(A Cray machine was, until November 2010, the world's fastest general-purpose supercomputer.) Undergraduate courses tend to focus on sequential programming-not surprisingly, since the industry is still dominated by sequential code except in a few specialist niches, and most programmers spend their time maintaining or extending old code, rather than writing entirely new code.
#PARALLEL BARS SERIAL#
“Our conscious minds tend to think in terms of serial steps,” says Steve Scott, chief technology officer at Cray, a storied maker of supercomputers. And what happens if two writers both end up waiting for each other? In the world of parallel programming, the resulting stoppage is known as “deadlock”.Īnother obstacle to parallel programming is cultural. In practice, that may involve a lot of waiting around for others to complete their subtasks. They might get the story written ten times faster than any one of them could on his own, but will it make much sense? Throwing 100 or 1,000 reporters at the same problem does not help-instead, the task becomes even more difficult, because each reporter must co-ordinate his actions with the others.
#PARALLEL BARS SOFTWARE#
Making efficient parallel software means starting from scratch.ĭr Patterson likens parallel programming to having ten reporters each write a paragraph of a news story. But there was little demand, because most existing programs do not lend themselves to running in parallel. He spent most of the 1980s and early 1990s at Alliant, a supercomputing company he co-founded that planned to convert ordinary (or “sequential”) software into parallel software automatically. The reasons for this neglect are simple and longstanding, says Craig Mundie, chief research and strategy officer at Microsoft. There are dozens of them-by one count, more than a hundred. Parallel-programming languages in particular tend to languish in academic obscurity. Can you name the inventors of COBOL, C, Java or Python? (The answers are Grace Murray Hopper, Dennis Ritchie, James Gosling and Guido van Rossum.) “There are thousands of programming languages, and only a handful are used by more than their inventors,” notes David Patterson, a computer scientist at the University of California at Berkeley. Even the inventors of successful languages are mostly unknown within the industry, let alone outside it.
Surely this problem will be solved by some bright young entrepreneur who will devise a new parallel-programming language and make a fortune in the process? Alas, designing languages does not seem to provide a path to fame and riches. Unless it is solved, notes David Smith of Gartner, a market-research firm, there will be a growing divide between computers' theoretical and actual performance.
#PARALLEL BARS FULL#
What was once an obscure academic problem-finding ways to make it easy to write software that can take full advantage of the power of parallel processing-is rapidly becoming a problem for the whole industry. Intel, the world's biggest chipmaker, has demonstrated a 48-core processor, and chips with hundreds of cores seem likely within a few years. After all, software written for one sort of machine is not expected to work on the other.īut the distinction between the two is slowly breaking down.
At present, mainstream programs written for PCs (such as word-processor software), and specialist programs written for supercomputers with thousands of processors (such as climate-modelling or protein-folding software), are written using entirely different tools, languages and techniques. Instead, making software run faster in the future will mean using parallel-programming techniques. “We're not going to have faster processors,” says Katherine Yelick, a computer scientist at the Lawrence Berkeley National Laboratory in California. But your spellchecker will not run six times faster on a six-core machine unless it has been specially written to share out the work between the available cores, so that they can tackle the job in parallel. And the computer's operating system may be able to assign different tasks to different cores, to ensure that, for example, video playback in a web browser does not slow down while a hard disk is scanned for viruses. A few pieces of specialist software can take advantage of multiple cores: image-processing software, for example, may divide up a difficult task and farm it out to multiple cores to get it done faster, combining the results when each core has finished its work. That is because nearly all software is still designed to run on a single-core chip in other words, it is designed to do only one thing at a time. You might expect a six-core machine to be six times faster than a machine with a single-core microprocessor.
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