1This document provides options for those wishing to keep their
2memory-ordering lives simple, as is necessary for those whose domain
3is complex.  After all, there are bugs other than memory-ordering bugs,
4and the time spent gaining memory-ordering knowledge is not available
5for gaining domain knowledge.  Furthermore Linux-kernel memory model
6(LKMM) is quite complex, with subtle differences in code often having
7dramatic effects on correctness.
8
9The options near the beginning of this list are quite simple.  The idea
10is not that kernel hackers don't already know about them, but rather
11that they might need the occasional reminder.
12
13Please note that this is a generic guide, and that specific subsystems
14will often have special requirements or idioms.  For example, developers
15of MMIO-based device drivers will often need to use mb(), rmb(), and
16wmb(), and therefore might find smp_mb(), smp_rmb(), and smp_wmb()
17to be more natural than smp_load_acquire() and smp_store_release().
18On the other hand, those coming in from other environments will likely
19be more familiar with these last two.
20
21
22Single-threaded code
23====================
24
25In single-threaded code, there is no reordering, at least assuming
26that your toolchain and hardware are working correctly.  In addition,
27it is generally a mistake to assume your code will only run in a single
28threaded context as the kernel can enter the same code path on multiple
29CPUs at the same time.  One important exception is a function that makes
30no external data references.
31
32In the general case, you will need to take explicit steps to ensure that
33your code really is executed within a single thread that does not access
34shared variables.  A simple way to achieve this is to define a global lock
35that you acquire at the beginning of your code and release at the end,
36taking care to ensure that all references to your code's shared data are
37also carried out under that same lock.  Because only one thread can hold
38this lock at a given time, your code will be executed single-threaded.
39This approach is called "code locking".
40
41Code locking can severely limit both performance and scalability, so it
42should be used with caution, and only on code paths that execute rarely.
43After all, a huge amount of effort was required to remove the Linux
44kernel's old "Big Kernel Lock", so let's please be very careful about
45adding new "little kernel locks".
46
47One of the advantages of locking is that, in happy contrast with the
48year 1981, almost all kernel developers are very familiar with locking.
49The Linux kernel's lockdep (CONFIG_PROVE_LOCKING=y) is very helpful with
50the formerly feared deadlock scenarios.
51
52Please use the standard locking primitives provided by the kernel rather
53than rolling your own.  For one thing, the standard primitives interact
54properly with lockdep.  For another thing, these primitives have been
55tuned to deal better with high contention.  And for one final thing, it is
56surprisingly hard to correctly code production-quality lock acquisition
57and release functions.  After all, even simple non-production-quality
58locking functions must carefully prevent both the CPU and the compiler
59from moving code in either direction across the locking function.
60
61Despite the scalability limitations of single-threaded code, RCU
62takes this approach for much of its grace-period processing and also
63for early-boot operation.  The reason RCU is able to scale despite
64single-threaded grace-period processing is use of batching, where all
65updates that accumulated during one grace period are handled by the
66next one.  In other words, slowing down grace-period processing makes
67it more efficient.  Nor is RCU unique:  Similar batching optimizations
68are used in many I/O operations.
69
70
71Packaged code
72=============
73
74Even if performance and scalability concerns prevent your code from
75being completely single-threaded, it is often possible to use library
76functions that handle the concurrency nearly or entirely on their own.
77This approach delegates any LKMM worries to the library maintainer.
78
79In the kernel, what is the "library"?  Quite a bit.  It includes the
80contents of the lib/ directory, much of the include/linux/ directory along
81with a lot of other heavily used APIs.  But heavily used examples include
82the list macros (for example, include/linux/{,rcu}list.h), workqueues,
83smp_call_function(), and the various hash tables and search trees.
84
85
86Data locking
87============
88
89With code locking, we use single-threaded code execution to guarantee
90serialized access to the data that the code is accessing.  However,
91we can also achieve this by instead associating the lock with specific
92instances of the data structures.  This creates a "critical section"
93in the code execution that will execute as though it is single threaded.
94By placing all the accesses and modifications to a shared data structure
95inside a critical section, we ensure that the execution context that
96holds the lock has exclusive access to the shared data.
97
98The poster boy for this approach is the hash table, where placing a lock
99in each hash bucket allows operations on different buckets to proceed
100concurrently.  This works because the buckets do not overlap with each
101other, so that an operation on one bucket does not interfere with any
102other bucket.
103
104As the number of buckets increases, data locking scales naturally.
105In particular, if the amount of data increases with the number of CPUs,
106increasing the number of buckets as the number of CPUs increase results
107in a naturally scalable data structure.
108
109
110Per-CPU processing
111==================
112
113Partitioning processing and data over CPUs allows each CPU to take
114a single-threaded approach while providing excellent performance and
115scalability.  Of course, there is no free lunch:  The dark side of this
116excellence is substantially increased memory footprint.
117
118In addition, it is sometimes necessary to occasionally update some global
119view of this processing and data, in which case something like locking
120must be used to protect this global view.  This is the approach taken
121by the percpu_counter infrastructure. In many cases, there are already
122generic/library variants of commonly used per-cpu constructs available.
123Please use them rather than rolling your own.
124
125RCU uses DEFINE_PER_CPU*() declaration to create a number of per-CPU
126data sets.  For example, each CPU does private quiescent-state processing
127within its instance of the per-CPU rcu_data structure, and then uses data
128locking to report quiescent states up the grace-period combining tree.
129
130
131Packaged primitives: Sequence locking
132=====================================
133
134Lockless programming is considered by many to be more difficult than
135lock-based programming, but there are a few lockless design patterns that
136have been built out into an API.  One of these APIs is sequence locking.
137Although this APIs can be used in extremely complex ways, there are simple
138and effective ways of using it that avoid the need to pay attention to
139memory ordering.
140
141The basic keep-things-simple rule for sequence locking is "do not write
142in read-side code".  Yes, you can do writes from within sequence-locking
143readers, but it won't be so simple.  For example, such writes will be
144lockless and should be idempotent.
145
146For more sophisticated use cases, LKMM can guide you, including use
147cases involving combining sequence locking with other synchronization
148primitives.  (LKMM does not yet know about sequence locking, so it is
149currently necessary to open-code it in your litmus tests.)
150
151Additional information may be found in include/linux/seqlock.h.
152
153Packaged primitives: RCU
154========================
155
156Another lockless design pattern that has been baked into an API
157is RCU.  The Linux kernel makes sophisticated use of RCU, but the
158keep-things-simple rules for RCU are "do not write in read-side code"
159and "do not update anything that is visible to and accessed by readers",
160and "protect updates with locking".
161
162These rules are illustrated by the functions foo_update_a() and
163foo_get_a() shown in Documentation/RCU/whatisRCU.rst.  Additional
164RCU usage patterns maybe found in Documentation/RCU and in the
165source code.
166
167
168Packaged primitives: Atomic operations
169======================================
170
171Back in the day, the Linux kernel had three types of atomic operations:
172
1731.	Initialization and read-out, such as atomic_set() and atomic_read().
174
1752.	Operations that did not return a value and provided no ordering,
176	such as atomic_inc() and atomic_dec().
177
1783.	Operations that returned a value and provided full ordering, such as
179	atomic_add_return() and atomic_dec_and_test().  Note that some
180	value-returning operations provide full ordering only conditionally.
181	For example, cmpxchg() provides ordering only upon success.
182
183More recent kernels have operations that return a value but do not
184provide full ordering.  These are flagged with either a _relaxed()
185suffix (providing no ordering), or an _acquire() or _release() suffix
186(providing limited ordering).
187
188Additional information may be found in these files:
189
190Documentation/atomic_t.txt
191Documentation/atomic_bitops.txt
192Documentation/core-api/atomic_ops.rst
193Documentation/core-api/refcount-vs-atomic.rst
194
195Reading code using these primitives is often also quite helpful.
196
197
198Lockless, fully ordered
199=======================
200
201When using locking, there often comes a time when it is necessary
202to access some variable or another without holding the data lock
203that serializes access to that variable.
204
205If you want to keep things simple, use the initialization and read-out
206operations from the previous section only when there are no racing
207accesses.  Otherwise, use only fully ordered operations when accessing
208or modifying the variable.  This approach guarantees that code prior
209to a given access to that variable will be seen by all CPUs has having
210happened before any code following any later access to that same variable.
211
212Please note that per-CPU functions are not atomic operations and
213hence they do not provide any ordering guarantees at all.
214
215If the lockless accesses are frequently executed reads that are used
216only for heuristics, or if they are frequently executed writes that
217are used only for statistics, please see the next section.
218
219
220Lockless statistics and heuristics
221==================================
222
223Unordered primitives such as atomic_read(), atomic_set(), READ_ONCE(), and
224WRITE_ONCE() can safely be used in some cases.  These primitives provide
225no ordering, but they do prevent the compiler from carrying out a number
226of destructive optimizations (for which please see the next section).
227One example use for these primitives is statistics, such as per-CPU
228counters exemplified by the rt_cache_stat structure's routing-cache
229statistics counters.  Another example use case is heuristics, such as
230the jiffies_till_first_fqs and jiffies_till_next_fqs kernel parameters
231controlling how often RCU scans for idle CPUs.
232
233But be careful.  "Unordered" really does mean "unordered".  It is all
234too easy to assume ordering, and this assumption must be avoided when
235using these primitives.
236
237
238Don't let the compiler trip you up
239==================================
240
241It can be quite tempting to use plain C-language accesses for lockless
242loads from and stores to shared variables.  Although this is both
243possible and quite common in the Linux kernel, it does require a
244surprising amount of analysis, care, and knowledge about the compiler.
245Yes, some decades ago it was not unfair to consider a C compiler to be
246an assembler with added syntax and better portability, but the advent of
247sophisticated optimizing compilers mean that those days are long gone.
248Today's optimizing compilers can profoundly rewrite your code during the
249translation process, and have long been ready, willing, and able to do so.
250
251Therefore, if you really need to use C-language assignments instead of
252READ_ONCE(), WRITE_ONCE(), and so on, you will need to have a very good
253understanding of both the C standard and your compiler.  Here are some
254introductory references and some tooling to start you on this noble quest:
255
256Who's afraid of a big bad optimizing compiler?
257	https://lwn.net/Articles/793253/
258Calibrating your fear of big bad optimizing compilers
259	https://lwn.net/Articles/799218/
260Concurrency bugs should fear the big bad data-race detector (part 1)
261	https://lwn.net/Articles/816850/
262Concurrency bugs should fear the big bad data-race detector (part 2)
263	https://lwn.net/Articles/816854/
264
265
266More complex use cases
267======================
268
269If the alternatives above do not do what you need, please look at the
270recipes-pairs.txt file to peel off the next layer of the memory-ordering
271onion.
272