The test seems to indicate that the sample is correct but it is not. Similar code could easily end up in production and might even run flawlessly for years.
We can start off by compiling the sample with -O3. Now, the sample hangs indefinitely. (The default is -O0, no optimization / debug-consistency, which is somewhat similar to making every variable volatile, which is the reason the test didn't reveal the code as unsafe.)
To get to the root cause, we have to inspect the generated assembly. First, the GCC 4.8.5 -O0 based x86_64 assembly corresponding to the un-optimized working binary:
// Thread B:
// shared = 42;
movq -8(%rbp), %rax
movq (%rax), %rax
movq $42, (%rax)
// Thread A:
// while (shared != 42) {
// }
.L11:
movq -32(%rbp), %rax # Check shared every iteration
cmpq $42, %rax
jne .L11
Thread B executes a simple store of the value 42 in shared.
Thread A reads shared for each loop iteration until the comparison indicates equality.
Now, we compare that to the -O3 outcome:
// Thread B:
// shared = 42;
movq 8(%rdi), %rax
movq $42, (%rax)
// Thread A:
// while (shared != 42) {
// }
cmpq $42, (%rsp) # check shared once
je .L87 # and skip the infinite loop or not
.L88:
jmp .L88 # infinite loop
.L87:
Optimizations associated with -O3 replaced the loop with a single comparison and, if not equal, an infinite loop to match the expected behavior. With GCC 10.2, the loop is optimized out. (Unlike C, infinite loops with no side-effects or volatile accesses are undefined behaviour in C++.)
The problem is that the compiler and its optimizer are not aware of the implementation's concurrency implications. Consequently, the conclusion needs to be that shared cannot change in thread A - the loop is equivalent to dead code. (Or to put it another way, data races are UB, and the optimizer is allowed to assume that the program doesn't encounter UB. If you're reading a non-atomic variable, that must mean nobody else is writing it. This is what allows compilers to hoist loads out of loops, and similarly sink stores, which are very valuable optimizations for the normal case of non-shared variables.)
The solution requires us to communicate to the compiler that shared is involved in inter-thread communication. One way to accomplish that may be volatile. While the actual meaning of volatile varies across compilers and guarantees, if any, are compiler-specific, the general consensus is that volatile prevents the compiler from optimizing volatile accesses in terms of register-based caching. This is essential for low-level code that interacts with hardware and has its place in concurrent programming, albeit with a downward trend due to the introduction of std::atomic.
With volatile int64_t shared, the generated instructions change as follows:
// Thread B:
// shared = 42;
movq 24(%rdi), %rax
movq $42, (%rax)
// Thread A:
// while (shared != 42) {
// }
.L87:
movq 8(%rsp), %rax
cmpq $42, %rax
jne .L87
The loop cannot be eliminated anymore as it must be assumed that shared changed even though there's no evidence of that in the form of code. As a result, the sample now works with -O3.
If volatile fixes the issue, why would you ever need std::atomic? Two aspects relevant for lock-free code are what makes std::atomic essential: memory operation atomicity and memory order.
To build the case for load/store atomicity, we review the generated assembly compiled with GCC4.8.5 -O3 -m32 (the 32-bit version) for volatile int64_t shared:
// Thread B:
// shared = 42;
movl 4(%esp), %eax
movl 12(%eax), %eax
movl $42, (%eax)
movl $0, 4(%eax)
// Thread A:
// while (shared != 42) {
// }
.L88: # do {
movl 40(%esp), %eax
movl 44(%esp), %edx
xorl $42, %eax
movl %eax, %ecx
orl %edx, %ecx
jne .L88 # } while(shared ^ 42 != 0);
For 32-bit x86 code generation, 64-bit loads and stores are usually split into two instructions. For single-threaded code, this is not an issue. For multi-threaded code, this means that another thread can see a partial result of the 64-bit memory operation, leaving room for unexpected inconsistencies that might not cause problems 100 percent of the time, but can occur at random and the probability of occurrence is heavily influenced by the surrounding code and software usage patterns. Even if GCC chose to generate instructions that guarantee atomicity by default, that still wouldn't affect other compilers and might not hold true for all supported platforms.
To guard against partial loads/stores in all circumstances and across all compilers and supported platforms, std::atomic can be employed. Let's review how std::atomic affects the generated assembly. The updated sample:
#include <atomic>
#include <cassert>
#include <cstdint>
#include <thread>
int main()
{
std::atomic<int64_t> shared;
std::thread thread([&shared]() {
shared.store(42, std::memory_order_relaxed);
});
while (shared.load(std::memory_order_relaxed) != 42) {
}
assert(shared.load(std::memory_order_relaxed) == 42);
thread.join();
return 0;
}
The generated 32-bit assembly based on GCC 10.2 (-O3: https://godbolt.org/z/8sPs55nzT):
// Thread B:
// shared.store(42, std::memory_order_relaxed);
movl $42, %ecx
xorl %ebx, %ebx
subl $8, %esp
movl 16(%esp), %eax
movl 4(%eax), %eax # function arg: pointer to shared
movl %ecx, (%esp)
movl %ebx, 4(%esp)
movq (%esp), %xmm0 # 8-byte reload
movq %xmm0, (%eax) # 8-byte store to shared
addl $8, %esp
// Thread A:
// while (shared.load(std::memory_order_relaxed) != 42) {
// }
.L9: # do {
movq -16(%ebp), %xmm1 # 8-byte load from shared
movq %xmm1, -32(%ebp) # copy to a dummy temporary
movl -32(%ebp), %edx
movl -28(%ebp), %ecx # and scalar reload
movl %edx, %eax
movl %ecx, %edx
xorl $42, %eax
orl %eax, %edx
jne .L9 # } while(shared.load() ^ 42 != 0);
To guarantee atomicity for loads and stores, the compiler emits an 8-byte SSE2 movq instruction (to/from the bottom half of a 128-bit SSE register). Additionally, the assembly shows that the loop remains intact even though volatile was removed.
By using std::atomic in the sample, it is guaranteed that
- std::atomic loads and stores are not subject to register-based caching
- std::atomic loads and stores do not allow partial values to be observed
The C++ standard doesn't talk about registers at all, but it does say:
Implementations should make atomic stores visible to atomic loads within a reasonable amount of time.
While that leaves room for interpretation, caching std::atomic loads across iterations, like triggered in our sample (without volatile or atomic) would clearly be a violation - the store might never become visible. Current compilers don't even optimize atomics within one block, like 2 accesses in the same iteration.
On x86, naturally-aligned loads/stores (where the address is a multiple of the load/store size) are atomic up to 8 bytes without special instructions. That's why GCC is able to use movq.
atomic<T> with a large T may not be supported directly by hardware, in which case the compiler can fall back to using a mutex.
A large T (e.g. the size of 2 registers) on some platforms might require an atomic RMW operation (if the compiler doesn't simply fall back to locking), which are sometimes provided with larger size than the largest efficient pure-load / pure-store that's guaranteed atomic. (e.g. on x86-64, lock cmpxchg16, or ARM ldrexd/strexd retry loop). Single-instruction atomic RMWs (like x86 uses) internally involve a cache line lock or a bus lock. For example, older versions of clang -m32 for x86 will use lock cmpxchg8b instead of movq for 8-byte pure-load or pure-store.
What's the second aspect mentioned above and what does std::memory_order_relaxed mean?
Both, the compiler and CPU can reorder memory operations to optimize efficiency. The primary constraint of reordering is that all loads and stores must appear to have been executed in the order given by the code (program order). Therefore, in case of inter-thread communication, the memory order must be take into account to establish the required order despite reordering attempts. The required memory order can be specified for std::atomic loads and stores. std::memory_order_relaxed does not impose any particular order.
Mutual exclusion primitives enforce a specific memory order (acquire-release order) so that memory operations stay in the lock scope and stores executed by previous lock owners are guaranteed to be visible to subsequent lock owners. Thus, using locks, all the aspects raised here are addressed simply by using the locking facility. As soon as you break out of the comfort locks provide, you have to be mindful of the consequences and the factors that affect concurrency correctness.
Being as explicit as possible about inter-thread communication is a good starting point so that the compiler is aware of the load/store context and can generate code accordingly. Whenever possible, prefer std::atomic<T> with std::memory_order_relaxed (unless the scenario calls for a specific memory order) to volatile T (and, of course, T). Also, whenever possible, prefer not to roll your own lock-free code to reduce code complexity and maximize the probability of correctness.
volatile. That part is basically a duplicate of Multithreading program stuck in optimized mode but runs normally in -O0, and also When to use volatile with multi threading? – Staneatomicis correct, others are UB with at least race accesses (even withvolatile). – Gratt