Posts Tagged ‘concurrency’

Whence Actors?

While sketching out the following drawing, I was thinking about the migration from sequential to concurrent programming driven by the rise of multicore machines. Can you find anything wrong with it?

Concurrency Abstractions

Right out of the box, C++ provided Objects (via classes) and procedures (via free standing functions). With C++11, standardized support for threads and tasks finally arrived to the language in library form. I can’t wait for Actors to appear in….. ?

cpp actors

Categories: C++11, C++14, C++17 Tags: , , ,

A Fascinating Conversation

October 22, 2014 2 comments

“A Conversation with Bjarne Stroustrup, Carl Hewitt, and Dave Ungar” is the most fascinating technical video I’ve seen in years. The primary focus of the discussion was how to write applications that efficiently leverage multicore processors. Because of the diversity of views, the video is so engrossing that I watched it three times. I also downloaded the MP3 podcast of the discussion and saved it to a USB stick for the drive to/from work.

When pure physics started limiting the enormous CPU clock speed gains being achieved in the 90s, vertical scaling came to an abrupt halt and horizontal scaling kicked into gear. The number of cores per processor started going up, and it still is today.

Cores vs clock speed

There was much discussion over the difference between a “lock” and “synchronization“. As the figure below illustrates, main memory is physically shared between the cores in a multicore processor. Thus, even if your programming language shields this fact from you by supporting high level, task-based, message passing instead of low-level cooperative threading with locks, some physical form of under-the-hood memory synchronization must be performed in order for the cores to communicate with each other through shared memory without data races.


Here is my layman’s take on each participant’s view of the solution to the multicore efficiency issue:

  • Hewitt: We need new, revolutionary, actor-based programming languages that abandon the traditional, sequential Von Neumann model. The current crop of languages just don’t cut it as the number of cores per processor keeps steadily increasing.
  • Ungar: We need incoherent, unsynchronized hardware memory architectures with background cache error correction. Build a reliable system out of unreliable parts.
  • Stroustrup: Revolutions happen much less than people seem to think. We need to build up and experiment with efficient concurrency abstractions in layered libraries that increasingly hide locks and core-to-memory synchronization details from programmers (the C++ threads-to-tasks-to-“next?” approach).


Parallelism And Concurrency

In the beginning of Robert Virding’s brilliant InfoQ talk on Erlang, he distinguishes between parallelism and concurrency. Parallelism is “physical“, having to do with the static  number of cores and processors in a system. Concurrency is “abstract“, having to do with the number of dynamic application processes and threads running in the system. To relate the physical with the abstract, I felt compelled to draw this physical-multi-core, physical-multi-node, abstract-multi-process, abstract-multi-thread diagram:


It’s not much different than the pic in this four year old post: PTCPN. It’s simply a less detailed, alternative point of view.

Beginner, Intermediate, Expert

September 3, 2012 Leave a comment

Check out this insightful quote from Couchbase CTO Damien Katz:

Threads are hard because, despite being extremely careful, it’s ridiculously easy to code in hard to find, undeterministic, data races and/or deadlocks. That’s why I always model my multi-threaded programs (using the MML, of course) to some extent before I dive into code:

Note that even though I created and evolved (using paygo, of course) the above, one page “agile model” for a program I wrote, I still ended up with an infrequently occurring  data race that took months, yes months, to freakin’ find. The culprit ended up being a data race on the (supposedly) thread-safe DB2 data structure accessed by the AT4, AT6, and AT7 threads. D’oh!

Two Plus Months

June 30, 2012 1 comment

Race conditions are one of the worst plagues of concurrent code: They can cause disastrous effects all the way up to undefined behavior and random code execution, yet they’re hard to discover reliably during testing, hard to reproduce when they do occur, and the icing on the cake is that we have immature and inadequate race detection and prevention tool support available today. – Herb Sutter (

With this opening paragraph in mind, observe the figure below. If you don’t lock-protect a stateful object that’s accessed by more than one thread, you’re guaranteed to fall into the dastardly trap that Herb describes. D’oh!

Now, look at the two object figure below. Unless you protect each of the two objects in the execution path with a lock, you’re hosed!

To improve performance at the expense of higher risk, you can use one lock for the two object example like on the left side of this graphic:

Alas, if you do choose to use one lock in a two object configuration like the example above, you better be sure that you don’t come in through the side with another thread to use the thread-unsafe object2. You also better be sure that a future maintainer of your code doesn’t do the same. But wait… How can you ensure that a maintainer won’t do that? You can’t. So stick with the more conservative, lower performance, one-lock-per-object approach.

Don’t ask me why I wrote this post cuz I ain’t answering. Well, Ok, ask. I wrote this post because I was burned by the left-hand side of the second graphic in this post. It took quite awhile, actually two plus months, to finally localize and squash the bugger in production code. As usual, Herb was right.

And please, don’t tell me that lock-free programming is the answer:

…replacing locks wholesale by writing your own lock-free code is not the answer. Lock-free code has two major drawbacks. First, it’s not broadly useful for solving typical problems—lots of basic data structures, even doubly linked lists, still have no known lock-free implementations. Second, it’s hard even for experts. It’s easy to write lock-free code that appears to work, but it’s very difficult to write lock-free code that is correct and performs well. Even good magazines and refereed journals have published a substantial amount of lock-free code that was actually broken in subtle ways and needed correction. – Herb Sutter (Dr. Dobbs).

Highly Available Systems == Scalable Systems

June 1, 2012 4 comments

In this QCon talk: “Building Highly Available Systems In Erlang“, Erlang programming language creator and highly-entertaining speaker Joe Armstrong asserts that if you build a highly available software system, then scalability comes along for “free” with that system. Say what? At first, I wanted to ask Joe what he was smoking, but after reflecting on his assertion and his supporting evidence, I think he’s right.

In his inimitable presentation, Joe postulates that there are 6 properties of Highly Available (HA) systems:

  1. Isolation (of modules from each other – a module crash can’t crash other modules).
  2. Concurrency (need at least two computers in the system so that when one crashes, you can fix it while the redundant one keeps on truckin’).
  3. Failure Detection (in order to fix a failure at its point of origin, you gotta be able to detect it first)
  4. Fault Identification (need post-failure info that allows you to zero-in on the cause and fix it quickly)
  5. Live Code Upgrade (for zero downtime, need to be able to hot-swap in code for either evolution or bug fixes)
  6. Stable Storage (multiple copies of data; distribution to avoid a single point of failure)

By design, all 6 HA rules are directly supported within the Erlang language. No, not in external libraries/frameworks/toolkits, but in the language itself:

  1. Isolation: Erlang processes are isolated from each other by the VM (Virtual Machine); one process cannot damage another and processes have no shared memory (look, no locks mom!).
  2. Concurrency: All spawned Erlang processes run in parallel – either virtually on one CPU, or really, on multiple cores and processor nodes.
  3. Failure Detection: Erlang processes can tell the VM that it wants to detect failures in those processes it spawns. When a parent process spawns a child process, in one line of code it can “link” to the child and be auto-notified by the VM of a crash.
  4. Fault Identification: In Erlang (out of band) error signals containing error descriptors are propagated to linked parent processes during runtime.
  5. Live Code Upgrade: Erlang application code can be modified in real-time as it runs (no, it’s not magic!)
  6. Stable Storage: Erlang contains a highly configurable, comically named database called “mnesia” .

The punch line is that systems composed of entities that are isolated (property 1) and concurrently runnable (property 2) are automatically scalable. Agree?

Standard, Portable C++ Concurrency

March 2, 2012 4 comments

Recently, I downloaded the Microsoft Visual Studio 11 IDE Beta in order to start experimenting with some C++11 features. Lo and behold, standard and portable concurrency is now supported:

At least on Windows, there’s no need to use the Win API,  Boost.Thread or ACE or any other third party library in the future to write multi-core friendly, multi-threaded C++ apps. I don’t know when GCC and/or CLANG will ship with the standard C++11 concurrency libs. Do you?

By the way, a series of quick tests verified that lambdas, strictly typed enums, auto, nullptr, std::array, std::regex, and std::atomic work. Initializer lists, raw string literals, “using” as typedef, and range-based for loops don’t work yet.

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