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multicore processor

Research opportunities

We're about to enter a whole new era of computing -- an era in which high-performance systems based on parallel computing will be on our desktops as well as in the data center.

In parallel computing, individual tasks are executed on several processors at the same time to provide increased speed. Until recently, each processor was housed in a separate chip, or even in a separate computer. But the introduction of multicore processors – which place two or more processors on each integrated circuit – offers the potential for highly parallel computing on the desktop.

Future desktops are expected to have multicores with 10, 20, or more processors. We expect that after 2010 all systems will be highly parallel.

Parallel computing systems are already used for scientific applications such as weather forecasting or fluid dynamics. But the need for highly parallel computing is expanding, as applications such as business intelligence, multimedia, voice recognition and financial forecasting become as complex and data-heavy as scientific applications.


Our approach

We are working to make it possible for the computing elements in a parallel system to work together in a way that is as fast and dependable as today's high-performance computers -- but at much lower cost.

Research focus

Our goals are to:

  • develop highly parallel systems that are easier to program, design, verify and debug than they are today
  • create high-performance and low-cost interconnections between computing elements
  •   know how to predict the performance and power of parallel systems

We are looking at systems from end to end, figuring out how to get multiple computing elements -- such as cores in a chip, chips in a system, and systems in a highly parallel system -- to work together better and faster.

Current work

On the programming end, our efforts include an initiative with MathWorks to develop a parallel extension to MATLAB, a language commonly used to model systems, so that it's easier to go from MATLAB to parallel programming. We're also working to fix the glitches that can occur when implementing parallel code into languages such as C++ and Java.

On the interconnection end, we're exploring ways to replace traditional high-performance networks, which are very expensive, with networks based on standards and commodity components that will offer higher performance and lower latency at lower cost.

And on the modeling end, we're trying to develop a system simulator that will help us better design parallel systems for customers, and let us quickly and accurately customize the systems to meet customers' needs.

At the same time, we're working on several application drivers, including efforts to speed computing of the price and return of certain financial instruments.

Finally, we are investigating novel system architectures based on blade components, focusing on attributes such as reliability, manageability and power efficiency for emerging enterprise applications. We are also looking at component-level support for such architectures; for example, we're working to design commodity microprocessors that are tolerant of the increasing soft and hard error rates inherent in future deep-submicron technologies.


Enterprise computing

» Data center automation
  » IT resource virtualization  
  » Data center power & cooling  
  » Storage infrastructure & management  
  » IT asset tracking  
  » Advanced architecture  

Related research

»  Systems architecture

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»  HP High Performance Computing
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