Effective Concurrency: Sharing Is the Root of All Contention

This month’s Effective Concurrency column, “Sharing Is the Root of All Contention”, is now live on DDJ’s website.

This article aims to address the root cause behind some frequently made assertions: Statements like “locks kill scalability” and “CAS kills scalability” are mostly true but focus on symptoms rather than causes; and others such as “reader/writer mutexes are great for scalability” at minimum need some heavy qualification.

From the article:

Quick: What fundamental principle do all of the following have in common?

  • Two children drawing with crayons.
  • Three customers at Starbucks adding cream to their coffee.
  • Four processes running on a CPU core.
  • Five threads updating an object protected by a mutex.
  • Six threads running on different processors updating the same lock-free queue.
  • Seven modules using the same reference-counted shared object.

Answer: In each case, multiple users share a resource that requires coordination because not everyone can use it simultaneously — and such sharing is the root cause of all resulting contention that will degrade everyone’s performance. (Note: The only kind of shared resource that doesn’t create contention is one that inherently requires no synchronization because all users can use it simultaneously without restriction, which means it doesn’t fall into the description above. For example, an immutable object can have its unchanging bits be read by any number of threads or processors at any time without synchronization.)

In this article, I’ll deliberately focus most of the examples on one important case, namely mutable (writable) shared objects in memory, which are an inherent bottleneck to scalability on multicore systems. But please don’t lose sight of the key point, namely that "sharing causes contention" is a general principle that is not limited to shared memory or even to computing.

The Inherent Costs of Sharing

Here’s the issue in one sentence: Sharing fundamentally requires waiting and demands answers to expensive questions. …

Aside: This will be in the first electronic-only Dr. Dobb’s Report, and I’m going to have to get used to Dobb’s being electronic-only. One consequence of this change that I just discovered today is that an article can go online hours after I correct the proofs. That may seem like it shouldn’t be surprising in the days of blogs, but a magazine isn’t (or at least wasn’t) like a blog. When I wrote for magazines like C++ Report in the late 1990s, the time between submitting the final column manuscript and seeing it in print was about two to three months, depending on the magazine. Since the mid-2000s with Dobb’s, it’s been only one month from proofs to print — and Dobb’s could have posted the online versions even sooner, but held them back to be semi-synced with the print magazine. Now that the print constraint has disappeared, apparently I can (and, today, did) start work on a Friday morning to find the Dobb’s proofs I corrected the night before already on the web. Nifty. (And thanks again, Deirdre!)

I hope you enjoy it. Finally, here are links to previous Effective Concurrency columns and the DDJ print magazine issue in which they first appeared:

The Pillars of Concurrency (Aug 2007)

How Much Scalability Do You Have or Need? (Sep 2007)

Use Critical Sections (Preferably Locks) to Eliminate Races (Oct 2007)

Apply Critical Sections Consistently (Nov 2007)

Avoid Calling Unknown Code While Inside a Critical Section (Dec 2007)

Use Lock Hierarchies to Avoid Deadlock (Jan 2008)

Break Amdahl’s Law! (Feb 2008)

Going Superlinear (Mar 2008)

Super Linearity and the Bigger Machine (Apr 2008)

Interrupt Politely (May 2008)

Maximize Locality, Minimize Contention (Jun 2008)

Choose Concurrency-Friendly Data Structures (Jul 2008)

The Many Faces of Deadlock (Aug 2008)

Lock-Free Code: A False Sense of Security (Sep 2008)

Writing Lock-Free Code: A Corrected Queue (Oct 2008)

Writing a Generalized Concurrent Queue (Nov 2008)

Understanding Parallel Performance (Dec 2008)

Measuring Parallel Performance: Optimizing a Concurrent Queue (Jan 2009)

volatile vs. volatile (Feb 2009)

Sharing Is the Root of All Contention (Mar 2009)

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