Memory Ordering in Modern Microprocessors, Part I
Since the 2.0 kernel release, Linux has supported a large number of SMP systems based on a variety of CPUs. Linux has done an excellent job of abstracting differences among these CPUs, even in kernel code. This article is an overview of one important difference: how CPUs allow memory accesses to be reordered in SMP systems.
Memory accesses are among the slowest of a CPU's operations, due to the fact that Moore's law has increased CPU instruction performance at a much greater rate than it has increased memory performance. This difference in performance increase means that memory operations have been getting increasingly expensive compared to simple register-to-register instructions. Modern CPUs sport increasingly large caches in order to reduce the overhead of these expensive memory accesses.
These caches can be thought of as simple hardware hash tables with fixed size buckets and no chaining, as shown in Figure 1. This cache has 16 lines and two ways for a total of 32 entries, each entry containing a single 256-byte cache line, which is a 256-byte-aligned block of memory. This cache line size is a little on the large size, but it makes the hexadecimal arithmetic much simpler. In hardware parlance, this is a two-way set-associative cache. It is analogous to a software hash table with 16 buckets, where each bucket's hash chain is limited to two elements at most. Because this cache is implemented in hardware, the hash function is extremely simple: extract four bits from the memory address.
In Figure 1, each box corresponds to a cache entry that can contain a 256-byte cache line. However, a cache entry can be empty, as indicated by the empty boxes in the figure. The rest of the boxes are flagged with the memory address of the cache line they contain. Because the cache lines must be 256-byte aligned, the low eight bits of each address are zero. The choice of hardware hash function means the next-higher four bits match the line number.
The situation depicted in Figure 1 might arise if the program's code was located at address 0x43210E00 through 0x43210EFF, and this program accessed data sequentially from 0x12345000 through 0x12345EFF. Suppose that the program now was to access location 0x12345F00. This location hashes to line 0xF, and both ways of this line are empty, so the corresponding 256-byte line can be accommodated. If the program was to access location 0x1233000, which hashes to line 0x0, the corresponding 256-byte cache line can be accommodated in way 1. However, if the program were to access location 0x1233E00, which hashes to line 0xE, one of the existing lines must be ejected from the cache to make room for the new cache line. This background on hardware caching allows us to look at why CPUs reorder memory accesses.
In a word, performance! CPUs have become so fast that the large multimegabyte caches cannot keep up with them. Therefore, caches often are partitioned into nearly independent banks, as shown in Figure 2. This allows each of the banks to run in parallel, thus keeping up better with the CPU. Memory normally is divided among the cache banks by address. For example, all the even-numbered cache lines might be processed by bank 0 and all of the odd-numbered cache lines by bank 1.
However, this hardware parallelism has a dark side: memory operations now can complete out of order, which can result in some confusion, as illustrated in Figure 3. CPU 0 might write first to location 0x12345000, an even-numbered cache line, and then to location 0x12345100, an odd-numbered cache line. If bank 0 is busy with earlier requests but bank 1 is idle, the first write is visible to CPU 1 after the second write. In other words, the writes are perceived out of order by CPU 1. Reads can be reordered in a similar manner. This reordering can cause many textbook parallel algorithms to fail.
A few machines offer sequential consistency, in which all operations happen in the order specified by the code and where all CPUs' views of these operations are consistent with a global ordering of the combined operations. Sequentially consistent systems have some nice properties, but high performance does not tend to be one of them. The need for global ordering severely constrains the hardware's ability to exploit parallelism, and therefore, commodity CPUs and systems do not offer sequential consistency.
On these systems, three orderings must be accounted for:
Program order: the order in which the memory operations are specified in the code running on a given CPU.
Execution order: the order in which the individual memory-reference instructions are executed on a given CPU. The execution order can differ from program order due to both compiler and CPU-implementation optimizations.
Perceived order: the order in which a given CPU perceives its and other CPUs' memory operations. The perceived order can differ from the execution order due to caching, interconnect and memory-system optimizations. Different CPUs might well perceive the same memory operations as occurring in different orders.
Popular memory-consistency models include x86's process consistency, in which writes from a given CPU are seen in order by all CPUs, and weak consistency, which permits arbitrary reorderings limited only by explicit memory-barrier instructions. For more information on memory-consistency models, see Gharachorloo's exhaustive technical report, listed in the on-line Resources.
Practical Task Scheduling Deployment
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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|The Firebird Project's Firebird Relational Database||Jul 29, 2016|
|Stunnel Security for Oracle||Jul 28, 2016|
|SUSE LLC's SUSE Manager||Jul 21, 2016|
|My +1 Sword of Productivity||Jul 20, 2016|
|Non-Linux FOSS: Caffeine!||Jul 19, 2016|
|Murat Yener and Onur Dundar's Expert Android Studio (Wrox)||Jul 18, 2016|
- The Firebird Project's Firebird Relational Database
- Stunnel Security for Oracle
- My +1 Sword of Productivity
- Non-Linux FOSS: Caffeine!
- Managing Linux Using Puppet
- SUSE LLC's SUSE Manager
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- Doing for User Space What We Did for Kernel Space
- Google's SwiftShader Released
- SuperTuxKart 0.9.2 Released
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This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide