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Concurrency: The Compiler Writers Perspective

Google’s Brian Grant discusses the problems of parallelism

By Alexandra Weber Morales

November 15, 2007 — (Page 1 of 3)
It’s rare to find concurrency practitioners with the right mix of technical insight and commercial software success. Brian Grant is a former software architect at PeakStream, which was acquired in June by Google. Though he’s tight-lipped about his current work, his concurrency background extends beyond PeakStream to include work at Lawrence Livermore National Labs on massively parallel machines.

PeakStream grew out of the Brook stream programming language for graphics processing units (GPUs), which originated at Stanford University. PeakStream’s suite of APIs, a virtual machine, a system profiler and a just-in-time compiler enables multithreaded applications to run on a panoply of hardware (multicore x86 processors, CPUs enhanced with GPUs, and possibly IBM's Cell). Before the acquisition by Google, PeakStream had aimed its multicore programming product at industries with obvious interest in high-performance computing: oil and gas, defense, finance and academia.

All this gives Grant a unique view on what makes concurrency hard, and what will—or won’t—make it easier. Though our conversation grazed only the surface of these issues, it was useful to gain a compiler writer’s perspective on the problems of parallelism.

SD Times: Is concurrency too difficult for developers accustomed to linear programming to grasp?

Brian Grant: It is challenging, but I don’t think it’s too challenging. I do think certain languages, especially C and C++, make it very challenging to write robust big multithreaded systems. Basically, they have features that are hostile to concurrency.

So the nondeterminism inherent in concurrency isn’t a deal-breaker?

I don’t think so, not by itself. There are tools and methodologies that can help developers cope with nondeterminism. However, many other features in the languages are problematic: for example, global and static variables in C and C++. In particular, if you’re coming from legacy code where you may not have considered the impact of concurrency, you may have lots of areas that will be problematic.

The key is managing the complexity by imposing some kind of discipline. As with all software, you need to break it into components, layers and well-defined interfaces. You have to access shared data in disciplined way. C, C++ and current compilers are inadequate to help in that way.

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