$LWLG Related: Information tech needs fundamental
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$LWLG Related: Information tech needs fundamental shift to continue rapid advances in computing 12/16/10 http://phy.so/211722098 via @physorg_com
Information technology needs fundamental shift to continue rapid advances in computing
December 16, 2010
The rapid advances in information technology that drive many sectors of the U.S. economy could stall unless the nation aggressively pursues fundamental research and development of parallel computing -- hardware and software that enable multiple computing activities to process simultaneously, says a new report by the National Research Council.
Better options for managing power consumption in computers will also be essential for continued improvements in IT performance.
For many decades, advances in single-processor, sequential computer microprocessors have enabled computing performance to increase dramatically -- on the order of 10,000 times in the last 20 years alone.
However, power management and other technological limitations have made it impractical to continue improving computer performance in this way much longer.
Parallel computing, therefore, is the only known alternative for improving computer performance without significantly increasing costs and energy usage, the report says.
"The societal and economic impact of computer technology is undeniable, increasing productivity and efficiency and fostering innovation in medicine, defense, entertainment, and communications," said Samuel H. Fuller, chief technology officer and vice president of research and development for Analog Devices Inc., Norwood, Mass., and chair of the committee that wrote the report.
"To ensure that computing systems continue to double in performance every few years, we need to make significant changes in computer software and hardware.
Investing in research and development of parallel computing offers a clear path forward."
Despite some mainstream successes in parallel computing -- such as the MapReduce programming framework used by Google to process large data sets using thousands of computers -- most parallel computing in use now is limited to comparatively narrow scientific and engineering applications.
To enable parallel computing for broader use, new algorithms, programming models, operating systems, and computer architectures will be required, the report says, and research and development in these areas should be pursued.
In particular, advances are necessary to develop new parallel programming methods and supporting computing systems.
Although computing hardware such as semiconductor chips that contain eight or more microprocessors have already been developed, software that can keep that many or more processors busy in parallel is not available for most computing applications.
Research and development should also focus on making computer systems more energy efficient, the report says.
Power constraints now affect systems ranging from handheld devices to the largest computing data centers. Most computer chips are designed with silicon-based complementary metal oxide semiconductor (CMOS) technology.
While the number of devices per CMOS chip continues to double every few years, the technology has essentially reached its limits with regard to power efficiency.
Even as new parallel computing models and solutions are found, most future performance will ultimately be limited by energy constraints, the report notes.
It cautions that while parallel computing is the best alternative for improving future performance, there is no guarantee that it will bring rapid advances like those experienced in recent decades, and a number of uncertainties still need to be addressed.
Therefore, research and development should also explore fundamentally different alternatives to today's CMOS technology.
The report also recommends developing open interface standards for parallel programming to promote cooperation and innovation in the industry, designing tools and methods for transferring today's sequential computing to parallel applications, and emphasizing parallel computing as part of computer science education.
Explore further: NVIDIA Ushers In the Era of Personal Supercomputing
Read more at: https://phys.org/news/2010-12-technology-fund...s.html#jCp
Information technology needs fundamental shift to continue rapid advances in computing
December 16, 2010
The rapid advances in information technology that drive many sectors of the U.S. economy could stall unless the nation aggressively pursues fundamental research and development of parallel computing -- hardware and software that enable multiple computing activities to process simultaneously, says a new report by the National Research Council.
Better options for managing power consumption in computers will also be essential for continued improvements in IT performance.
For many decades, advances in single-processor, sequential computer microprocessors have enabled computing performance to increase dramatically -- on the order of 10,000 times in the last 20 years alone.
However, power management and other technological limitations have made it impractical to continue improving computer performance in this way much longer.
Parallel computing, therefore, is the only known alternative for improving computer performance without significantly increasing costs and energy usage, the report says.
"The societal and economic impact of computer technology is undeniable, increasing productivity and efficiency and fostering innovation in medicine, defense, entertainment, and communications," said Samuel H. Fuller, chief technology officer and vice president of research and development for Analog Devices Inc., Norwood, Mass., and chair of the committee that wrote the report.
"To ensure that computing systems continue to double in performance every few years, we need to make significant changes in computer software and hardware.
Investing in research and development of parallel computing offers a clear path forward."
Despite some mainstream successes in parallel computing -- such as the MapReduce programming framework used by Google to process large data sets using thousands of computers -- most parallel computing in use now is limited to comparatively narrow scientific and engineering applications.
To enable parallel computing for broader use, new algorithms, programming models, operating systems, and computer architectures will be required, the report says, and research and development in these areas should be pursued.
In particular, advances are necessary to develop new parallel programming methods and supporting computing systems.
Although computing hardware such as semiconductor chips that contain eight or more microprocessors have already been developed, software that can keep that many or more processors busy in parallel is not available for most computing applications.
Research and development should also focus on making computer systems more energy efficient, the report says.
Power constraints now affect systems ranging from handheld devices to the largest computing data centers. Most computer chips are designed with silicon-based complementary metal oxide semiconductor (CMOS) technology.
While the number of devices per CMOS chip continues to double every few years, the technology has essentially reached its limits with regard to power efficiency.
Even as new parallel computing models and solutions are found, most future performance will ultimately be limited by energy constraints, the report notes.
It cautions that while parallel computing is the best alternative for improving future performance, there is no guarantee that it will bring rapid advances like those experienced in recent decades, and a number of uncertainties still need to be addressed.
Therefore, research and development should also explore fundamentally different alternatives to today's CMOS technology.
The report also recommends developing open interface standards for parallel programming to promote cooperation and innovation in the industry, designing tools and methods for transferring today's sequential computing to parallel applications, and emphasizing parallel computing as part of computer science education.
Explore further: NVIDIA Ushers In the Era of Personal Supercomputing
Read more at: https://phys.org/news/2010-12-technology-fund...s.html#jCp
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