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Previous Projects
ULTRA Evaluation
This work evaluates existing and emerging large-scale HEC architectures using a set of in-depth studies from full applications. The novel aspect of this research is the emphasis on full applications, run with real input data and at the scale desired by application scientists in the domain. These problems are much more complicated than in traditional benchmarking suites such as the NAS Parallel Benchmarks or the LINPACK benchmark, and therefore reveal the kinds of performance issues… Read More »
LDRD: Enhancing the Effectiveness of Manycore Chip Technologies for High-End Computing
Jonathan Carter (PI) For the past 15 years, CPU performance has improved at an exponential pace &emdash; doubling approximately every 18 months with remarkable consistency. In order to maintain performance improvements within the conservative power envelope allowed by practical system design, the historical trend of increasing clock rates at an exponential pace has given way to a chip-scale multiprocessor (CMP) design strategy where the performance of individual CPU cores stays… Read More »
LDRD: Holistic Approach to Energy Efficient Computing Architecture
John Shalf (PI) The goal of this project is to develop new technology for energy-efficient computational science. The approach is to take a vertical slice through the space of applications, algorithms, software and hardware to perform a study on how to build the most energy-efficient system to solve a particular computation science problem. The initial target will be climate simulation, and the solution is likely to require higher degrees of parallelism in the climate … Read More »
LDRD: Reference Benchmarks for the Dwarfs
Erich Strohmaier (PI) We are developing a testbed of benchmarks by selecting a representative algorithm for each class of algorithms known as ‘dwarfs.’ To avoid preordaining parallel codes, we are developing pencil and paper description first, and then create realistic, scalable problem descriptions and input datasets to enable experiments from single sockets up to full scale HPC systems. We are also conducting an auto-tuning pilot study for the kernels of some one… Read More »
Other Projects
The Complex Systems Group participated in a number of other projects, most of which are led by researchers in other groups or divisions at LBNL. Here is a brief list: Computational, data management and analysis methods for the study of a rapidly expanding genome and metagenome sequence data space. CXG's Buluc is working with computational biologists at LBNL to determine if significant savings in storage and processing times are possible by using a pangenomic representation of… Read More »
HipGISAXS
HipGISAXS is a massively parallel software for analysis of X-ray scattering data for nanostructure reconstruction.
We have developed it in C++, with hybrid parallelism using MPI, Nvidia CUDA, and OpenMP. It is primarily designed for large-scale clusters of multi/many-cores and graphics processors. HipGISAXS currently supports *NIX based systems, and is able to harness computational power from any general-purpose CPUs including state-of-the-art multicores, as well as Nvidia GPUs and Intel MIC coprocessors.
HipGISAXS is able to handle large and custom structural morphologies, and perform X-ray scattering simulations at high resolutions. Please visit the HipGISAXS website for details. Read More »
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 sciences. The Department of Energy acknowledges the challenges posed by fast-growing scientific data sets and more complex data. The graph abstraction provides a natural way to represent relationships among complex fast-growing scientific data… Read More »
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 Read More »
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 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… Read More »
