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Kernel development

Brief items

Kernel release status

The current 2.6 patch remains 2.6.24-rc5; no new -rc releases have been made over the last week. Fixes do continue to find their way into the mainline git repository, though.

The current -mm tree is 2.6.24-rc5-mm1. Recent changes to -mm include some significant device model changes; a number of subsystem trees have been dropped from this release due to patch conflicts.

The current stable 2.6 kernel is The big patch is, released on December 14, with several dozen fixes. The (December 14) and (December 18) releases contain small fixes for problems caused by

For older kernels: was released on December 14 with quite a few fixes.

2.4.36-rc1 was released on December 17 with a number of security-related fixes. The release also contains those fixes.

Comments (3 posted)

Kernel development news

Quotes of the week

Just for some context, I have...

  • 1,400-odd open bugzilla reports
  • 719 emails saved away in my emailed-bug-reports folder, all of which need to be gone through, asking originators to retest and re-report-if-unfixed.
  • A big ugly email titled "2.6.24-rc5-git1: Reported regressions from 2.6.23" in my inbox.

All of which makes it a bit inappropriate to be thinking about intrusive-looking new features.

Ho hum. Just send me the whole lot against rc5-mm1 and I'll stick it in there and we'll see what breaks.

-- Andrew Morton

ok, and given the time-shift and apparent season-shift i'll sit in the evening, watch the snowfall and think happy thoughts of kittens fetching nuclear-tipped uzis and hunting ueber-elite wireless developers to beat some humanity and compassion into them, ok?
-- Ingo Molnar

Comments (1 posted)

Short subjects: kerneloops, read-mostly, and port 80

By Jonathan Corbet
December 18, 2007
Kerneloops. Triage is an important part of a kernel developer's job. A project as large and as widely-used as the kernel will always generate more bug reports than can be realistically addressed in the amount of time which is available. So developers must figure out which reports are most deserving of their attention. Sometimes the existence of an irate, paying customer makes this decision easy. Other times, though, it is a matter of making a guess at which bugs are affecting the largest numbers of users. And that often comes down to how many different reports have come in for a given problem.

Of course, counting reports is not the easiest thing to do, especially if they are not all sent to the same place. In an attempt to make this process easier, Arjan van de Ven has announced a new site at Arjan has put together some software which scans certain sites and mailing lists for posted kernel oops output; whenever a crash is found, it is stuffed into a database. Then an attempt is made to associate reports with each other based on kernel version and the call trace; from that, a list of the most popular ways to crash can be created. As of this writing, the current fashion for kernel oopses would appear to be in ieee80211_tx() in current development kernels. Some other information is stored with the trace; in particular, it is possible to see what the oldest kernel version associated with the problem is.

This is clearly a useful resource, but there are a couple of problems which make it harder to do the job properly. One is that there is no distinctive marker which indicates the end of an oops listing, so the scripts have a hard time knowing where to stop grabbing information. The other is that multiple reports of the same oops can artificially raise the count for a particular crash. The solution to both problems is to place a marker at the end of the oops output which includes a random UUID generated at system boot time. Patches to this effect are circulating, though getting the random number into the output turns out to be a little harder than one might have expected. So, for 2.6.24, the "random" number may be all zeroes, with the real problem to be solved in 2.6.25.

Read-mostly. Anybody who digs through kernel source for any period of time will notice a number of variables declared in a form like this:

    static int __read_mostly ignore_loglevel;

The __read_mostly attribute says that accesses to this variable are usually (but not always) read operations. There were some questions recently about why this annotation is done; the answer is that it's an important optimization, though it may not always be having the effect that developers are hoping for.

As is well described in What every programmer should know about memory, proper use of processor memory caches is crucial for optimal performance. The idea behind __read_mostly is to group together variables which are rarely changed so they can all share cache lines which need not be bounced between processors on multiprocessor systems. As long as nobody changes a __read_mostly variable, it can reside in a shared cache line with other such variables and be present in cache (if needed) on all processors in the system.

The read-mostly attribute generally works well and yields a measurable performance improvement. There are concerns, though, that this feature could be over-used. Andrew Morton expressed it this way:

So... once we've moved all read-mostly variables into __read_mostly, what is left behind in bss? All the write-often variables. All optimally packed together to nicely maximise cacheline sharing.

Combining frequently-written variables into shared cache lines is a good way to maximize the bouncing of those cache lines between processors - which would be bad for performance. So over-aggressive segregation of read-mostly variables to minimize cache line bouncing could have the opposite of the desired effect: it could make the kernel's cache behavior worse.

The better way, says Andrew, would have been to create a "read often" attribute for variables which are frequently used in a read-only mode. That would leave behind the numerous read-rarely variables to serve as padding keeping the write-often variables nicely separated from each other. Thus far, patches to make this change have not been forthcoming.

I/O port delays. The functions provided by the kernel for access to I/O ports have long included versions which insert delays. A driver would normally read a byte from a port with inb(), but inb_p() could be used if an (unspecified) short delay was needed after the operation. A look through the driver tree shows that quite a few drivers use the delayed versions of the I/O port accessors, even though, in many cases, there is no real need for that delay.

This delay is implemented (on x86 architectures) with a write to I/O port 80. There is generally no hardware listening for an I/O operation on that port, so this write has the sole effect of delaying the processor while the bus goes through an abortive attempt to execute the operation. It is an operation with reasonably well-defined semantics, and it has worked for Linux for many years.

Except that now, it seems, this technique no longer works on a small subset of x86_64 systems. Instead, the write to port 80 will, on occasion, freeze the system hard; this, in turn, generates a rather longer delay than was intended. One could imagine the creation of an elaborate mechanism for restarting I/O operations after the user resets the system, but the kernel developers, instead, chose to look for alternative ways of implementing I/O delays.

In almost every case, the alternative form of the delay is a call to udelay(). The biggest problem here is that udelay() works by sitting in a tight loop; it cannot know how many times to go through the loop until the speed of the processor has been calibrated. That calibration happens reasonably early in the boot process, but there are still tasks to be performed - including I/O port operations - first. This problem is being worked around by removing some delayed operations from the early setup code, but some developers worry that it will never be possible to get them all. It has been suggested that the kernel could just assume it's running on the fastest-available processor until the calibration happens, but, beyond being somewhat inelegant, that could significantly slow the bootstrap process on slower machines - all of which work just fine with the current code.

The real solution is to simply get rid of almost all of the delayed I/O port operations. Very few of them are likely to be needed with any hardware which still works. In some cases, what may really be going on is that the delays are being used to paper over driver bugs - such as failing to force a needed PCI write out by doing a read operation. Just removing the delays outright would probably cause instability in unpredictable places - not a result most developers are striving for. So the task of cleaning up those calls will have to be done carefully over time. Meanwhile, the use of port 80 will probably remain unchanged for 2.6.24.

Comments (6 posted)

revoke() returns

By Jonathan Corbet
December 18, 2007
LWN last looked at Pekka Enberg's revoke() patch in July, 2006. The purpose of this proposed system call is to completely disconnect all processes from a specific file, thus allowing a new process to have exclusive access to that file. There are a number of applications for this functionality, such as ensuring that a newly logged-in user is the only one able to access resources associated with the console - the sound device, for example. There are kernel developers who occasionally mutter ominously about unfixable security problems resulting from the lack of the ability to revoke open file descriptors - though they tend, for some reason, to not want to publish the details of those vulnerabilities. Any sort of real malware scanning application will also need to be able to revoke access to files determined to contain Bad Stuff.

Pekka has recently posted a new version of the patch, so a new look seems warranted. The first thing one notes is that the revoke() system call is gone; instead, the new form of the system call is:

    int revokeat(int dir_fd, const char *filename);

This call thus follows the form of a number of other, relatively new *at() system calls. Here, filename is the name of the file for which access is to be revoked; if it is an absolute pathname then dir_fd is ignored. Otherwise, dir_fd is an open file descriptor for the directory to be used as the starting point in the lookup of filename. The special value AT_FDCWD indicates the current working directory for the calling process. If the revokeat() call completes successfully, only file descriptors for filename which are created after the call will be valid.

There is a new file_operations member created by this patch set:

    int (*revoke)(struct file *filp);

This function's job is to ensure that any outstanding I/O operations on the given file have completed, with a failure status if needed. So far, the only implementation is a generic version for filesystems; it is, in its entirety:

    int generic_file_revoke(struct file *file)
	return do_fsync(file, 1);

In the long term, revokeat() will need support from at least a subset of device drivers to be truly useful.

Disconnecting access to regular file descriptors is relatively straightforward; the system call simply iterates through the list of open files on the relevant device and replaces the file_operations structure with a new set which returns EBADF for every attempted operation. (OK, for almost every attempted operation - reads from sockets and device files return zero instead). The only tricky part is that it must iterate through the file list multiple times until no open files are found; otherwise there could be race conditions with other system calls creating new file descriptors at the same time that the old ones are being revoked.

The trickier part is dealing with memory mappings. In most cases, it is a matter of finding all virtual memory areas (VMAs) associated with the file, setting the new VM_REVOKED flag, and calling zap_page_range() to clear out the associated page table entries. The VM_REVOKED flag ensures that any attempt to fault pages back in will result in a SIGBUS signal - likely to be an unpleasant surprise for any process attempting to access that area.

Even trickier is the case of private, copy-on-write (COW) mappings, which can be created when a process forks. Simply clearing those mappings might be effective, but it could result in the death of processes which do not actually need to be killed. But it is important that the COW mapping not be a way to leak data written to the file after the revokeat() call. So the COW mappings are separated from each other by a simple (but expensive) call to get_user_pages(), which will create private copies of all of the relevant pages.

There has been relatively little discussion of this patch so far - perhaps the relevant developers have begun their holiday breaks and revoked their access to linux-kernel. This is an important patch with a lot of difficult, low-level operations, though; that is part of why it has been so long in the making. So it will need some comprehensive review before it can be considered ready for the mainline. Given the nature of the problem, it would not be surprising if another iteration or two were needed still.

Comments (1 posted)

What is RCU, Fundamentally?

December 17, 2007

This article was contributed by Paul McKenney

[Editor's note: this is the first in a three-part series on how the read-copy-update mechanism works. Many thanks to Paul McKenney and Jonathan Walpole for allowing us to publish these articles. The remaining two sections will appear in future weeks.]

Part 1 of 3 of What is RCU, Really?

Paul E. McKenney, IBM Linux Technology Center
Jonathan Walpole, Portland State University Department of Computer Science


Read-copy update (RCU) is a synchronization mechanism that was added to the Linux kernel in October of 2002. RCU achieves scalability improvements by allowing reads to occur concurrently with updates. In contrast with conventional locking primitives that ensure mutual exclusion among concurrent threads regardless of whether they be readers or updaters, or with reader-writer locks that allow concurrent reads but not in the presence of updates, RCU supports concurrency between a single updater and multiple readers. RCU ensures that reads are coherent by maintaining multiple versions of objects and ensuring that they are not freed up until all pre-existing read-side critical sections complete. RCU defines and uses efficient and scalable mechanisms for publishing and reading new versions of an object, and also for deferring the collection of old versions. These mechanisms distribute the work among read and update paths in such a way as to make read paths extremely fast. In some cases (non-preemptable kernels), RCU's read-side primitives have zero overhead.

Quick Quiz 1: But doesn't seqlock also permit readers and updaters to get work done concurrently?

This leads to the question "what exactly is RCU?", and perhaps also to the question "how can RCU possibly work?" (or, not infrequently, the assertion that RCU cannot possibly work). This document addresses these questions from a fundamental viewpoint; later installments look at them from usage and from API viewpoints. This last installment also includes a list of references.

RCU is made up of three fundamental mechanisms, the first being used for insertion, the second being used for deletion, and the third being used to allow readers to tolerate concurrent insertions and deletions. These mechanisms are described in the following sections, which focus on applying RCU to linked lists:

  1. Publish-Subscribe Mechanism (for insertion)
  2. Wait For Pre-Existing RCU Readers to Complete (for deletion)
  3. Maintain Multiple Versions of Recently Updated Objects (for readers)

These sections are followed by concluding remarks and the answers to the Quick Quizzes.

Publish-Subscribe Mechanism

One key attribute of RCU is the ability to safely scan data, even though that data is being modified concurrently. To provide this ability for concurrent insertion, RCU uses what can be thought of as a publish-subscribe mechanism. For example, consider an initially NULL global pointer gp that is to be modified to point to a newly allocated and initialized data structure. The following code fragment (with the addition of appropriate locking) might be used for this purpose:

  1 struct foo {
  2   int a;
  3   int b;
  4   int c;
  5 };
  6 struct foo *gp = NULL;
  8 /* . . . */
 10 p = kmalloc(sizeof(*p), GFP_KERNEL);
 11 p->a = 1;
 12 p->b = 2;
 13 p->c = 3;
 14 gp = p;

Unfortunately, there is nothing forcing the compiler and CPU to execute the last four assignment statements in order. If the assignment to gp happens before the initialization of p's fields, then concurrent readers could see the uninitialized values. Memory barriers are required to keep things ordered, but memory barriers are notoriously difficult to use. We therefore encapsulate them into a primitive rcu_assign_pointer() that has publication semantics. The last four lines would then be as follows:

  1 p->a = 1;
  2 p->b = 2;
  3 p->c = 3;
  4 rcu_assign_pointer(gp, p);

The rcu_assign_pointer() would publish the new structure, forcing both the compiler and the CPU to execute the assignment to gp after the assignments to the fields referenced by p.

However, it is not sufficient to only enforce ordering at the updater, as the reader must enforce proper ordering as well. Consider for example the following code fragment:

  1 p = gp;
  2 if (p != NULL) {
  3   do_something_with(p->a, p->b, p->c);
  4 }

Although this code fragment might well seem immune to misordering, unfortunately, the DEC Alpha CPU [PDF] and value-speculation compiler optimizations can, believe it or not, cause the values of p->a, p->b, and p->c to be fetched before the value of p! This is perhaps easiest to see in the case of value-speculation compiler optimizations, where the compiler guesses the value of p, fetches p->a, p->b, and p->c, then fetches the actual value of p in order to check whether its guess was correct. This sort of optimization is quite aggressive, perhaps insanely so, but does actually occur in the context of profile-driven optimization.

Clearly, we need to prevent this sort of skullduggery on the part of both the compiler and the CPU. The rcu_dereference() primitive uses whatever memory-barrier instructions and compiler directives are required for this purpose:

  1 rcu_read_lock();
  2 p = rcu_dereference(gp);
  3 if (p != NULL) {
  4   do_something_with(p->a, p->b, p->c);
  5 }
  6 rcu_read_unlock();

The rcu_dereference() primitive can thus be thought of as subscribing to a given value of the specified pointer, guaranteeing that subsequent dereference operations will see any initialization that occurred before the corresponding publish (rcu_assign_pointer()) operation. The rcu_read_lock() and rcu_read_unlock() calls are absolutely required: they define the extent of the RCU read-side critical section. Their purpose is explained in the next section, however, they never spin or block, nor do they prevent the list_add_rcu() from executing concurrently. In fact, in non-CONFIG_PREEMPT kernels, they generate absolutely no code.

Although rcu_assign_pointer() and rcu_dereference() can in theory be used to construct any conceivable RCU-protected data structure, in practice it is often better to use higher-level constructs. Therefore, the rcu_assign_pointer() and rcu_dereference() primitives have been embedded in special RCU variants of Linux's list-manipulation API. Linux has two variants of doubly linked list, the circular struct list_head and the linear struct hlist_head/struct hlist_node pair. The former is laid out as follows, where the green boxes represent the list header and the blue boxes represent the elements in the list.

Linux list

Adapting the pointer-publish example for the linked list gives the following:

  1 struct foo {
  2   struct list_head list;
  3   int a;
  4   int b;
  5   int c;
  6 };
  7 LIST_HEAD(head);
  9 /* . . . */
 11 p = kmalloc(sizeof(*p), GFP_KERNEL);
 12 p->a = 1;
 13 p->b = 2;
 14 p->c = 3;
 15 list_add_rcu(&p->list, &head);

Line 15 must be protected by some synchronization mechanism (most commonly some sort of lock) to prevent multiple list_add() instances from executing concurrently. However, such synchronization does not prevent this list_add() from executing concurrently with RCU readers.

Subscribing to an RCU-protected list is straightforward:

  1 rcu_read_lock();
  2 list_for_each_entry_rcu(p, head, list) {
  3   do_something_with(p->a, p->b, p->c);
  4 }
  5 rcu_read_unlock();

The list_add_rcu() primitive publishes an entry into the specified list, guaranteeing that the corresponding list_for_each_entry_rcu() invocation will properly subscribe to this same entry.

Quick Quiz 2: What prevents the list_for_each_entry_rcu() from getting a segfault if it happens to execute at exactly the same time as the list_add_rcu()?

Linux's other doubly linked list, the hlist, is a linear list, which means that it needs only one pointer for the header rather than the two required for the circular list. Thus, use of hlist can halve the memory consumption for the hash-bucket arrays of large hash tables.

Linux hlist

Publishing a new element to an RCU-protected hlist is quite similar to doing so for the circular list:

  1 struct foo {
  2   struct hlist_node *list;
  3   int a;
  4   int b;
  5   int c;
  6 };
  7 HLIST_HEAD(head);
  9 /* . . . */
 11 p = kmalloc(sizeof(*p), GFP_KERNEL);
 12 p->a = 1;
 13 p->b = 2;
 14 p->c = 3;
 15 hlist_add_head_rcu(&p->list, &head);

As before, line 15 must be protected by some sort of synchronization mechanism, for example, a lock.

Subscribing to an RCU-protected hlist is also similar to the circular list:

  1 rcu_read_lock();
  2 hlist_for_each_entry_rcu(p, q, head, list) {
  3   do_something_with(p->a, p->b, p->c);
  4 }
  5 rcu_read_unlock();

Quick Quiz 3: Why do we need to pass two pointers into hlist_for_each_entry_rcu() when only one is needed for list_for_each_entry_rcu()?

The set of RCU publish and subscribe primitives are shown in the following table, along with additional primitives to "unpublish", or retract:

Category Publish Retract Subscribe
Pointers rcu_assign_pointer() rcu_assign_pointer(..., NULL) rcu_dereference()
Lists list_add_rcu()
list_del_rcu() list_for_each_entry_rcu()
Hlists hlist_add_after_rcu()
hlist_del_rcu() hlist_for_each_entry_rcu()

Note that the list_replace_rcu(), list_del_rcu(), hlist_replace_rcu(), and hlist_del_rcu() APIs add a complication. When is it safe to free up the data element that was replaced or removed? In particular, how can we possibly know when all the readers have released their references to that data element?

These questions are addressed in the following section.

Wait For Pre-Existing RCU Readers to Complete

In its most basic form, RCU is a way of waiting for things to finish. Of course, there are a great many other ways of waiting for things to finish, including reference counts, reader-writer locks, events, and so on. The great advantage of RCU is that it can wait for each of (say) 20,000 different things without having to explicitly track each and every one of them, and without having to worry about the performance degradation, scalability limitations, complex deadlock scenarios, and memory-leak hazards that are inherent in schemes using explicit tracking.

In RCU's case, the things waited on are called "RCU read-side critical sections". An RCU read-side critical section starts with an rcu_read_lock() primitive, and ends with a corresponding rcu_read_unlock() primitive. RCU read-side critical sections can be nested, and may contain pretty much any code, as long as that code does not explicitly block or sleep (although a special form of RCU called "SRCU" does permit general sleeping in SRCU read-side critical sections). If you abide by these conventions, you can use RCU to wait for any desired piece of code to complete.

RCU accomplishes this feat by indirectly determining when these other things have finished, as has been described elsewhere for RCU Classic and realtime RCU.

In particular, as shown in the following figure, RCU is a way of waiting for pre-existing RCU read-side critical sections to completely finish, including memory operations executed by those critical sections.

periods extend to contain pre-existing RCU read-side critical sections.

However, note that RCU read-side critical sections that begin after the beginning of a given grace period can and will extend beyond the end of that grace period.

The following pseudocode shows the basic form of algorithms that use RCU to wait for readers:

  1. Make a change, for example, replace an element in a linked list.

  2. Wait for all pre-existing RCU read-side critical sections to completely finish (for example, by using the synchronize_rcu() primitive). The key observation here is that subsequent RCU read-side critical sections have no way to gain a reference to the newly removed element.

  3. Clean up, for example, free the element that was replaced above.

The following code fragment, adapted from those in the previous section, demonstrates this process, with field a being the search key:

  1 struct foo {
  2   struct list_head list;
  3   int a;
  4   int b;
  5   int c;
  6 };
  7 LIST_HEAD(head);
  9 /* . . . */
 11 p = search(head, key);
 12 if (p == NULL) {
 13   /* Take appropriate action, unlock, and return. */
 14 }
 15 q = kmalloc(sizeof(*p), GFP_KERNEL);
 16 *q = *p;
 17 q->b = 2;
 18 q->c = 3;
 19 list_replace_rcu(&p->list, &q->list);
 20 synchronize_rcu();
 21 kfree(p);

Lines 19, 20, and 21 implement the three steps called out above. Lines 16-19 gives RCU ("read-copy update") its name: while permitting concurrent reads, line 16 copies and lines 17-19 do an update.

The synchronize_rcu() primitive might seem a bit mysterious at first. After all, it must wait for all RCU read-side critical sections to complete, and, as we saw earlier, the rcu_read_lock() and rcu_read_unlock() primitives that delimit RCU read-side critical sections don't even generate any code in non-CONFIG_PREEMPT kernels!

There is a trick, and the trick is that RCU Classic read-side critical sections delimited by rcu_read_lock() and rcu_read_unlock() are not permitted to block or sleep. Therefore, when a given CPU executes a context switch, we are guaranteed that any prior RCU read-side critical sections will have completed. This means that as soon as each CPU has executed at least one context switch, all prior RCU read-side critical sections are guaranteed to have completed, meaning that synchronize_rcu() can safely return.

Thus, RCU Classic's synchronize_rcu() can conceptually be as simple as the following:

  1 for_each_online_cpu(cpu)
  2   run_on(cpu);

Here, run_on() switches the current thread to the specified CPU, which forces a context switch on that CPU. The for_each_online_cpu() loop therefore forces a context switch on each CPU, thereby guaranteeing that all prior RCU read-side critical sections have completed, as required. Although this simple approach works for kernels in which preemption is disabled across RCU read-side critical sections, in other words, for non-CONFIG_PREEMPT and CONFIG_PREEMPT kernels, it does not work for CONFIG_PREEMPT_RT realtime (-rt) kernels. Therefore, realtime RCU uses a different approach based loosely on reference counters.

Of course, the actual implementation in the Linux kernel is much more complex, as it is required to handle interrupts, NMIs, CPU hotplug, and other hazards of production-capable kernels, but while also maintaining good performance and scalability. Realtime implementations of RCU must additionally help provide good realtime response, which rules out implementations (like the simple two-liner above) that rely on disabling preemption.

Although it is good to know that there is a simple conceptual implementation of synchronize_rcu(), other questions remain. For example, what exactly do RCU readers see when traversing a concurrently updated list? This question is addressed in the following section.

Maintain Multiple Versions of Recently Updated Objects

This section demonstrates how RCU maintains multiple versions of lists to accommodate synchronization-free readers. Two examples are presented showing how an element that might be referenced by a given reader must remain intact while that reader remains in its RCU read-side critical section. The first example demonstrates deletion of a list element, and the second example demonstrates replacement of an element.

Example 1: Maintaining Multiple Versions During Deletion

To start the "deletion" example, we will modify lines 11-21 in the example in the previous section as follows:

  1 p = search(head, key);
  2 if (p != NULL) {
  3   list_del_rcu(&p->list);
  4   synchronize_rcu();
  5   kfree(p);
  6 }

The initial state of the list, including the pointer p, is as follows.

Initial list

The triples in each element represent the values of fields a, b, and c, respectively. The red borders on each element indicate that readers might be holding references to them, and because readers do not synchronize directly with updaters, readers might run concurrently with this entire replacement process. Please note that we have omitted the backwards pointers and the link from the tail of the list to the head for clarity.

After the list_del_rcu() on line 3 has completed, the 5,6,7 element has been removed from the list, as shown below. Since readers do not synchronize directly with updaters, readers might be concurrently scanning this list. These concurrent readers might or might not see the newly removed element, depending on timing. However, readers that were delayed (e.g., due to interrupts, ECC memory errors, or, in CONFIG_PREEMPT_RT kernels, preemption) just after fetching a pointer to the newly removed element might see the old version of the list for quite some time after the removal. Therefore, we now have two versions of the list, one with element 5,6,7 and one without. The border of the 5,6,7 element is still red, indicating that readers might be referencing it.


Please note that readers are not permitted to maintain references to element 5,6,7 after exiting from their RCU read-side critical sections. Therefore, once the synchronize_rcu() on line 4 completes, so that all pre-existing readers are guaranteed to have completed, there can be no more readers referencing this element, as indicated by its black border below. We are thus back to a single version of the list.

After deletion.

At this point, the 5,6,7 element may safely be freed, as shown below:

After deletion.

At this point, we have completed the deletion of element 5,6,7. The following section covers replacement.

Example 2: Maintaining Multiple Versions During Replacement

To start the replacement example, here are the last few lines of the example in the previous section:

  1 q = kmalloc(sizeof(*p), GFP_KERNEL);
  2 *q = *p;
  3 q->b = 2;
  4 q->c = 3;
  5 list_replace_rcu(&p->list, &q->list);
  6 synchronize_rcu();
  7 kfree(p);

The initial state of the list, including the pointer p, is the same as for the deletion example:

Initial list state.

As before, the triples in each element represent the values of fields a, b, and c, respectively. The red borders on each element indicate that readers might be holding references to them, and because readers do not synchronize directly with updaters, readers might run concurrently with this entire replacement process. Please note that we again omit the backwards pointers and the link from the tail of the list to the head for clarity.

Line 1 kmalloc()s a replacement element, as follows:

List state after

Line 2 copies the old element to the new one:

List state after

Line 3 updates q->b to the value "2":

List state after
update of b.

Line 4 updates q->c to the value "3":

List state after
update of c.

Now, line 5 does the replacement, so that the new element is finally visible to readers. At this point, as shown below, we have two versions of the list. Pre-existing readers might see the 5,6,7 element, but new readers will instead see the 5,2,3 element. But any given reader is guaranteed to see some well-defined list.

List state after

After the synchronize_rcu() on line 6 returns, a grace period will have elapsed, and so all reads that started before the list_replace_rcu() will have completed. In particular, any readers that might have been holding references to the 5,6,7 element are guaranteed to have exited their RCU read-side critical sections, and are thus prohibited from continuing to hold a reference. Therefore, there can no longer be any readers holding references to the old element, as indicated by the thin black border around the 5,6,7 element below. As far as the readers are concerned, we are back to having a single version of the list, but with the new element in place of the old.

List state after
grace period.

After the kfree() on line 7 completes, the list will appear as follows:

List state after
grace period.

Despite the fact that RCU was named after the replacement case, the vast majority of RCU usage within the Linux kernel relies on the simple deletion case shown in the previous section.


These examples assumed that a mutex was held across the entire update operation, which would mean that there could be at most two versions of the list active at a given time.

Quick Quiz 4: How would you modify the deletion example to permit more than two versions of the list to be active?

Quick Quiz 5: How many RCU versions of a given list can be active at any given time?

This sequence of events shows how RCU updates use multiple versions to safely carry out changes in presence of concurrent readers. Of course, some algorithms cannot gracefully handle multiple versions. There are techniques [PDF] for adapting such algorithms to RCU, but these are beyond the scope of this article.


This article has described the three fundamental components of RCU-based algorithms:

  1. a publish-subscribe mechanism for adding new data,

  2. a way of waiting for pre-existing RCU readers to finish, and

  3. a discipline of maintaining multiple versions to permit change without harming or unduly delaying concurrent RCU readers.

Quick Quiz 6: How can RCU updaters possibly delay RCU readers, given that the rcu_read_lock() and rcu_read_unlock() primitives neither spin nor block?

These three RCU components allow data to be updated in face of concurrent readers, and can be combined in different ways to implement a surprising variety of different types of RCU-based algorithms, some of which will be the topic of the next installment in this "What is RCU, Really?" series.


We are all indebted to Andy Whitcroft, Gautham Shenoy, and Mike Fulton, whose review of an early draft of this document greatly improved it. We owe thanks to the members of the Relativistic Programming project and to members of PNW TEC for many valuable discussions. We are grateful to Dan Frye for his support of this effort. Finally, this material is based upon work supported by the National Science Foundation under Grant No. CNS-0719851.

This work represents the view of the authors and does not necessarily represent the view of IBM or of Portland State University.

Linux is a registered trademark of Linus Torvalds.

Other company, product, and service names may be trademarks or service marks of others.

Answers to Quick Quizzes

Quick Quiz 1: But doesn't seqlock also permit readers and updaters to get work done concurrently?

Answer: Yes and no. Although seqlock readers can run concurrently with seqlock writers, whenever this happens, the read_seqretry() primitive will force the reader to retry. This means that any work done by a seqlock reader running concurrently with a seqlock updater will be discarded and redone. So seqlock readers can run concurrently with updaters, but they cannot actually get any work done in this case.

In contrast, RCU readers can perform useful work even in presence of concurrent RCU updaters.

Quick Quiz 2: What prevents the list_for_each_entry_rcu() from getting a segfault if it happens to execute at exactly the same time as the list_add_rcu()?

Answer: On all systems running Linux, loads from and stores to pointers are atomic, that is, if a store to a pointer occurs at the same time as a load from that same pointer, the load will return either the initial value or the value stored, never some bitwise mashup of the two. In addition, the list_for_each_entry_rcu() always proceeds forward through the list, never looking back. Therefore, the list_for_each_entry_rcu() will either see the element being added by list_add_rcu(), or it will not, but either way, it will see a valid well-formed list.

Back to Quick Quiz 2.

Quick Quiz 3: Why do we need to pass two pointers into hlist_for_each_entry_rcu() when only one is needed for list_for_each_entry_rcu()?

Answer: Because in an hlist it is necessary to check for NULL rather than for encountering the head. (Try coding up a single-pointer hlist_for_each_entry_rcu(). If you come up with a nice solution, it would be a very good thing!)

Back to Quick Quiz 3.

Quick Quiz 4: How would you modify the deletion example to permit more than two versions of the list to be active?

Answer: One way of accomplishing this is as follows:

p = search(head, key);
if (p == NULL)
else {

Note that this means that multiple concurrent deletions might be waiting in synchronize_rcu().

Back to Quick Quiz 4.

Quick Quiz 5: How many RCU versions of a given list can be active at any given time?

Answer: That depends on the synchronization design. If a semaphore protecting the update is held across the grace period, then there can be at most two versions, the old and the new.

However, if only the search, the update, and the list_replace_rcu() were protected by a lock, then there could be an arbitrary number of versions active, limited only by memory and by how many updates could be completed within a grace period. But please note that data structures that are updated so frequently probably are not good candidates for RCU. That said, RCU can handle high update rates when necessary.

Back to Quick Quiz 5.

Quick Quiz 6: How can RCU updaters possibly delay RCU readers, given that the rcu_read_lock() and rcu_read_unlock() primitives neither spin nor block?

Answer: The modifications undertaken by a given RCU updater will cause the corresponding CPU to invalidate cache lines containing the data, forcing the CPUs running concurrent RCU readers to incur expensive cache misses. (Can you design an algorithm that changes a data structure without inflicting expensive cache misses on concurrent readers? On subsequent readers?)

Back to Quick Quiz 6.

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