235 research outputs found
Atomic detail visualization of photosynthetic membranes with GPU-accelerated ray tracing
The cellular process responsible for providing energy for most life on Earth, namely, photosynthetic light-harvesting, requires the cooperation of hundreds of proteins across an organelle, involving length and time scales spanning several orders of magnitude over quantum and classical regimes. Simulation and visualization of this fundamental energy conversion process pose many unique methodological and computational challenges. We present, in two accompanying movies, light-harvesting in the photosynthetic apparatus found in purple bacteria, the so-called chromatophore. The movies are the culmination of three decades of modeling efforts, featuring the collaboration of theoretical, experimental, and computational scientists. We describe the techniques that were used to build, simulate, analyze, and visualize the structures shown in the movies, and we highlight cases where scientific needs spurred the development of new parallel algorithms that efficiently harness GPU accelerators and petascale computers
Acceleration and Verification of Virtual High-throughput Multiconformer Docking
The work in this dissertation explores the use of massive computational power available through modern supercomputers as a virtual laboratory to aid drug discovery. As of November 2013, Tianhe-2, the fastest supercomputer in the world, has a theoretical performance peak of 54,902 TFlop/s or nearly 55 thousand trillion calculations per second. The Titan supercomputer located at Oak Ridge National Laboratory has 560,640 computing cores that can work in parallel to solve scientific problems. In order to harness this computational power to assist in drug discovery, tools are developed to aid in the preparation and analysis of high-throughput virtual docking screens, a tool to predict how and how well small molecules bind to disease associated proteins and potentially serve as a novel drug candidate. Methods and software for performing large screens are developed that run on high-performance computer systems. The future potential and benefits of using these tools to study polypharmacology and revolutionizing the pharmaceutical industry are also discussed
Comparing archival policies for Blue Waters
This paper introduces two new tape archival policies that can im- prove tape archive performance in certain regimes, compared to the classical RAIT (Redundant Array of Independent Tapes) policy. The first policy, PARALLEL, still requires as many parallel tape drives as RAIT but pre-computes large data stripes that are written contiguously on tapes to increase write/read performance. The second policy, VERTICAL, writes contiguous data into a single tape, while updating error correcting information on the fly and delaying its archival until enough data has been archived. This second approach reduces the number of tape drives used for every user request to one. The performance of the three RAIT, PARALLEL and VERTICAL policies is assessed through extensive simulations, using a hardware configuration and a distribution of I/O requests similar to these expected on the Blue Waters system. These simulations show that VERTICAL is the most suitable policy for small files, whereas PARALLEL must be used for files larger than 1 GB. We also demonstrate that RAIT never outperforms both proposed policies, and that a heterogeneous policies mixing VERTICAL and PARALLEL performs 10 times better than any other policy
Metascheduling of HPC Jobs in Day-Ahead Electricity Markets
High performance grid computing is a key enabler of large scale collaborative
computational science. With the promise of exascale computing, high performance
grid systems are expected to incur electricity bills that grow super-linearly
over time. In order to achieve cost effectiveness in these systems, it is
essential for the scheduling algorithms to exploit electricity price
variations, both in space and time, that are prevalent in the dynamic
electricity price markets. In this paper, we present a metascheduling algorithm
to optimize the placement of jobs in a compute grid which consumes electricity
from the day-ahead wholesale market. We formulate the scheduling problem as a
Minimum Cost Maximum Flow problem and leverage queue waiting time and
electricity price predictions to accurately estimate the cost of job execution
at a system. Using trace based simulation with real and synthetic workload
traces, and real electricity price data sets, we demonstrate our approach on
two currently operational grids, XSEDE and NorduGrid. Our experimental setup
collectively constitute more than 433K processors spread across 58 compute
systems in 17 geographically distributed locations. Experiments show that our
approach simultaneously optimizes the total electricity cost and the average
response time of the grid, without being unfair to users of the local batch
systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System
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