The  throughput  of  the  random  reader evidently
    oscillates  around 10MB/s during the test. This implies that it
    is  at least slightly below 10MB/s for a significant percentage
    of  the  time.  But  when this happens, the low-limit mechanism
    limits  the  maximum bandwidth of every active group to the low
    limit  set  for the group, i.e., to just 10MB/s. The end result
    is  a total throughput lower than 10% of the throughput reached
    without I/O control.
    
    That  said, the high throughput achieved without I/O control is
    obtained  by  choking  the random I/O of the target in favor of
    the  sequential  I/O  of  the interferers. Thus, it is probably
    more  interesting  to  compare  low-none  throughput  with  the
    throughput  reachable while actually guaranteeing 10MB/s to the
    target.  The  target  is  a single, synchronous, random reader,
    which  reaches  23MB/s while active. So, to guarantee 10MB/s to
    the  target,  it  is  enough  to serve it for about half of the
    time,  and the interferers for the other half. Since the device
    reaches  ~500MB/s  with  the sequential I/O of the interferers,
    the  resulting  throughput  with  this  service scheme would be
    (500+23)/2,  or  about 260MB/s. low-none thus reaches less than
    20%  of  the total throughput that could be reached while still
    preserving the target bandwidth.
    
    prop-bfq  provides the target with a slightly higher throughput
    than  low-none.  This  makes  it harder for prop-bfq to reach a
    high  total throughput, because prop-bfq serves more random I/O
    (from  the target) than low-none. Nevertheless, prop-bfq gets a
    much  higher  total  throughput than low-none. According to the
    above  estimate,  this  throughput  is about 90% of the maximum
    throughput  that  could  be reached, for this workload, without
    violating  service  guarantees. The reason for this good result
    is  that  bfq  provides  an  effective  implementation  of  the
    proportional-share  service  policy.  At  any time, each active
    group  is  granted  a fraction of the current total throughput,
    and  the  sum  of  these  fractions  is  equal to one; so group
    bandwidths  naturally  saturate  the available total throughput
    at all times.
    
    Things  change  with  the  second  workload:  a  random  reader
    against  sequential writers. Now low-none reaches a much higher
    total  throughput  than  prop-bfq.  low-none  serves  much more
    sequential  (write)  I/O  than  prop-bfq because writes somehow
    break  the  low-limit  mechanisms and prevail over the reads of
    the  target.  Conceivably,  this happens because writes tend to
    both  starve  reads  in  the OS (mainly by eating all available
    I/O  tags)  and to cheat on their completion time in the drive.
    In  contrast,  bfq  is  intentionally  configured  to privilege
    reads, to counter these issues.
    
    In  particular, low-none gets an even higher throughput than no
    I/O  control  at all because it penalizes the random I/O of the
    target even more than the no-controller configuration.
    
    Finally,  with  the  last  two workloads, prop-bfq reaches even
    higher  total  throughput  than  with the first two. It happens
    because  the  target  also  does  sequential  I/O,  and serving
    sequential  I/O  is  much  more  beneficial for throughput than
    serving  random  I/O.  With  these  two  workloads,  the  total
    throughput  is, respectively, close to or much higher than that
    reached  without  I/O control. For the last workload, the total
    throughput  is  much higher because, differently from none, bfq
    privileges  reads  over  asynchronous writes, and reads yield a
    higher  throughput  than  writes.  In  contrast, low-none still
    gets  lower  or much lower throughput than prop-bfq, because of
    the  same issues that hinder low-none throughput with the first
    two workloads.
    
    As  for  bandwidth  guarantees,  with  readers  as  interferers
    (third  workload),  prop-bfq,  as  expected, gives the target a
    fraction  of  the  total throughput proportional to its weight.
    bfq    approximates    perfect   proportional-share   bandwidth
    distribution  among groups doing I/O of the same type (reads or
    writes)  and  with  the  same  locality (sequential or random).
    With  the last workload, prop-bfq gives much more throughput to
    the  reader  than  to  all the interferers, because interferers
    are asynchronous writers, and bfq privileges reads.
    
    The  second  group  of  workloads  (static random), is the one,
    among   all   the  workloads  considered,  for  which  prop-bfq
    performs worst. Results are shown below:
    
    This  chart reports results not only for mainline bfq, but also
    for  an improved version of bfq which is currently under public
    testing.  As  can  be  seen, with only random readers, prop-bfq
    reaches  a  much  lower  total  throughput  than low-none. This
    happens  because of the Achilles heel of the bfq I/O scheduler.
    If  the  process  in  service  does  synchronous  I/O and has a
    higher  weight  than  some  other process, then, to give strong
    bandwidth   guarantees   to   that   process,   bfq  plugs  I/O
    dispatching  every  time  the process temporarily stops issuing
    I/O   requests.   In  this  respect,  processes  actually  have
    differentiated  weights and do synchronous I/O in the workloads
    tested.  So  bfq systematically performs I/O plugging for them.
    Unfortunately,  this  plugging  empties  the internal queues of
    the  drive, which kills throughput with random I/O. And the I/O
    of all processes in these workloads is also random.
    
    The  situation  reverses  with  a  sequential reader as target.
    Yet,  the most interesting results come from the new version of
    bfq,  containing  small  changes  to  counter exactly the above
    weakness.  This  version  recovers  most of the throughput loss
    with  the  workload  made of only random I/O and more; with the
    second  workload,  where  the target is a sequential reader, it
    reaches about 3.7 times the total throughput of low-none.
    
    When  the main concern is the latency of flows containing short
    I/O,  Linux seems now rather high performing, thanks to the bfq
    I/O  scheduler  and  the  I/O  latency  controller.  But if the
    requirement  is  to  provide  explicit bandwidth guarantees (or
    just  fairness) to I/O flows, then one must be ready to give up
    much  or most of the speed of the storage media. bfq helps with
    some   workloads,   but  loses  most  of  the  throughput  with
    workloads  consisting  of mostly random I/O. Fortunately, there
    is  apparently  hope  for  much  better  performance  since  an
    improvement,  still  under  development, seems to enable bfq to
    reach a high throughput with all workloads tested so far.
    
    [  I  wish  to  thank  Vivek Goyal for enabling me to make this
    article much more fair and sound.]
    
    [66]Comments (4 posted)
    
    [67]KDE's onboarding initiative, one year later
    
    August 24, 2018
    
    This article was contributed by Marta Rybczyńska
    
    [68]Akademy
    
    In  2017,  the  KDE  community  decided  on  [69]three goals to
    concentrate  on  for  the  next  few  years.  One  of  them was
    [70]streamlining   the  onboarding  of  new  contributors  (the
    others  were  [71]improving usability and [72]privacy ). During
    [73]Akademy  ,  the  yearly  KDE  conference  that  was held in
    Vienna  in  August,  Neofytos Kolokotronis shared the status of
    the  onboarding  goal,  the work done during the last year, and
    further  plans.  While it is a complicated process in a project
    as  big  and  diverse  as  KDE, numerous improvements have been
    already made.
    
    Two  of the three KDE community goals were proposed by relative
    newcomers.  Kolokotronis  was  one  of those, having joined the
    [74]KDE  Promo  team  not  long  before  proposing the focus on
    onboarding.  He  had  previously  been involved with [75]Chakra
    Linux  ,  a  distribution  based on KDE software. The fact that
    new  members of the community proposed strategic goals was also
    noted in the [76]Sunday keynote by Claudia Garad .
    
    Proper  onboarding  adds excitement to the contribution process
    and  increases retention, he explained. When we look at [77]the
    definition  of  onboarding  ,  it is a process in which the new
    contributors  acquire  knowledge, skills, and behaviors so that
    they  can  contribute effectively. Kolokotronis proposed to see
    it  also  as  socialization:  integration  into  the  project's
    relationships, culture, structure, and procedures.
    
    The  gains  from  proper  onboarding  are many. The project can
    grow   by  attracting  new  blood  with  new  perspectives  and
    solutions.   The  community  maintains  its  health  and  stays
    vibrant.  Another  important  advantage of efficient onboarding
    is  that  replacing  current  contributors  becomes easier when
    they  change interests, jobs, or leave the project for whatever
    reason.  Finally,  successful  onboarding adds new advocates to
    the project.
    
    Achievements so far and future plans
    
    The  team  started  with  ideas  for  a  centralized onboarding
    process  for the whole of KDE. They found out quickly that this
    would  not  work  because KDE is "very decentralized", so it is
    hard  to  provide  tools  and procedures that are going to work
    for   the  whole  project.  According  to  Kolokotronis,  other
    characteristics   of   KDE  that  impact  onboarding  are  high
    diversity,   remote   and   online   teams,   and  hundreds  of
    contributors  in dozens of projects and teams. In addition, new
    contributors  already know in which area they want to take part
    and  they  prefer  specific  information  that will be directly
    useful for them.
    
    So  the  team  changed its approach; several changes have since
    been  proposed  and  implemented.  The  [78]Get  Involved page,
    which  is  expected to be one of the resources new contributors
    read  first, has been rewritten. For the [79]Junior Jobs page ,
    the  team  is  [80] [81]discussing what the generic content for
    KDE  as  a whole should be. The team simplified [82]Phabricator
    registration  ,  which  resulted  in  documenting  the  process
    better.  Another part of the work includes the [83]KDE Bugzilla
    ;  it  includes, for example initiatives to limit the number of
    states of a ticket or remove obsolete products.
    
    The   [84]Plasma   Mobile  team  is  heavily  involved  in  the
    onboarding  goal.  The Plasma Mobile developers have simplified
    their    development   environment   setup   and   created   an
    [85]interactive  "Get  Involved"  page. In addition, the Plasma
    team  changed  the  way task descriptions are written; they now
    contain  more detail, so that it is easier to get involved. The
    basic  description  should  be  short  and clear, and it should
    include  details  of  the  problem  and possible solutions. The
    developers  try  to  share  the  list  of  skills  necessary to
    fulfill  the  tasks  and  include  clear links to the technical
    resources needed.
    
    Kolokotronis  and  team  also identified a new potential source
    of  contributors  for  KDE:  distributions using KDE. They have
    the  advantage  of  already knowing and using the software. The
    next  idea  the team is working on is to make sure that setting
    up  a  development  environment is easy. The team plans to work
    on this during a dedicated sprint this autumn.
    
    Searching for new contributors
    
    Kolokotronis  plans  to  search  for  new  contributors  at the
    periphery  of  the  project,  among  the "skilled enthusiasts":
    loyal  users  who  actually  care  about the project. They "can
    make  wonders",  he  said.  Those  individuals may be also less
    confident  or  shy,  have  troubles  making the first step, and
    need  guidance.  The  project  leaders  should  take  that into
    account.
    
    In   addition,   newcomers   are  all  different.  Kolokotronis
    provided  a  long  list  of  how contributors differ, including
    skills  and  knowledge,  motives  and  interests,  and time and
    dedication.  His  advice  is to "try to find their superpower",
    the  skills  they  have  that  are  missing  in the team. Those
    "superpowers" can then be used for the benefit of the project.
    
    If  a project does nothing else, he said, it can start with its
    documentation.   However,   this   does   not  only  mean  code
    documentation.  Writing  down  the  procedures  or  information
    about  the internal work of the project, like who is working on
    what,  is  an  important  part of a project's documentation and
    helps  newcomers.  There  should  be  also guidelines on how to
    start, especially setting up the development environment.
    
    The  first  thing  the  project leaders should do, according to
    Kolokotronis,  is to spend time on introducing newcomers to the
    project.  Ideally  every  new  contributor  should  be assigned
    mentors  —  more  experienced  members  who  can help them when
    needed.  The mentors and project leaders should find tasks that
    are   interesting   for  each  person.  Answering  an  audience
    question   on   suggestions   for   shy  new  contributors,  he
    recommended  even  more  mentoring.  It is also very helpful to
    make  sure  that  newcomers  have  enough  to  read, but "avoid
    RTFM",  he  highlighted.  It is also easy for a new contributor
    "to  fly  away",  he  said.  The solution is to keep requesting
    things and be proactive.
    
    What the project can do?
    
    Kolokotronis  suggested  a number of actions for a project when
    it   wants  to  improve  its  onboarding.  The  first  step  is
    preparation:  the  project  leaders  should know the team's and
    the  project's  needs. Long-term planning is important, too. It
    is  not  enough  to wait for contributors to come — the project
    should  be  proactive,  which means reaching out to candidates,
    suggesting   appropriate  tasks  and,  finally,  making  people
    available for the newcomers if they need help.
    
    This  leads to next step: to be a mentor. Kolokotronis suggests
    being  a  "great  host",  but  also  trying  to  phase  out the
    dependency   on   the   mentor   rapidly.  "We  have  been  all
    newcomers",  he  said.  It  can  be  intimidating  to  join  an
    existing  group. Onboarding creates a sense of belonging which,
    in turn, increases retention.
    
    The  last  step  proposed  was  to  be strategic. This includes
    thinking  about  the  emotions  you  want  newcomers  to  feel.
    Kolokotronis  explained the strategic part with an example. The
    overall   goal   is   (surprise!)  improve  onboarding  of  new
    contributors.  An  intermediate  objective might be to keep the
    newcomers  after  they  have  made  their first commit. If your
    strategy  is  to  keep  them  confident  and proud, you can use
    different  tactics  like  praise and acknowledgment of the work
    in  public.  Another  useful  tactic  may  be  assigning simple
    tasks, according to the skill of the contributor.
    
    To   summarize,   the   most   important  thing,  according  to
    Kolokotronis,  is  to  respond  quickly and spend time with new
    contributors.  This  time should be used to explain procedures,
    and  to  introduce the people and culture. It is also essential
    to  guide  first  contributions  and praise contributor's skill
    and  effort. Increase the difficulty of tasks over time to keep
    contributors  motivated  and  challenged. And finally, he said,
    "turn them into mentors".
    
    Kolokotronis  acknowledges  that  onboarding  "takes  time" and
    "everyone  complains"  about  it. However, he is convinced that
    it  is  beneficial  in  the  long  term  and  that it decreases
    developer turnover.
    
    Advice to newcomers
    
    Kolokotronis  concluded  with some suggestions for newcomers to
    a  project.  They  should  try  to be persistent and to not get
    discouraged  when  something  goes  wrong. Building connections
    from   the  very  beginning  is  helpful.  He  suggests  asking
    questions  as  if you were already a member "and things will be
    fine". However, accept criticism if it happens.
    
    One  of  the  next  actions  of  the onboarding team will be to
    collect  feedback  from  newcomers and experienced contributors
    to  see  if they agree on the ideas and processes introduced so
    far.
    
    [86]Comments (none posted)
    
    [87]Sharing and archiving data sets with Dat
    
    August 27, 2018
    
    This article was contributed by Antoine Beaupré
    
    [88]Dat  is  a  new peer-to-peer protocol that uses some of the
    concepts  of  [89]BitTorrent  and  Git.  Dat  primarily targets
    researchers  and  open-data activists as it is a great tool for
    sharing,  archiving, and cataloging large data sets. But it can
    also  be  used to implement decentralized web applications in a
    novel way.
    
    Dat quick primer
    
    Dat  is  written in JavaScript, so it can be installed with npm
    ,  but there are [90]standalone binary builds and a [91]desktop
    application  (as an AppImage). An [92]online viewer can be used
    to  inspect data for those who do not want to install arbitrary
    binaries on their computers.
    
    The  command-line  application  allows  basic  operations  like
    downloading  existing  data sets and sharing your own. Dat uses
    a  32-byte hex string that is an [93]ed25519 public key , which
    is  is  used  to  discover  and  find  content  on the net. For
    example, this will download some sample data:  $ dat clone \
    
    dat://778f8d955175c92e4ced5e4f5563f69bfec0c86cc6f670352c457943-
    666fe639 \
    
    ~/Downloads/dat-demo
    
    Similarly,  the  share  command  is  used  to share content. It
    indexes  the  files  in  a  given  directory  and creates a new
    unique  address  like the one above. The share command starts a
    server  that uses multiple discovery mechanisms (currently, the
    [94]Mainline  Distributed  Hash  Table  (DHT), a [95]custom DNS
    server  ,  and  multicast  DNS)  to announce the content to its
    peers.  This  is  how another user, armed with that public key,
    can  download  that  content with dat clone or mirror the files
    continuously with dat sync .
    
    So  far,  this  looks  a  lot  like BitTorrent [96]magnet links
    updated  with 21st century cryptography. But Dat adds revisions
    on  top  of  that,  so  modifications  are automatically shared
    through  the  swarm.  That is important for public data sets as
    those  are  often  dynamic  in  nature.  Revisions also make it
    possible  to  use [97]Dat as a backup system by saving the data
    incrementally using an [98]archiver .
    
    While  Dat  is designed to work on larger data sets, processing
    them  for  sharing  may  take a while. For example, sharing the
    Linux  kernel  source  code  required about five minutes as Dat
    worked  on indexing all of the files. This is comparable to the
    performance  offered by [99]IPFS and BitTorrent. Data sets with
    more or larger files may take quite a bit more time.
    
    One  advantage  that  Dat  has  over  IPFS  is  that it doesn't
    duplicate  the  data. When IPFS imports new data, it duplicates
    the  files  into  ~/.ipfs . For collections of small files like
    the  kernel,  this  is not a huge problem, but for larger files
    like  videos  or  music,  it's  a  significant limitation. IPFS
    eventually  implemented  a solution to this [100]problem in the
    form  of the experimental [101]filestore feature , but it's not
    enabled  by  default.  Even  with that feature enabled, though,
    changes   to  data  sets  are  not  automatically  tracked.  In
    comparison,  Dat  operation on dynamic data feels much lighter.
    The downside is that each set needs its own dat share process.
    
    Like  any  peer-to-peer  system, Dat needs at least one peer to
    stay  online  to  offer  the  content, which is impractical for
    mobile  devices. Hosting providers like [102]Hashbase (which is
    a  [103]pinning  service  in  Dat  jargon)  can help users keep
    content  online  without  running  their  own [104]server . The
    closest   parallel  in  the  traditional  web  ecosystem  would
    probably   be  content  distribution  networks  (CDN)  although
    pinning    services    are   not   necessarily   geographically
    distributed  and  a  CDN does not necessarily retain a complete
    copy of a website.  [105]
    
    A  web  browser called [106]Beaker , based on the [107]Electron
    framework,  can  access  Dat  content  natively  without  going
    through  a pinning service. Furthermore, Beaker is essential to
    get   any   of  the  [108]Dat  applications  working,  as  they
    fundamentally  rely  on  dat://  URLs  to  do their magic. This
    means  that  Dat  applications won't work for most users unless
    they  install that special web browser. There is a [109]Firefox
    extension  called " [110]dat-fox " for people who don't want to
    install  yet  another  browser,  but  it  requires installing a
    [111]helper  program  .  The  extension  will  be  able to load
    dat://  URLs  but  many  applications  will still not work. For
    example,  the  [112]photo  gallery application completely fails
    with dat-fox.
    
    Dat-based  applications  look promising from a privacy point of
    view.  Because of its peer-to-peer nature, users regain control
    over  where their data is stored: either on their own computer,
    an  online server, or by a trusted third party. But considering
    the  protocol  is not well established in current web browsers,
    I  foresee  difficulties  in adoption of that aspect of the Dat
    ecosystem.  Beyond  that,  it  is rather disappointing that Dat
    applications  cannot  run  natively in a web browser given that
    JavaScript is designed exactly for that.
    
    Dat privacy
    
    An  advantage  Dat  has  over other peer-to-peer protocols like
    BitTorrent   is   end-to-end   encryption.   I  was  originally
    concerned   by   the   encryption   design   when  reading  the
    [113]academic paper [PDF] :
    
    It  is  up  to  client programs to make design decisions around
    which  discovery  networks  they  trust.  For  example if a Dat
    client  decides  to  use  the BitTorrent DHT to discover peers,
    and  they  are  searching for a publicly shared Dat key (e.g. a
    key  cited publicly in a published scientific paper) with known
    contents,  then because of the privacy design of the BitTorrent
    DHT  it  becomes  public  knowledge  what  key  that  client is
    searching for.
    
    So  in  other  words, to share a secret file with another user,
    the  public key is transmitted over a secure side-channel, only
    to  then  leak  during  the discovery process. Fortunately, the
    public  Dat  key is not directly used during discovery as it is
    [114]hashed  with  BLAKE2B  .  Still, the security model of Dat
    assumes   the   public  key  is  private,  which  is  a  rather
    counterintuitive  concept  that  might upset cryptographers and
    confuse  users  who  are  frequently  encouraged  to  type such
    strings  in  address bars and search engines as part of the Dat
    experience.  There  is a [115]security & privacy FAQ in the Dat
    documentation warning about this problem:
    
    One  of  the key elements of Dat privacy is that the public key
    is  never  used  in  any  discovery  network. The public key is
    hashed,  creating  the discovery key. Whenever peers attempt to
    connect to each other, they use the discovery key.
    
    Data  is  encrypted  using  the  public key, so it is important
    that this key stays secure.
    
    There  are  other  privacy  issues outlined in the document; it
    states that " Dat faces similar privacy risks as BitTorrent ":
    
    When  you download a dataset, your IP address is exposed to the
    users  sharing  that dataset. This may lead to honeypot servers
    collecting  IP addresses, as we've seen in Bittorrent. However,
    with  dataset  sharing we can create a web of trust model where
    specific  institutions  are  trusted  as  primary  sources  for
    datasets, diminishing the sharing of IP addresses.
    
    A  Dat  blog  post  refers to this issue as [116]reader privacy
    and  it is, indeed, a sensitive issue in peer-to-peer networks.
    It  is  how  BitTorrent  users  are discovered and served scary
    verbiage  from  lawyers, after all. But Dat makes this a little
    better  because,  to  join  a swarm, you must know what you are
    looking  for  already,  which means peers who can look at swarm
    activity  only  include  users  who know the secret public key.
    This  works  well  for  secret  content, but for larger, public
    data  sets, it is a real problem; it is why the Dat project has
    [117]avoided creating a Wikipedia mirror so far.
    
    I  found  another  privacy  issue that is not documented in the
    security  FAQ  during  my  review of the protocol. As mentioned
    earlier,  the [118]Dat discovery protocol routinely phones home
    to  DNS  servers operated by the Dat project. This implies that
    the  default  discovery  servers (and an attacker watching over
    their  traffic)  know  who is publishing or seeking content, in
    essence  discovering  the  "social  network"  behind  Dat. This
    discovery  mechanism  can be disabled in clients, but a similar
    privacy  issue  applies  to  the  DHT as well, although that is
    distributed  so  it  doesn't  require  trust of the Dat project
    itself.
    
    Considering  those  aspects  of the protocol, privacy-conscious
    users  will  probably  want  to  use Tor or other anonymization
    techniques to work around those concerns.
    
    The future of Dat
    
    [119]Dat  2.0  was  released  in  June  2017  with  performance
    improvements   and   protocol   changes.  [120]Dat  Enhancement
    Proposals  (DEPs)  guide the project's future development; most
    work  is  currently  geared  toward  implementing  the  draft "
    [121]multi-writer   proposal   "   in  [122]HyperDB  .  Without
    multi-writer  support, only the original publisher of a Dat can
    modify  it.  According  to  Joe  Hand, co-executive-director of
    [123]Code  for  Science & Society (CSS) and Dat core developer,
    in  an  IRC  chat, "supporting multiwriter is a big requirement
    for  lots  of  folks". For example, while Dat might allow Alice
    to  share  her  research  results with Bob, he cannot modify or
    contribute  back  to  those results. The multi-writer extension
    allows  for  Alice  to assign trust to Bob so he can have write
    access to the data.
    
    Unfortunately,  the  current  proposal doesn't solve the " hard
    problems  " of " conflict merges and secure key distribution ".
    The  former  will  be worked out through user interface tweaks,
    but  the  latter  is  a  classic problem that security projects
    have   typically   trouble  finding  solutions  for—Dat  is  no
    exception.  How  will Alice securely trust Bob? The OpenPGP web
    of  trust?  Hexadecimal  fingerprints  read over the phone? Dat
    doesn't provide a magic solution to this problem.
    
    Another  thing limiting adoption is that Dat is not packaged in
    any  distribution  that I could find (although I [124]requested
    it  in  Debian  )  and,  considering the speed of change of the
    JavaScript  ecosystem,  this  is  unlikely  to  change any time
    soon.  A  [125]Rust  implementation  of  the  Dat  protocol has
    started,  however,  which  might  be easier to package than the
    multitude  of  [126]Node.js  modules. In terms of mobile device
    support,  there is an experimental Android web browser with Dat
    support  called  [127]Bunsen  , which somehow doesn't run on my
    phone.  Some  adventurous  users  have  successfully run Dat in
    [128]Termux  .  I  haven't  found an app running on iOS at this
    point.
    
    Even  beyond  platform  support, distributed protocols like Dat
    have  a  tough  slope  to climb against the virtual monopoly of
    more  centralized  protocols,  so  it  remains  to  be seen how
    popular  those  tools  will  be.  Hand says Dat is supported by
    multiple  non-profit  organizations. Beyond CSS, [129]Blue Link
    Labs  is working on the Beaker Browser as a self-funded startup
    and  a  grass-roots  organization, [130]Digital Democracy , has
    contributed  to  the  project.  The  [131]Internet  Archive has
    [132]announced  a  collaboration  between  itself, CSS, and the
    California  Digital  Library to launch a pilot project to see "
    how   members  of  a  cooperative,  decentralized  network  can
    leverage  shared  services  to  ensure  data preservation while
    reducing storage costs and increasing replication counts ".
    
    Hand  said  adoption in academia has been "slow but steady" and
    that  the [133]Dat in the Lab project has helped identify areas
    that  could  help researchers adopt the project. Unfortunately,
    as  is  the case with many free-software projects, he said that
    "our  team is definitely a bit limited on bandwidth to push for
    bigger  adoption".  Hand said that the project received a grant
    from   [134]Mozilla   Open   Source   Support  to  improve  its
    documentation, which will be a big help.
    
    Ultimately,   Dat   suffers   from  a  problem  common  to  all
    peer-to-peer  applications,  which is naming. Dat addresses are
    not  exactly  intuitive:  humans  do not remember strings of 64
    hexadecimal  characters well. For this, Dat took a [135]similar
    approach  to IPFS by using DNS TXT records and /.well-known URL
    paths   to  bridge  existing,  human-readable  names  with  Dat
    hashes.  So  this sacrifices a part of the decentralized nature
    of the project in favor of usability.
    
    I  have  tested  a lot of distributed protocols like Dat in the
    past  and I am not sure Dat is a clear winner. It certainly has
    advantages  over IPFS in terms of usability and resource usage,
    but  the  lack  of packages on most platforms is a big limit to
    adoption  for  most  people. This means it will be difficult to
    share  content  with  my  friends  and  family with Dat anytime
    soon,  which  would  probably  be  my  primary use case for the
    project.  Until  the  protocol  reaches the wider adoption that
    BitTorrent  has  seen  in  terms  of  platform  support, I will
    probably   wait   before  switching  everything  over  to  this
    promising project.
    
    [136]Comments (11 posted)
    
    Page editor : Jonathan Corbet
    
    Inside this week's LWN.net Weekly Edition
    
    [137]Briefs  :  OpenSSH  7.8;  4.19-rc1;  Which stable?; Netdev
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    [138]Announcements  :  Newsletters;  events;  security updates;
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