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Combustion Co-Design

Combusion Codesign

Researchers of the Performance and Algorithms Research group are heavily involved with Researchers from the Computer Architecture Group and the Center for Computational Science and Engineering on Co-Designing algorithms, implementation, and architecture to maximize performance and energy efficiency in the context of combustion simulations. » Read More

TOP 500

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The TOP500 project was started in 1993 to provide a reliable basis for tracking and detecting trends in high-performance computing. Twice a year, a list of the sites operating the 500 most powerful computer systems is assembled and released. » Read More

EDGAR: Energy Efficient Data and Graph Alogrithms

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The EDGAR project explores methods to increase the energy efficiency of parallel graph algorithms and data mining tasks. A new family of algorithms will be developed to drastically reduce the energy footprint and running time of the graph and sparse matrix computations that form the basis of various data mining techniques. » Read More

Research

Roofline Performance Model

Roofline is a visually intuitive performance model used to bound the performance of various numerical methods and operations running on multicore, manycore, or accelerator processor architectures. Rather than simply using percent-of-peak estimates,…

High Performance Geometric Multigrid

High Performance Geometric Multigrid (HPGMG-FV) is a benchmark designed to proxy the finite volume based geometric multigrid linear solvers found in adaptive mesh refinement (AMR) based applications like the Low Mach Combustion Code (LMC). HPGMG-FV…

Auto-tuning

Automatic Performance Tuning or "Auto-tuning", is an empirical, feedback-driven performance optimization technique designed to maximize performance across a wide variety of architectures without sacrificing portability or productivity. Over the…

EDGAR: Energy-efficient Data and Graph Algorithms Research

This work is supported through a DOE Early Career award from the Office of Advanced Scientific Computing Research (ASCR) (Applied Mathematics) for the period of 2013-2018. Data are fundamental sources of insight for experimental and computational…

SciDAC-3

Researchers from FTG are engaged in a number of activities in the Scientific Discovery through Advanced Computing (SciDAC) initiative. The SciDAC program was initiated in 2001 in order to develop the Scientific Computing Software and Hardware…

SciDAC-4

Researchers from PAR are engaged in a number of activities in the Scientific Discovery through Advanced Computing (SciDAC) initiative. The SciDAC program was initiated in 2001 in order to develop the Scientific Computing Software and Hardware…

BeBOP (Berkeley Benchmarking and Optimization)

The BeBOP group is broadly interested in understanding software performance tuning issues, and the interaction or implications for hardware design. Among our general interests include: (1) the interaction between application software, compilers, and hardware, (2), managing trade-offs among the various measures of performance, such as speed, accuracy, power, storage, ... (3) automating the performance tuning process, starting with the computational kernels which dominate application performance in scientific computing and information retrieval, (4) performance modeling and evaluation of future computer architectures

TOP500

TOP500 Supercomputing Sites The TOP500 project was started in 1993 to provide a reliable basis for tracking and detecting trends in high-performance computing. Twice a year, a list of the sites operating the 500 most powerful computer systems…

Previous Projects

Over the last 10 years, researchers in the Performance and Algorithms research group have led a number of research projects spanning performance optimization, performance modeling, co-design, supercomputer benchmarking, and application of novel algorithms.

More from Research »

The Performance and Algorithms Research Group focuses on the research and development of technologies and algorithms that enhance the performance, scalability, and energy efficiency of applications running on the Department of Energy's multicore-, manycore-, and accelerator-based supercomputers.  Moreover, we develop performance models to understand the inherent bottlenecks in today's systems as well as predict the performance and bottlenecks of tomorrow's exascale systems.  To that end, we have formed strong research collaborations with computer science, computer architecture, applied math, and application research teams.

Group Leader: Erich Strohmaier