Research
Researchers in the Performance and Algorithms Group are currently engaged in a wide variety of projects including collaborations with computer architects and programming models researchers. Below is a subset of our active research projects and topics.
 Autotuning Automatic Performance Tuning or "Autotuning", is an empirical, feedbackdriven performance optimization technique designed to maximize performance across a wide variety of architectures without sacrificing portability or productivity. Over the years, autotuning has expanded from simple loop tiling and unrollandjam to encompass transformations to data structures, parallelization, and algorithmic parameters. Perhaps the genesis of autotuning was PHiPAC (Portable High Performance ANSI C)…
 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 tradeoffs 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
 EDGAR: Energyefficient 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 20132018. Data are fundamental sources of insight for experimental and computational sciences. The Department of Energy acknowledges the challenges posed by fastgrowing scientific data sets and more complex data. The graph abstraction provides a natural way to represent relationships among complex fastgrowing scientific data…
 ExaBiome
 Roofline Performance Model Roofline is a visually intuitive performance model created by Samuel Williams that is used to bound the performance of various numerical methods and operations running on multicore, manycore, or accelerator processor architectures. Rather than simply using percentofpeak estimates, the model can be used to assess the quality of attained performance by combining locality, bandwidth, and different parallelization paradigms into a single performance figure. One can examine the resultant Roofline…
 High Performance Geometric Multigrid High Performance Geometric Multigrid (HPGMGFV) 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). HPGMGFV is being used to conduct computer science (e.g. Top500 benchmarking, programming models, compilers, performance optimization, and autotuners), computer architecture, and applied math research. HPGMGFV solves variablecoefficient elliptic problems…
 SciDAC4 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 Infrastructure needed to advance scientific discovery using supercomputers. As supercomputers continuously evolve, direct engagement of computer scientists and applied mathematicians with the scientists of targeted application domains becomes ever more…
 SciDAC3 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 Infrastructure needed to advance scientific discovery using supercomputers. As supercomputers continuously evolve, direct engagement of computer scientists and applied mathematicians with the scientists of targeted application domains becomes ever more…

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, codesign, supercomputer benchmarking, and application of novel algorithms.
 Autotuning Graphs, Sparse Linear Algebra, and ParticleinCell Codes for Exascale (XStack)
 XTune: Autotuning for Exascale
 CACHE Joint MathCS Institute
 Combustion CoDesign
 miniGMG
 TORCH Testbed
 Application Performance Characterization Benchmarking (APEX)
 Green Flash
 ULTRA Evaluation
 LDRD: Enhancing the Effectiveness of Manycore Chip Technologies for HighEnd Computing
 LDRD: Holistic Approach to Energy Efficient Computing Architecture
 LDRD: Reference Benchmarks for the Dwarfs
 Other Projects
 HipGISAXS