WHY IS THE STACK SO FAST?

00:13:45
https://www.youtube.com/watch?v=N3o5yHYLviQ

摘要

TLDRIn this video, George, from Core Dumped, explains the efficiency and limitations of using the memory stack in low-level programming. The stack, a last-in-first-out data structure, helps in managing local variables and function calls by pre-allocating a memory region at program start, avoiding the costly on-the-fly memory requests to the operating system. Specifying variable sizes at compile time is crucial for performance, as it helps the compiler organize data efficiently, reducing fragmentation and increasing cache hit chances. However, the stack has limitations, including fixed size and inability to dynamically manage variable sizes, which can lead to stack overflow errors, especially in recursive functions. Multithreading further complicates stack use since each thread requires its own stack, posing challenges for memory sharing. The video underscores the importance of understanding these intricacies to optimize programming and hints at future discussions on the heap, an alternative to the stack for dynamic memory management.

心得

  • 🔍 Specifying variable sizes helps optimize compiler efficiency and program speed.
  • 📏 The stack operates on a Last In, First Out basis and is pre-allocated at program start.
  • ⚠️ Stack overflow occurs when recursion or excess variables exceed stack size limits.
  • 🚀 Memory allocation on the stack is quick due to minimal OS involvement.
  • 🛑 The stack lacks flexibility for dynamic resizing of stored variables.
  • 🔗 Each thread in multi-threaded programs requires its own stack, posing memory challenges.
  • 📈 Cache optimization involves keeping data compact, aiding in faster CPU access.
  • 🔄 Stack acts as a memory control hub during function calls and local variable handling.
  • 🗂️ Understanding stack behavior is key in low-level programming for performance gains.
  • 🔧 Future videos will explore the heap as a dynamic memory management solution.

时间轴

  • 00:00:00 - 00:05:00

    In the previous video, George discussed how specifying variable sizes can enhance program speed but introduced the concept of dynamically growing or shrinking types at runtime, which presents challenges in low-level programming as compilers require explicit size declarations. This leads to limitations with arrays needing specified sizes that persist throughout the program’s lifetime. The video moves on to explain the stack, a memory region known for speed due to its Last-In-First-Out structure, although it's limited in size, leading to potential overflow. It clarifies that the stack being discussed is the execution stack of a program, not a software stack, and delves into how operating systems manage memory allocation, highlighting issues like external fragmentation if data isn't efficiently organized. The video also touches on how performance considerations impact where and how data is stored, foreshadowing a deeper dive into heap memory management in future videos.

  • 00:05:00 - 00:13:45

    George continues by addressing performance issues, even with modern CPUs, due to memory being a bottleneck. He explores how using cache, a smaller, faster memory, can mitigate these issues by storing copies of frequently accessed data. He then aligns the use of a stack with these ideas, showing how pre-allocating a small memory space at program start avoids frequent memory requests, keeping data compact for efficient cache use. Functions use stack frames to manage memory for local variables, which are removed post-execution. Consequently, specifying exact sizes is crucial for compilers to generate efficient code, particularly when handling stack allocation and recursion which may lead to stack overflow. He concludes by acknowledging stack limitations, such as finite size and single-thread operational constraints, setting up a segue to future discussions on heap memory which offers more flexibility for dynamic data structures.

思维导图

Mind Map

常见问题

  • Why should I specify the size of variables in programming?

    Specifying the size of variables helps compilers optimize data organization and efficiency, reducing fragmentation and improving performance.

  • What is a stack in computer programming?

    A stack is a memory structure following Last In, First Out (LIFO) principle, which is used for storing local variables and function call management.

  • What causes a stack overflow error?

    Stack overflow occurs when the stack exceeds its size limit, often due to excessive recursion or improper memory management.

  • Why is memory allocation on the stack faster?

    Stack allocation is faster because the stack pointer provides constant guidance, and memory addresses are calculated quickly without OS intervention.

  • What limitations does the stack have?

    The stack has a size constraint, lacks dynamic growth flexibility, and imposes limitations in multi-threaded contexts where each thread needs its own stack.

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  • 00:00:00
    in my previous video I talked about how
  • 00:00:02
    providing the size of your variables in
  • 00:00:03
    your code can help compilers speed up
  • 00:00:05
    your programs I ended that video by
  • 00:00:08
    mentioning that you might want your
  • 00:00:09
    types to grow or Shrink dynamically at
  • 00:00:11
    runtime but that's a problem because in
  • 00:00:14
    low-level programming languages
  • 00:00:16
    compilers complain when you are not
  • 00:00:17
    explicit about sizes so you can't
  • 00:00:21
    declare custom types with arrays unless
  • 00:00:23
    you give the array a specific size but
  • 00:00:26
    that means your array will have that
  • 00:00:28
    size during the entire lifetime of your
  • 00:00:29
    program
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    by the end of this video you will
  • 00:00:32
    understand the reasons behind this
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    limitation hi friends my name is George
  • 00:00:38
    and this is core dumped today we are
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    going to take a look at the stack that
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    memory region that everybody tells you
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    is super fast and you should always try
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    to put data on it but almost nobody
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    tells you why a stack is a data
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    structure that follows the last in first
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    out principle this means that the last
  • 00:00:56
    element added to the stack is the first
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    one to be removed
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    usually its size is limited so if we
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    push too many elements and run out of
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    space we say the stack has overflowed
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    which may sound familiar to you although
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    recently the site is dead due to the
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    rise of large language models like me I
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    mean like
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    gp4 Anyway the stack in this video is
  • 00:01:18
    not a software stack although there is a
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    relationship as you'll see what we are
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    studying right now is the execution
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    stack sometimes called the program stack
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    or even the hardware stack
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    when we run a program theoretically
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    there is nothing preventing us from
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    writing our values anywhere in memory
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    even though we are specifying the size
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    of our values randomly placing them
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    could result in wasted space to
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    comprehend this it's crucial to consider
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    the role of the operating system you
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    might be familiar with the concept that
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    the operating system always acts as an
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    intermediary between programs and
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    Hardware well memory is no
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    exception when a program is executed it
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    cannot simply begin storing data
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    anywhere in physical memory as other
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    programs might already be utilizing some
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    of that space therefore before it must
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    request memory from the operating system
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    the operating system in turn will search
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    for available spaces where our program
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    can write if we try to read from or
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    write to a memory address that the
  • 00:02:19
    operating system hasn't allocated to our
  • 00:02:21
    program it will promptly terminate the
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    program's execution for safety
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    reasons this is when we encounter the
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    infamous error known as segmentation
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    fault core dumped from which this
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    channel derives its
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    name the important part here is that the
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    operating system doesn't allocate memory
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    precisely to the amount we need instead
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    it provides a memory chunk where we can
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    freely read and
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    write however if we don't organize our
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    data and just place it anywhere we might
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    face this issue where there is
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    sufficient free memory but we can't
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    store data due to the lack of contiguous
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    space this problem is commonly referred
  • 00:03:00
    to as external
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    fragmentation in this case the only way
  • 00:03:06
    to store our value in memory is to
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    request another
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    chunk if we had organized our data more
  • 00:03:15
    efficiently there would be no
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    requirement to request additional
  • 00:03:20
    memory it's worth noting that asking for
  • 00:03:22
    more memory can be considerably
  • 00:03:23
    expensive in terms of performance as a
  • 00:03:26
    brief preview this is why many recommend
  • 00:03:28
    minimizing the use of the Heap we'll
  • 00:03:30
    delve deeper into this topic in my
  • 00:03:32
    upcoming video about the Heap so
  • 00:03:33
    consider subscribing for more
  • 00:03:35
    details furthermore when the operating
  • 00:03:38
    system allocates memory to our program
  • 00:03:40
    it doesn't track whether we utilize that
  • 00:03:42
    memory or not its sole awareness is that
  • 00:03:44
    the specified memory region belongs to
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    our program if another program requires
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    memory it will have to put its data
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    somewhere else even if there is enough
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    free contiguous memory in our program
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    memory
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    region but memory is finite
  • 00:04:01
    so what do we do if all the memory is
  • 00:04:03
    already in use by other
  • 00:04:05
    programs once again the answer lies with
  • 00:04:07
    the operating system modern operating
  • 00:04:10
    systems are specifically designed to
  • 00:04:11
    address this type of issue in the
  • 00:04:14
    absence of available memory they
  • 00:04:15
    leverage storage space as if it were
  • 00:04:17
    additional
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    memory have you ever seen that partition
  • 00:04:21
    called swap in Linux systems well now
  • 00:04:24
    you know what it
  • 00:04:26
    does this is what we call virtual memory
  • 00:04:29
    and it is very useful because it not
  • 00:04:31
    only creates the illusion of having much
  • 00:04:32
    more memory than is actually present but
  • 00:04:35
    it also gives the appearance of writing
  • 00:04:36
    directly to physical memory so as
  • 00:04:39
    programmers we don't have to worry about
  • 00:04:41
    all the underlying Concepts I could
  • 00:04:44
    spend 30 minutes talking about virtual
  • 00:04:45
    memory but that's out of the scope of
  • 00:04:47
    this video however it's important to
  • 00:04:50
    note that relying on storage to extend
  • 00:04:51
    main memory is a double-edged sword
  • 00:04:54
    storage is thousands of times slower
  • 00:04:56
    than RAM and this feature that operating
  • 00:04:58
    systems provide should be used with with
  • 00:05:00
    caution in my previous video Someone
  • 00:05:03
    commented that in modern days the size
  • 00:05:05
    of our data in memory might not seem
  • 00:05:07
    crucial due to the virtually unlimited
  • 00:05:09
    memory available while it's true that
  • 00:05:11
    memory appears Limitless for us as
  • 00:05:13
    programmers it's essential to exercise
  • 00:05:15
    caution and consider that performance
  • 00:05:17
    still plays a significant
  • 00:05:19
    role and speaking of performance it
  • 00:05:22
    turns out that even memory can be a
  • 00:05:24
    bottleneck despite the immense speed of
  • 00:05:26
    modern CPUs fetching data from memory
  • 00:05:28
    incurs a noticeable
  • 00:05:30
    cost to address this manufacturers
  • 00:05:33
    integrate a feature called cache into
  • 00:05:35
    modern chips cache is essentially a
  • 00:05:38
    small dedicated memory inside the
  • 00:05:40
    processor where a copy of a memory
  • 00:05:42
    region is stored this way when the CPU
  • 00:05:45
    requires data for manipulation or an
  • 00:05:47
    operation it can promptly retrieve it
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    from the cache instead of searching for
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    it in the main
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    memory when the necessary information is
  • 00:05:55
    present in the cache we refer to it as a
  • 00:05:58
    cach hit however if the required data is
  • 00:06:01
    not in the cache it results in a cache
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    Miss in such cases the CPU is compelled
  • 00:06:08
    to wait for the data to be fetched from
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    the main memory incurring additional
  • 00:06:11
    time once again this is completely
  • 00:06:14
    invisible to us the programmers in this
  • 00:06:16
    case is the hardware and not the
  • 00:06:18
    operating system who decides what memory
  • 00:06:20
    region is kept in
  • 00:06:22
    Cache if we care about performance the
  • 00:06:25
    best we can do is to keep our data as
  • 00:06:27
    compact as possible this this way there
  • 00:06:30
    is a higher probability that it will fit
  • 00:06:32
    in the cache this proximity increases
  • 00:06:35
    the chances of a cash
  • 00:06:37
    hit this new concept of keeping data as
  • 00:06:40
    close as possible to the CPU is what
  • 00:06:42
    low-level people know as locality and
  • 00:06:45
    there you have the second reason to keep
  • 00:06:46
    data compact
  • 00:06:49
    performance now you might be thinking
  • 00:06:51
    what the hell all of this has to do with
  • 00:06:53
    the stack well it turns out if we want
  • 00:06:55
    to keep data compact a good way to
  • 00:06:57
    organize things is using a stack let's
  • 00:07:00
    take a look the first thing we can do to
  • 00:07:02
    speed things up is to pre-allocate
  • 00:07:04
    memory when our program starts this way
  • 00:07:07
    every time we need to store some value
  • 00:07:09
    we already have somewhere to write it
  • 00:07:11
    since our goal here is to avoid
  • 00:07:13
    requesting memory from the operating
  • 00:07:15
    system on the Fly the size of this
  • 00:07:17
    pre-allocated region remains fixed
  • 00:07:19
    throughout the entire lifetime of our
  • 00:07:21
    program therefore we must be careful
  • 00:07:23
    when determining its size while there's
  • 00:07:25
    nothing preventing us from allocating
  • 00:07:27
    gigabytes to this region it's essential
  • 00:07:29
    to consider the Simplicity of our
  • 00:07:30
    program if it only uses a couple of
  • 00:07:33
    variables it may never fully occupy such
  • 00:07:35
    a large memory space as mentioned
  • 00:07:38
    earlier this would result in memory
  • 00:07:40
    wastage as the operating system would
  • 00:07:42
    Reserve that free space for our program
  • 00:07:45
    preventing other programs from utilizing
  • 00:07:46
    it for this reason the size of this
  • 00:07:49
    pre-allocated region is typically kept
  • 00:07:51
    small the exact size depends on the
  • 00:07:54
    compiler we are using but generally it
  • 00:07:56
    won't exceed a few megabytes this might
  • 00:07:59
    seem seem insufficient at first glance
  • 00:08:01
    but if you do the math in only 1
  • 00:08:02
    Megabyte you can fit more than 13,
  • 00:08:05
    64-bit
  • 00:08:09
    integers when our program starts the
  • 00:08:11
    main function serves as our entry point
  • 00:08:14
    each time a new variable is encountered
  • 00:08:16
    its value is stacked into this
  • 00:08:17
    designated region this is where the term
  • 00:08:20
    the stack becomes apparent the memory
  • 00:08:22
    address marking the beginning of the
  • 00:08:24
    stack is referred to as the stack origin
  • 00:08:26
    while the stack pointer indicates the
  • 00:08:28
    current topmost data on the
  • 00:08:30
    stack of course the stack pointer is
  • 00:08:32
    stored somewhere it can be done in
  • 00:08:34
    memory but modern architectures
  • 00:08:36
    incorporate a register exclusively
  • 00:08:38
    dedicated to holding the stack pointer
  • 00:08:40
    value directly within the CPU
  • 00:08:42
    consequently the processor doesn't need
  • 00:08:44
    to wait for it to be fetched from memory
  • 00:08:46
    or even from the
  • 00:08:47
    cache one of the reasons why memory
  • 00:08:49
    allocation on the stack is highly
  • 00:08:51
    efficient lies in the constant guidance
  • 00:08:53
    provided by the stack pointer to write
  • 00:08:55
    something onto the stack all that's
  • 00:08:57
    required is to fetch the stack pointer
  • 00:08:58
    address address add one to that value
  • 00:09:01
    and the result is the memory address
  • 00:09:03
    where we can write more data and that's
  • 00:09:04
    it that's all it takes to allocate
  • 00:09:06
    memory on the stack as you'll discover
  • 00:09:08
    in my upcoming video this process is not
  • 00:09:11
    as straightforward when allocating
  • 00:09:12
    memory on the Heap in that scenario we
  • 00:09:15
    must request memory from the operating
  • 00:09:17
    system wait for it to locate available
  • 00:09:19
    space and due to the system not
  • 00:09:21
    returning the exact amount of needed
  • 00:09:22
    memory we must employ fancy strategies
  • 00:09:25
    to avoid fragmentation and all of those
  • 00:09:27
    are extra steps that make the process
  • 00:09:30
    slower when a function is called all its
  • 00:09:33
    local values are pushed onto the stack
  • 00:09:36
    but it's crucial to remember the
  • 00:09:37
    starting point of these local values
  • 00:09:39
    because when the function concludes we
  • 00:09:41
    need to reset the stack pointer to its
  • 00:09:43
    position just before the function calls
  • 00:09:46
    these distinct sub regions are known as
  • 00:09:48
    stack
  • 00:09:51
    frames when a function being called is
  • 00:09:53
    expected to return a value such as in
  • 00:09:55
    this example we allocate a distinct
  • 00:09:57
    space called the return link following
  • 00:09:59
    this we start pushing our local
  • 00:10:03
    values upon the function's conclusion
  • 00:10:05
    the return value is directly written
  • 00:10:07
    into the return link subsequently the
  • 00:10:10
    stack frame of the Cali function is
  • 00:10:11
    removed except for the returning value
  • 00:10:14
    which now integrates into the stack
  • 00:10:15
    frame of the caller function here it's
  • 00:10:18
    important to note that for the compiler
  • 00:10:19
    to generate efficient machine code it
  • 00:10:21
    requires knowledge of the exact size of
  • 00:10:24
    the return link this is why systems
  • 00:10:26
    programming languages mandate specifying
  • 00:10:28
    the size of the Val value a function is
  • 00:10:30
    returning which you do by providing a
  • 00:10:32
    return
  • 00:10:34
    type finally let's consider some
  • 00:10:36
    important limitations in this example we
  • 00:10:39
    write some values in the stack a number
  • 00:10:42
    an array then more
  • 00:10:45
    numbers and let's say then we want to
  • 00:10:47
    add some elements to the array see the
  • 00:10:50
    problem
  • 00:10:52
    here adding elements to an array on the
  • 00:10:55
    stack means we might have to overwrite
  • 00:10:57
    other values that we might need
  • 00:11:00
    later this is exactly what I talked
  • 00:11:02
    about in my previous
  • 00:11:04
    video the compiler will complain if you
  • 00:11:06
    don't specify the size of any array even
  • 00:11:09
    if the array is a member of a custom
  • 00:11:12
    type sorry if I made you wait a lot for
  • 00:11:14
    this answer but I think everything I've
  • 00:11:15
    talked about up to this point was
  • 00:11:17
    necessary to understand
  • 00:11:19
    this when you specify the size of an
  • 00:11:21
    array the compiler gains precise
  • 00:11:23
    knowledge about the amount of space
  • 00:11:25
    required to accommodate that value in
  • 00:11:27
    the
  • 00:11:27
    stack
  • 00:11:30
    but to the finite size of the stack even
  • 00:11:32
    with provided sizes exceeding this limit
  • 00:11:34
    can lead to a program crash resulting in
  • 00:11:36
    a stack Overflow
  • 00:11:39
    error this scenario is particularly
  • 00:11:41
    pertinent when dealing with recursion
  • 00:11:43
    when a function calls itself new stack
  • 00:11:45
    frames are successively pushed into the
  • 00:11:47
    stack for each recursive call if a base
  • 00:11:50
    case isn't defined to Halt the recursion
  • 00:11:52
    the program will keep adding stack
  • 00:11:54
    frames until it overflows furthermore
  • 00:11:57
    even with a base case a stack overflow
  • 00:11:59
    can occur if the program doesn't reach
  • 00:12:01
    it before surpassing the Stack's
  • 00:12:03
    capacity hence there are instances where
  • 00:12:06
    using iterative Solutions is recommended
  • 00:12:08
    over recursive ones it's essential to
  • 00:12:11
    clarify that the behavior of the stack
  • 00:12:12
    is influenced by the programming
  • 00:12:14
    language and its compiler what you've
  • 00:12:16
    seen in this video might not precisely
  • 00:12:18
    mirror the implementation in every
  • 00:12:20
    language therefore it's important to
  • 00:12:22
    understand that I aim to simplify
  • 00:12:24
    concepts for the purpose of illustration
  • 00:12:26
    in these
  • 00:12:27
    videos in summary
  • 00:12:29
    today we learned that maintaining
  • 00:12:30
    compact data is crucial for reducing
  • 00:12:33
    memory usage by avoiding fragmentation
  • 00:12:35
    and enhancing performance through an
  • 00:12:37
    increased probability of cach a hits
  • 00:12:39
    specifying the size of our variables
  • 00:12:41
    enables compilers to organize our data
  • 00:12:43
    more efficiently and predictably in the
  • 00:12:45
    stack this organization facilitates the
  • 00:12:48
    handling of function calls and local
  • 00:12:49
    variables in a seamless and Speedy
  • 00:12:51
    manner we also delved into the
  • 00:12:53
    limitations of the stack it possesses a
  • 00:12:56
    size constraint and lacks the
  • 00:12:57
    flexibility for types to Dynam
  • 00:12:58
    dynamically grow or Shrink Additionally
  • 00:13:01
    the stack operates as a single-threaded
  • 00:13:03
    entity in the context of multi-threading
  • 00:13:06
    each thread necessitates its own stack
  • 00:13:08
    posing a limitation when attempting to
  • 00:13:10
    share memory between threads all these
  • 00:13:13
    challenges find a common solution in
  • 00:13:14
    what is known as the Heap a topic that
  • 00:13:16
    we will explore in a future video and
  • 00:13:19
    that is a very very simplified version
  • 00:13:21
    of how the stack Works hopefully you now
  • 00:13:24
    have an idea of why it is so fast if you
  • 00:13:27
    learned something hit that like button
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    that will help me a lot making these
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    videos is kind of timec consuming but
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    I've been enjoying it so consider
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    subscribing if you want to learn more
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    it's free and by the way this is where I
  • 00:13:40
    say goodbye because as a free AI model I
  • 00:13:43
    have a token limit of 250
标签
  • stack
  • memory management
  • compilers
  • performance
  • recursion
  • fragmentation
  • cache
  • operating system
  • low-level programming
  • threading