Previously we looked at how the kernel manages virtual memory for a user process, but files and I/O were left out. This post covers the important and often misunderstood relationship between files and memory and its consequences for performance.
Two serious problems must be solved by the OS when it comes to files. The first one is the mind-blowing slowness of hard drives, and disk seeks in particular, relative to memory. The second is the need to load file contents in physical memory once and share the contents among programs. If you use Process Explorer to poke at Windows processes, you’ll see there are ~15MB worth of common DLLs loaded in every process. My Windows box right now is running 100 processes, so without sharing I’d be using up to ~1.5 GB of physical RAM just for common DLLs. No good. Likewise, nearly all Linux programs need ld.so and libc, plus other common libraries.
Happily, both problems can be dealt with in one shot: the page cache, where the kernel stores page-sized chunks of files. To illustrate the page cache, I’ll conjure a Linux program named render, which opens file scene.dat and reads it 512 bytes at a time, storing the file contents into a heap-allocated block. The first read goes like this:
After 12KB have been read, render‘s heap and the relevant page frames look thus:
This looks innocent enough, but there’s a lot going on. First, even though this program uses regular read calls, three 4KB page frames are now in the page cache storing part of scene.dat. People are sometimes surprised by this, but all regular file I/O happens through the page cache. In x86 Linux, the kernel thinks of a file as a sequence of 4KB chunks. If you read a single byte from a file, the whole 4KB chunk containing the byte you asked for is read from disk and placed into the page cache. This makes sense because sustained disk throughput is pretty good and programs normally read more than just a few bytes from a file region. The page cache knows the position of each 4KB chunk within the file, depicted above as #0, #1, etc. Windows uses 256KB views analogous to pages in the Linux page cache.
Sadly, in a regular file read the kernel must copy the contents of the page cache into a user buffer, which not only takes cpu time and hurts the cpu caches, but also wastes physical memory with duplicate data. As per the diagram above, the scene.dat contents are stored twice, and each instance of the program would store the contents an additional time. We’ve mitigated the disk latency problem but failed miserably at everything else. Memory-mapped files are the way out of this madness:
When you use file mapping, the kernel maps your program’s virtual pages directly onto the page cache. This can deliver a significant performance boost: Windows System Programming reports run time improvements of 30% and up relative to regular file reads, while similar figures are reported for Linux and Solaris in Advanced Programming in the Unix Environment. You might also save large amounts of physical memory, depending on the nature of your application.
As always with performance, measurement is everything, but memory mapping earns its keep in a programmer’s toolbox. The API is pretty nice too, it allows you to access a file as bytes in memory and does not require your soul and code readability in exchange for its benefits. Mind your address space and experiment with mmap in Unix-like systems, CreateFileMapping in Windows, or the many wrappers available in high level languages. When you map a file its contents are not brought into memory all at once, but rather on demand via page faults. The fault handler maps your virtual pages onto the page cache after obtaining a page frame with the needed file contents. This involves disk I/O if the contents weren’t cached to begin with.
Now for a pop quiz. Imagine that the last instance of our render program exits. Would the pages storing scene.dat in the page cache be freed immediately? People often think so, but that would be a bad idea. When you think about it, it is very common for us to create a file in one program, exit, then use the file in a second program. The page cache must handle that case. When you think more about it, why should the kernel ever get rid of page cache contents? Remember that disk is 5 orders of magnitude slower than RAM, hence a page cache hit is a huge win. So long as there’s enough free physical memory, the cache should be kept full. It is therefore not dependent on a particular process, but rather it’s a system-wide resource. If you run render a week from now and scene.dat is still cached, bonus! This is why the kernel cache size climbs steadily until it hits a ceiling. It’s not because the OS is garbage and hogs your RAM, it’s actually good behavior because in a way free physical memory is a waste. Better use as much of the stuff for caching as possible.
Due to the page cache architecture, when a program calls write() bytes are simply copied to the page cache and the page is marked dirty. Disk I/O normally does not happen immediately, thus your program doesn’t block waiting for the disk. On the downside, if the computer crashes your writes will never make it, hence critical files like database transaction logs must be fsync()ed (though one must still worry about drive controller caches, oy!). Reads, on the other hand, normally block your program until the data is available. Kernels employ eager loading to mitigate this problem, an example of which is read ahead where the kernel preloads a few pages into the page cache in anticipation of your reads. You can help the kernel tune its eager loading behavior by providing hints on whether you plan to read a file sequentially or randomly (see madvise(), readahead(), Windows cache hints). Linux does read-ahead for memory-mapped files, but I’m not sure about Windows. Finally, it’s possible to bypass the page cache using O_DIRECT in Linux or NO_BUFFERING in Windows, something database software often does.
A file mapping may be private or shared. This refers only to updates made to the contents in memory: in a private mapping the updates are not committed to disk or made visible to other processes, whereas in a shared mapping they are. Kernels use the copy on write mechanism, enabled by page table entries, to implement private mappings. In the example below, both render and another program called render3d (am I creative or what?) have mapped scene.dat privately. Render then writes to its virtual memory area that maps the file:
The read-only page table entries shown above do not mean the mapping is read only, they’re merely a kernel trick to share physical memory until the last possible moment. You can see how ‘private’ is a bit of a misnomer until you remember it only applies to updates. A consequence of this design is that a virtual page that maps a file privately sees changes done to the file by other programs as long as the page has only been read from. Once copy-on-write is done, changes by others are no longer seen. This behavior is not guaranteed by the kernel, but it’s what you get in x86 and makes sense from an API perspective. By contrast, a shared mapping is simply mapped onto the page cache and that’s it. Updates are visible to other processes and end up in the disk. Finally, if the mapping above were read-only, page faults would trigger a segmentation fault instead of copy on write.
Dynamically loaded libraries are brought into your program’s address space via file mapping. There’s nothing magical about it, it’s the same private file mapping available to you via regular APIs. Below is an example showing part of the address spaces from two running instances of the file-mapping render program, along with physical memory, to tie together many of the concepts we’ve seen.
This concludes our 3-part series on memory fundamentals. I hope the series was useful and provided you with a good mental model of these OS topics. Next week there’s one more post on memory usage figures, and then it’s time for a change of air. Maybe some Web 2.0 gossip or something.
I hope the previous post explained virtual memory adequately, but I must admit I held back a much better explanation, which I first saw in Expert C Programming. It wasn’t written by the book’s author, Peter van der Linden, but rather by Jeff Berryman in 1972. Here goes:
The Thing King and the Paging Game
This note is a formal non-working paper of the Project MAC Computer Systems Research Division. It should be reproduced and distributed wherever levity is lacking, and may be referenced at your own risk in other publications.
- Each player gets several million things.
- Things are kept in crates that hold 4096 things each. Things in the same crate are called crate-mates.
- Crates are stored either in the workshop or the warehouses. The workshop is almost always too small to hold all the crates.
- There is only one workshop but there may be several warehouses. Everybody shares them.
- Each thing has its own thing number.
- What you do with a thing is to zark it. Everybody takes turns zarking.
- You can only zark your things, not anybody else’s.
- Things can only be zarked when they are in the workshop.
- Only the Thing King knows whether a thing is in the workshop or in a warehouse.
- The longer a thing goes without being zarked, the grubbier it is said to become.
- The way you get things is to ask the Thing King. He only gives out things by the crateful. This is to keep the royal overhead down.
- The way you zark a thing is to give its thing number. If you give the number of a thing that happens to be in a workshop it gets zarked right away. If it is in a warehouse, the Thing King packs the crate containing your thing back into the workshop. If there is no room in the workshop, he first finds the grubbiest crate in the workshop, whether it be yours or somebody else’s, and packs it off with all its crate-mates to a warehouse. In its place he puts the crate containing your thing. Your thing then gets zarked and you never know that it wasn’t in the workshop all along.
- Each player’s stock of things have the same numbers as everybody else’s. The Thing King always knows who owns what thing and whose turn it is, so you can’t ever accidentally zark somebody else’s thing even if it has the same thing number as one of yours.
- Traditionally, the Thing King sits at a large, segmented table and is attended to by pages (the so-called “table pages”) whose job it is to help the king remember where all the things are and who they belong to.
- One consequence of Rule 13 is that everybody’s thing numbers will be similar from game to game, regardless of the number of players.
- The Thing King has a few things of his own, some of which move back and forth between workshop and warehouse just like anybody else’s, but some of which are just too heavy to move out of the workshop.
- With the given set of rules, oft-zarked things tend to get kept mostly in the workshop while little-zarked things stay mostly in a warehouse. This is efficient stock control.
Long Live the Thing King!
Update: Alex pointed out the difficulties of measuring grubbiness in a comment below.
After examining the virtual address layout of a process, we turn to the kernel and its mechanisms for managing user memory. Here is gonzo again:
Linux processes are implemented in the kernel as instances of task_struct, the process descriptor. The mm field in task_struct points to the memory descriptor, mm_struct, which is an executive summary of a program’s memory. It stores the start and end of memory segments as shown above, the number of physical memory pages used by the process (rss stands for Resident Set Size), the amount of virtual address space used, and other tidbits. Within the memory descriptor we also find the two work horses for managing program memory: the set of virtual memory areas and the page tables. Gonzo’s memory areas are shown below:
Each virtual memory area (VMA) is a contiguous range of virtual addresses; these areas never overlap. An instance of vm_area_struct fully describes a memory area, including its start and end addresses, flags to determine access rights and behaviors, and the vm_file field to specify which file is being mapped by the area, if any. A VMA that does not map a file is anonymous. Each memory segment above (e.g., heap, stack) corresponds to a single VMA, with the exception of the memory mapping segment. This is not a requirement, though it is usual in x86 machines. VMAs do not care which segment they are in.
A program’s VMAs are stored in its memory descriptor both as a linked list in the mmap field, ordered by starting virtual address, and as a red-black tree rooted at the mm_rb field. The red-black tree allows the kernel to search quickly for the memory area covering a given virtual address. When you read file /proc/pid_of_process/maps, the kernel is simply going through the linked list of VMAs for the process and printing each one.
In Windows, the EPROCESS block is roughly a mix of task_struct and mm_struct. The Windows analog to a VMA is the Virtual Address Descriptor, or VAD; they are stored in an AVL tree. You know what the funniest thing about Windows and Linux is? It’s the little differences.
The 4GB virtual address space is divided into pages. x86 processors in 32-bit mode support page sizes of 4KB, 2MB, and 4MB. Both Linux and Windows map the user portion of the virtual address space using 4KB pages. Bytes 0-4095 fall in page 0, bytes 4096-8191 fall in page 1, and so on. The size of a VMA must be a multiple of page size. Here’s 3GB of user space in 4KB pages:
The processor consults page tables to translate a virtual address into a physical memory address. Each process has its own set of page tables; whenever a process switch occurs, page tables for user space are switched as well. Linux stores a pointer to a process’ page tables in the pgd field of the memory descriptor. To each virtual page there corresponds one page table entry (PTE) in the page tables, which in regular x86 paging is a simple 4-byte record shown below:
Linux has functions to read and set each flag in a PTE. Bit P tells the processor whether the virtual page is present in physical memory. If clear (equal to 0), accessing the page triggers a page fault. Keep in mind that when this bit is zero, the kernel can do whatever it pleases with the remaining fields. The R/W flag stands for read/write; if clear, the page is read-only. Flag U/S stands for user/supervisor; if clear, then the page can only be accessed by the kernel. These flags are used to implement the read-only memory and protected kernel space we saw before.
Bits D and A are for dirty and accessed. A dirty page has had a write, while an accessed page has had a write or read. Both flags are sticky: the processor only sets them, they must be cleared by the kernel. Finally, the PTE stores the starting physical address that corresponds to this page, aligned to 4KB. This naive-looking field is the source of some pain, for it limits addressable physical memory to 4 GB. The other PTE fields are for another day, as is Physical Address Extension.
A virtual page is the unit of memory protection because all of its bytes share the U/S and R/W flags. However, the same physical memory could be mapped by different pages, possibly with different protection flags. Notice that execute permissions are nowhere to be seen in the PTE. This is why classic x86 paging allows code on the stack to be executed, making it easier to exploit stack buffer overflows (it’s still possible to exploit non-executable stacks using return-to-libc and other techniques). This lack of a PTE no-execute flag illustrates a broader fact: permission flags in a VMA may or may not translate cleanly into hardware protection. The kernel does what it can, but ultimately the architecture limits what is possible.
Virtual memory doesn’t store anything, it simply maps a program’s address space onto the underlying physical memory, which is accessed by the processor as a large block called the physical address space. While memory operations on the bus are somewhat involved, we can ignore that here and assume that physical addresses range from zero to the top of available memory in one-byte increments. This physical address space is broken down by the kernel into page frames. The processor doesn’t know or care about frames, yet they are crucial to the kernel because the page frame is the unit of physical memory management. Both Linux and Windows use 4KB page frames in 32-bit mode; here is an example of a machine with 2GB of RAM:
In Linux each page frame is tracked by a descriptor and several flags. Together these descriptors track the entire physical memory in the computer; the precise state of each page frame is always known. Physical memory is managed with the buddy memory allocation technique, hence a page frame is free if it’s available for allocation via the buddy system. An allocated page frame might be anonymous, holding program data, or it might be in the page cache, holding data stored in a file or block device. There are other exotic page frame uses, but leave them alone for now. Windows has an analogous Page Frame Number (PFN) database to track physical memory.
Let’s put together virtual memory areas, page table entries and page frames to understand how this all works. Below is an example of a user heap:
Blue rectangles represent pages in the VMA range, while arrows represent page table entries mapping pages onto page frames. Some virtual pages lack arrows; this means their corresponding PTEs have the Present flag clear. This could be because the pages have never been touched or because their contents have been swapped out. In either case access to these pages will lead to page faults, even though they are within the VMA. It may seem strange for the VMA and the page tables to disagree, yet this often happens.
A VMA is like a contract between your program and the kernel. You ask for something to be done (memory allocated, a file mapped, etc.), the kernel says “sure”, and it creates or updates the appropriate VMA. But it does not actually honor the request right away, it waits until a page fault happens to do real work. The kernel is a lazy, deceitful sack of scum; this is the fundamental principle of virtual memory. It applies in most situations, some familiar and some surprising, but the rule is that VMAs record what has been agreed upon, while PTEs reflect what has actually been done by the lazy kernel. These two data structures together manage a program’s memory; both play a role in resolving page faults, freeing memory, swapping memory out, and so on. Let’s take the simple case of memory allocation:
When the program asks for more memory via the brk() system call, the kernel simply updates the heap VMA and calls it good. No page frames are actually allocated at this point and the new pages are not present in physical memory. Once the program tries to access the pages, the processor page faults and do_page_fault() is called. It searches for the VMA covering the faulted virtual address using find_vma(). If found, the permissions on the VMA are also checked against the attempted access (read or write). If there’s no suitable VMA, no contract covers the attempted memory access and the process is punished by Segmentation Fault.
When a VMA is found the kernel must handle the fault by looking at the PTE contents and the type of VMA. In our case, the PTE shows the page is not present. In fact, our PTE is completely blank (all zeros), which in Linux means the virtual page has never been mapped. Since this is an anonymous VMA, we have a purely RAM affair that must be handled by do_anonymous_page(), which allocates a page frame and makes a PTE to map the faulted virtual page onto the freshly allocated frame.
Things could have been different. The PTE for a swapped out page, for example, has 0 in the Present flag but is not blank. Instead, it stores the swap location holding the page contents, which must be read from disk and loaded into a page frame by do_swap_page() in what is called a major fault.
This concludes the first half of our tour through the kernel’s user memory management. In the next post, we’ll throw files into the mix to build a complete picture of memory fundamentals, including consequences for performance.