erything needs to be parallel. What’s it going to do for you to make Word run a little faster?”

Larus says some applications, such as speech recognition, for which parallel programming is seen as a requirement, might benefit more from algorithmic improvements of existing serially written applications instead of converting to parallel processing. “The people working on [speech recogni-tion] at Microsoft say a machine that’s 10 times faster would probably reduce the error rate by a few tenths of a percent,” says Larus. “They see the future in terms of better algorithms, not more computation. We’re saying we can keep giving you exponential growth in compute power for certain types of programs, and people are telling us ‘That’s not really what we need for what we’re doing’ or ‘That’s not enough for what we’re doing.’”

Larus’s cautions aside, the computer industry is moving en masse to multicore machines, and users will expect to receive additional performance for their money—performance that will often depend on parallel applications. Therefore, some experts say, there is a danger in not immediately starting to train programmers on the requirements of parallel programming. This

amdahl’s Law

consciousness raising is important at all levels, from industry veterans to undergraduate students. “Sequential programming models do not work well enough,” says Maurice Herlihy, a professor of computer science at Brown University. “We can more or less keep busy” four cores or fewer, he says, “but beyond that we’ll have to rethink some things. If you can’t deliver more value with more cores, why would anybody ever upgrade?” Herlihy sees a peril that the engine of progress that has driven computer science for decades could run out of fuel, with dire consequences. “If this were to dissipate, then all the smart students would go to bioengineering or something, and computer science as a field would suffer.” Indeed, he says, “even one generation of stagnation could do lasting damage.”

 

incremental integration Computer scientists on university fac-ulties say academia is debating how and when to introduce parallel programming throughout the curriculum, instead of just offering an upper-level course as is now common. Both Brown’s Herlihy and Guy Blelloch, a professor of computer science at Carnegie Mellon University, say the early emphasis should be on higher-level

 

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Named after computer architect Gene amdahl, amdahl’s Law is frequently used in parallel programming to predict the theoretical maximum speedup using multiple processors.

parallel concepts and not on coding particulars such as languages or development frameworks.

Yet without some sort of introduction to the emerging post-collegiate parallel programming practice and tools environment, these ne w engineers might need even more training. Herlihy says that existing parallel frameworks— such as OpenMP, which dates to 1997, and the newly released OpenCL—are well suited for professional programmers, but not for students, who largely program in Java. This lack of grounding in the fundamentals of parallel code writing could lead to a looming disconnect between what new programmers know and what industry needs.

Intel’s Mattson, who worked on both frameworks, says one of the major blind spots of both OpenMP and OpenCL is a lack of support for managed languages such as Java. He also says the idea that there may be some type of revolutionary parallel programming language or approach on the near-term horizon that solves the multicore conundrum is misplaced. “Programmers insist on incremental approaches,” Mattson says. “They have a huge base of code written in established languages they will not throw away to adopt a whole new language, and they have to be able to incrementally evolve this legacy of code into the parallel space.”

The good news is that tools to assist programmers in this task of incrementally parallelizing code are proliferating on the market. Examples include Cilk++ from Cilk Arts, a Burlington, MA, company that extends the work of the Cilk Project at MIT. Cilk++ allows parallel programs written in C++ to retain serial semantics, which in turn permits programmers to use serial methodologies. CriticalBlue, an Edinburgh, Scotland-based company, recently released Prism, a parallel analysis and coding tool that CEO David Stewart says works with C or C++ and that allows users to explore parallelization strategies—which pieces to run in parallel, which dependencies to break, how many cores to use, and so on— before touching the code.

The most sensible way to implement parallelism, Stewart contends, is to enable software developers to analyze how much potential parallelism is in their code and to determine the min-