The Challenges of Concurrency in Software March 12, 2015Posted by Peter Varhol in Software development, Software tools.
Tags: deadlock, Race conditions, Thinking Software
I learned how to program on an Apple Macintosh, circa late 1980s, using Pascal and C. As I pursued graduate work in computer science, I worked with Lisp and Smalltalk, running to the Motorola 680X0 and eventually the Intel architecture.
These were all single-threaded programs, meaning that they executed sequentially, one step at a time. As a CS grad student, and later as a university professor, I learned and taught about multi-threading and concurrent code execution.
But it was almost entirely theoretical. Until the turn of the century, almost no code was executed in parallel. Part of the reason was that none but the most sophisticated computers executed in parallel. Even as Intel and other processors moved decisively into multi-core architectures, operating systems and programmers weren’t ready to take advantage of this hardware innovation.
But only by taking advantage of multi-core and hyper-threaded processors could developers improve the performance of increasingly complex applications. So, aided by modern programming languages such as Java and C#, developers have been cautiously working on applications that can take better advantage of these processors.
To do so, they’re dusting off their old textbooks and looking at concepts like “critical section”, “fork”, and “join”. They are deeply examining their code to determine which operations can occur simultaneously without producing errors.
To be fair, several tools came out in the mid-2000s claiming the ability to automatically parallelize existing code, mostly by analyzing the code and trying to parcel out threads based on an expectation that certain code segments can be parallelized. In practice, there was not a lot of code that could safely be parallelized in this way.
But most new applications are multithreaded, and the operating system can dispatch threads to different cores and CPUs for concurrent execution. Using today’s processors, this is the only way to get the best performance out of modern software.
The problem is that developers are still fumbling their way through the process of writing code that can execute in parallel. There are two types of problems. One is deadlock, where code can’t continue because another thread won’t give up a resource, such as a piece of data. This will stop execution altogether.
Another is the insidious race condition, where the result is dependent upon which thread completes first. This is insidious because an incorrect result can be random, and is often difficult to detect because it may not result in an obvious error.
Fortunately, tools are emerging that help in the identification and analysis of concurrent software issues. One is Race Catcher, from Thinking Software. It can be used in two ways during the application lifecycle. During development and test, it can dynamically analyze Java code to look ahead for deadlocks and race conditions. You can’t predict the occurrence of a race condition, of course, but you can tell where the same data is being processed in different ways, at the same time.
In a headless version, it can run as an agent on production servers, doing the same thing. That’s a version of DevOps. We catch things in production before they become problems, and refer them back to development to be fixed.
In an era where software development is changing more quickly and dramatically than any time since the PC era, we need more tools like this.