Computing in a Parallel Universe
Multicore chips could bring about the biggest change in computing since the microprocessor
The Slice-and-Dice Compiler
Not everyone shares Lee's bleak outlook. There may well be ways to tame the multicore monster.
One idea is to let the operating system deal with the problems of allocating tasks to processors and balancing the workload. This is the main approach taken today with time-sliced multiprocessing and, more recently, with dual-core chips. Whether it will continue to work well with hundreds of cores is unclear. In the simplest case, an operating system would adopt a one-processor-per-program rule. Thus the spreadsheet running in the background would never slow down the action in the video game on your screen. But this policy leaves processors idle if there aren't enough programs running, and it would do nothing to help any single program run faster. To make better use of the hardware, each program needs to be divided into many threads of execution.
An alternative is to put the burden on the compiler—the software that translates a program text into machine code. The dream is to start with an ordinary sequential program and have it magically sliced and diced for execution on any number of processors. Needless to say, this Vegematic compiler doesn't yet exist, although some compilers do detect certain opportunities for parallel processing.
Both of these strategies rely on the wizardry of a programming elite—those who build operating systems and compilers—allowing the rest of us to go on pretending we live in a sequential world. But if massive parallelism really is the way of the future, it can't remain hidden behind the curtain forever. Everyone who writes software will have to confront the challenge of creating programs that run correctly and efficiently on multicore systems.
Contrarians argue that parallel programming is not really much harder than sequential programming; it just requires a different mode of thinking. Both Hillis and Gelernter have taken this position, backing it up with detailed accounts drawn from their own experience. For example, Hillis and Guy L. Steele, Jr., describe their search for the best sorting algorithm on the Connection Machine. They found, to no one's surprise, that solutions from the uniprocessor world are seldom optimal when you have 65,536 processors to play with. What's more illuminating is their realization that having immediate access to every element of a large data set means you may not need to sort at all. More recently, Jeffrey Dean and Sanjay Ghemawat of Google have described a major success story for parallel programming. They and their colleagues have written hundreds of programs that run on very large clusters of computers, all using a programming model they call MapReduce.
The lesson of these examples appears to be that we shouldn't waste effort trying to adapt or convert existing software. A new computer architecture calls for a new mental model, a new metaphor. We need to rethink the problem as well as the solution. In other words, we have a historic opportunity to clean out the closet of computer science, to throw away all those dusty old sorting algorithms and the design patterns that no longer fit. We get to make a fresh start. (Be ready to buy all new software along with your new kilocore computer.)
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