Shared Objects Cause Overhead Even In a Race (C1, C2)
Here's an interesting fact, and perhaps surprising: Writing to a shared memory object from multiple processors makes those processors wait for each other (C1) and incurs synchronization overhead (C2), even if you fail to do any locking or other synchronization that is, even in a race. To see why, let's dig a little.
If we change the Example 1 code to remove the locking, then we have a classic race condition:
// Example 2: Evil code for demonstration only // (closed course, professional driver) // Thread 1 Modify( x, thisWay ); // race condition // Thread 2 Modify( x, thatWay ); // race condition // Thread 3 Modify( x, anotherWay ); // race condition
Take a moment to consider this question: Have we really removed all waiting (row 1) and overhead (row 2)? Is the performance of this code now going to be the same as if the threads were using only unshared local objects?
Perhaps surprisingly, on mainstream multicore systems the answer is No. We've definitely removed the obvious and visible synchronization (column A in Table 1). But on mainstream multicore systems, we are still incurring invisible costs (column C) just because we're writing to a shared object through potentially complex levels of cache. Achieving cache coherence across the system inherently requires coordination and overhead, to hide the complexity of the memory hierarchy and maintain the illusion that the data only exists in one place (its expected memory location) when it actually exists in multiple places (the actual memory location plus all the copies in various caches).  For more about the memory hierarchy and its costs, see also .
Consider how threads 1-3 might execute on the first three cores of a typical modern mainstream multicore processor: an Intel Dunnington, whose simplified block diagram is shown in Figure 1. There are six CPU cores and three levels of cache on the die; each core has 96KB of private level 1 (L1) cache; each pair of processors shares 3MB of L2 cache; and all six processors share up to 16MB of L3 cache in common. (If you already have experience with NUMA architectures, bladed servers, and such, this may look very familiar. All mainstream commodity hardware will be basically NUMA from now on.)
When a core uses object
x, the memory containing the accessed parts of
x must be loaded from RAM up through the pyramid of cache leading to that core. In particular, caches manipulate memory in small contiguous chunks called cache lines, so asking for one byte requires loading the entire cache line containing that byte. In Figure 1, if cores 1, 2, and 3 request access to
x, the hardware has to load the cache line(s) containing
x into the processor-wide L3 cache, then into the two L2 caches for cores 1-2 and 3-4, then into the three L1 caches for cores 1, 2, and 3.
For an unshared or read-only shared memory location, it's enough to have each core load the cache line(s) containing that location all the way up into its local cache. So if
x were immutable and all the accesses were reads, we'd be done and there would be no contention; each core could simply load a copy of the data into its cache and read it independently.
But, in our case, cores 1-3 are modifying
x, and that's where it gets interesting because to write to a memory location a core must additionally have exclusive ownership of the cache line containing that location. While one core has exclusive use, all other cores trying to write the same memory location must wait and take turns that is, they must run serially. Conceptually, it's as if each cache line were protected by a hardware mutex, where only one core can hold the hardware lock on that cache line at a time. Here, let's say core 1 gets to go first: It acquires exclusive use of the cache line, which typically includes following some procedure for invalidating that cache line in at least core 3's L2 cache and possibly also in core 2's and core 3's L1 caches; it performs its update to the appropriate parts of the cache line, then flushes the result out to L2, L3, and RAM for other cores to see; and then, when memory has been sufficiently synchronized, it releases ownership of the cache line. In the meantime, cores 2 and 3 must wait idly (C1), waiting for their turn to reload the cache line with the new value and then perform the same exclusion dance in turn. The deeper the memory hierarchy, the "farther apart" two cores can be and the greater the cost of synchronizing them (C2). 
It can be discouraging to see how the overheads of writing to a shared memory location add up. To avoid race conditions, our software should be using mutexes or other synchronization, which means impeding threads' progress as they wait for each other (A1 in Table 1) and incurring the overhead of software synchronization operations (A2 in Table 1). But even in a race, the mere act of sharing a writable memory location causes contention in hardware: We block other cores' and threads' progress by incurring waiting on the memory location (C1), and we incur the cost of synchronization to flush and propagate each write to cores near and far across the rest of the cache hierarchy (C2).
All Sharing Is Bad -- Even of "Unshared" Objects (C1, C2)
It's bad enough that writing to the same shared object inherently throttles scalability, but what's worse is that writing to nearby objects can have the same effect thanks to false sharing.
False sharing (aka "ping-ponging") occurs when two different objects shared or not! happen to live in the same cache line, and the cache system treats them as a single lump for caching purposes. I've discussed some of the perils of false sharing before, in  and Example 4 of , but it arises again here: Sadly, the C1 and C2 costs of writing to shared memory can cost you even when you're not actually writing to the same shared object.
Figure 2 shows a sample memory layout where each row represents a 32-byte cache line; note that objects
obj3, and part of
obj4, all reside on the same line. This can cause a real performance problem if
obj4 are not intended to be shared together, because it is typically impossible for most mainstream multicore hardware to let two different cores simultaneously write to different parts of the same cache line without serializing those writes. For example, a core that wants to update (or in some cases even read from)
obj2 must wait for another core that happens to be concurrently writing to the first part of
obj4. So these two threads end up competing at least enough to get these writes serialized, even if they're supposed to be able to run fully concurrently -- if, say,
obj4 are protected by different mutexes or, worse still, because
obj4 are actually supposed to be unshared private objects used only by its respective thread!
Will shuffling objects around or adding padding significantly affect performance? It sure can. Here are two examples: First, in Example 4 of , I demonstrated code and performance measurements where merely adding padding increased scalability of a lock-free queue on a 24-core benchmark machine from peaking at 15 cores to peaking at over 20 cores. Second, a few days before I wrote this article, the PLINQ  team discovered that moving a single field of a data structure to get it off a popular cache line improved the scalability of one benchmark from 5.2x to 14.8x on a 16-core machine. Incidentally, you usually won't notice such scalability differences on machines with too few cores, because first you have to get enough hardware concurrency for the scalability to matter; on a 4-core machine, the improvement from that same change was only from 3.8x to 3.9x.
The moral(s) of the story? Joe Duffy put it well:
- Test on bigger machines wherever possible. [After all, a "big" machine today is probably similar to a "typical" machine your customer will be running tomorrow. -hs]
- Validate any performance fixes or experiments on the larger machines, else you will miss cache-related regressions and you might write off potential performance improvements that would have showed a lot of gain had you only run it on a big machine.
- False sharing is deadly. And even more so on big machines. We knew that, but you'd be surprised how tiny a change led to the cache problems: the movement of a single field [moving a single writable field out of a cache line that otherwise contained only read-only immutable data, so that the cache line went from "read-mostly" to "read-only" and the writer thread stopped interfering with otherwise-independent reader threads]. 
Today's performance profiling tools are poor at helping us find these penalties. For example, in all common profiling tools and simple utilities like Windows' Task Manager, your CPU appears busy even while idly waiting for memory due to miss latency and cache line exclusion, so that even when all cores appear to be pegged at 100% the cores may easily be waiting for memory 90% of the time or more. The industry is still working on providing decent tools to let programmers detect these and similar sharing-related performance issues, particularly those due to hardware effects.
Be aware of false sharing, and "if variables A and B are…liable to be used by two different threads, keep them on separate cache lines" to avoid penalizing scalability. 
Other Shared Memory Costs in Hardware (C1, C2, C3)
Is a shared read less expensive than a shared write? We saw that a shared write is expensive because it needs exclusion (C1), and must be propagated across the memory subsystem and conceptually requires a synchronization after the write (C2). We might expect that reads are cheaper, because they conceptually don't require exclusivity or propagation, and in general writes are more expensive than reads. But the answer depends on the processor: On many processors, shared reads are indeed cheaper, close or equal in cost to an unshared read; on other processors, in some circumstances even a shared read must incur full hardware synchronization overhead, such as that a sequentially consistent read requires a
hwsync instruction on current PowerPC processors. 
What about the cost CAS compared to a shared write? A CAS also requires exclusion (C1) and conceptually requires synchronization not only after, but also before, the operation (C2), so we would expect a CAS to be at least as expensive, and probably more expensive, than a shared write. That's usually the case, and on some processors the most efficient implementation of CAS is to generate a loop that can actually perform, not just one, but repeated heavyweight synchronizations (C3).  (Note that this isn't the same as the "CAS loop" lock-free coding technique I mentioned earlier, which meant writing an explicit loop in your code that performs repeated CAS operations; here I mean that the implementation of the CAS operation itself can have a loop buried inside by which it could be forced to retry and multiple heavy sync instructions each time the software tries to perform a single CAS.)
Other Invisible Costs in Hardware (C1, C2, C3)
We've focused on synchronizing memory operations, but there are other examples of costs in column C.
Consider two threads or processes sharing a spindle. If two threads or processes try to read or write files on the same hard disk drive, or read files on the same DVD-ROM, their operations will have to wait for each other (C1), much as processes do when scheduled on the same core. Additionally, every time the spindle device switches to serve a different user request, it will experience overhead for moving the (usually singular) head across the media and probably some rotational delay for the right part of the media to reach the head (C2), much as processes sharing a core experience will context switch overhead; the amount of time the clients each get access to the hardware will add up to less than 100% due to this overhead.
As mentioned under B2, sharing also inhibits optimizations at the hardware level (still C2). Having critical sections in code prevents processors from reordering instructions, and caches from performing optimizations, that would use memory and other resources more efficiently.
Finally, consider a very different kind of memory sharing: cache residency and eviction under contention (C3). Two threads or processes may share a cache in common at any level of the hierarchy; for example, in Figure 1, they may share only L3 in common (e.g., if running on cores 1 and 6), share both L3 and L2 in common (e.g., if running on cores 5 and 6), or share L3, L2, and even L1 in common (e.g. if running on the same core). Each of the two threads or processes has a given hot working set of data it needs to perform its current work. When the shared cache is big enough for one or both of the contending threads' hot data taken by itself, but not big enough for the total, then running the threads simultaneously means that memory will appear to be slower as they more often evict both their own and each other's data from the shared cache. For example, in Figure 1 and given two threads running memory-intensive inner loops each using 2MB of hot data, the threads can run on cores 1 and 6 without experiencing cache eviction interference, because 4MB fits into L3 cache (assume that the rest of the system's work fits in the remainder). If they run on cores 5 and 6, however, their demand for 4MB will contend for 3MB of L2 cache, which doesn't fit without regular eviction and reloading from L3. If they run on the same core, interleaved with context switches, then on each context switch from one thread to the other we can expect L1 cache to be completely evicted and reloaded, which is a kind of "undo/redo" C3 cost over and above the B2 cost of context switching.
|To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.|
Best of the Web
The earliest known C compiler by the legendary Dennis Ritchie has been published on the repository.
HTML5 Mobile Development: Seven Good Ideas (and Three Bad Ones)
All you need to know to get up and running... and programming on ARM
Amazon Web Services CTO says promised land of cloud computing requires a new generation of applications that follow different principles.
Eventually, the vast majority of Web applications will run on a platform-as-a-service, or PaaS, vendor's infrastructure. To help sort out the options, we sent out a matrix with more than 70 decision points to a variety of PaaS providers.