Early in the 21st century, improvements in single-processor performance slowed, as measured in instructions executed per second, and such performance now improves at a very modest pace, if at all. This abrupt shift is due to fundamental limits in the power efficiency of complementary metal oxide semiconductor integrated circuits (used in virtually all computer chips today) and apparent limits in the efficiencies that can be exploited in single-processor architectures. Reductions in transistor size continue apace, and so more transistors can still be packed onto chips, albeit without the speedups seen in the past. As a result, the computer-hardware industry has commenced building chips with multiple processors. Current chips range from several complex processors to hundreds of simpler processors, and future generations will keep adding more. Unfortunately, that change in hardware requires a concomitant change in the software programming model. To use chip multiprocessors, applications must use a parallel programming model, which divides a program into parts that are then executed in parallel on distinct processors. However, much software today is written according to a sequential programming model, and applications written this way cannot easily be sped up by using parallel processors.

The only foreseeable way to continue advancing performance is to match parallel hardware with parallel software and ensure that the new software is portable across generations of parallel hardware. There has been genuine progress on the software front in specific fields, such as some scientific applications and commercial searching and transactional applications. Heroic programmers can exploit vast amounts of parallelism, domain-specific languages flourish, and powerful abstractions hide complexity. However, none of those developments comes close to the ubiquitous support for programming parallel hardware that is required to ensure that IT’s effect on society over the next two decades will be as stunning as it has been over the last half-century.

For those reasons, the Committee on Sustaining Growth in Computing Performance recommends that our nation place a much greater emphasis on IT and computer-science research and development focused on improvements and innovations in parallel processing, and on making the transition to computing centered on parallelism. The following should have high priority:

  • Algorithms that can exploit parallel processing;
  • New computing “stacks” (applications, programming languages, compilers, runtime/virtual machines, operating systems, and architectures) that execute parallel rather than sequential programs and that effectively manage software parallelism, hardware parallelism, power, memory, and other resources;


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