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The PxP Project

Co-Managing Quality-Performance-Power Tradeoffs

Quality and Performance: Many large-scale scientific simulations require the numerical solution of models based on time-dependent nonlinear partial differential equations in two or three spatial dimensions. The nonlinear equations in these systems are often solved with implicit Newton-type methods or semi-implicit schemes, both of which leverage sparse or irregular computational kernels for basic operations, such as mesh management, differentiation, and sparse linear system solution. Each kernel has a multitude of implementations offering a wide range of tradeoffs in solution quality (e.g., accuracy, reliability, and scalability) and performance (e.g., execution time/rate and parallel efficiency/speedup). Thus, proper method selection to meet changing application quality-of-service requirements and changing technologies can potentially provide dramatic performance improvements. A principal challenge is to automate dynamic method selection; without automation, the problem of selecting and using the best solution method is impractical, and the application community cannot easily reap the benefits of research of the last several decades.

 

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Power: Current high-end platforms are ensembles of multiple fast CPUs with deep memory hierarchies and high-speed interconnects. Geometric scaling of raw power (Moore's Law) arises from more and faster transistors on a chip. However, chips are approaching their packaging thermal limits, and the power-related costs for high-end systems, both electrical power consumed (in Megawatts) and machine room cooling loads (200 W/ft^2), continue to grow as a quadratic function of peak execution rates and clock frequencies. Although a faster scientific simulation, such as one obtained by exploiting quality-performance tradeoffs, is also often one that consumes less power by using fewer compute cycles, a major challenge is developing explicitly power-aware scientific computing tools. In the near term, such tools can deliver lower-power realizations without adversely impacting application performance, and in the longer term, provide insights that can be used by the designers of systems software and microprocessors to develop future systems.

 

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Our project seeks to address these two challenges by developing adaptive software tools to co-manage quality-performance-power tradeoffs. Key aspects of our project (initiated in 9/04) include:

 

  • Combinatorial and statistical adaptive techniques to select methods dynamically, to deliver improved performance while producing a solution that meets application quality requirements.
  • Using an annotated model of computation and communication costs and sparse data access patterns, to develop techniques for power reduction without performance impairment (using techniques such as DVS).
  • Implementing our techniques by developing an adaptive component software system on high-end multiprocessors such as the Blue-Gene system.

 

Publications

For information on PxP-related publications, please see the PxP Publications page.

 

Participants

 

Funding

For information on PxP-related funding, please see the PXP Funding page.

-- JohnConner - 09 May 2005

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