![]() It is a forward single-arm spectrometer dedicated to precision measurements of CP violation and rare decays in the b quark sector. The LHCb experiment is one of the four large particle detectors currently under construction at the LHC accelerator at CERN. Further, Avalon ranked at 315th on the June 1998 TOP500 list, by obtaining a result of 19.3 Gflops on the parallel Linpack benchmark. This simulation is exactly the same as that which won a Gordon Bell price/performance prize last year on the Loki cluster, at a total performance 7.7 times that of Loki, and a price/performance 2.6 times better than Loki. This puts it among the few scientific simulations to have ever involved more than 10 Petaflops of computation.Avalon also performed a gravitational treecode N-body simulation of galaxy formation using 9.75 million particles, which sustained an average of 6.78 Gflops over a 26 hour period. This simulation continued to run for a total of 332 hours on Avalon, computing a total of 1.12 x 1016 floating point operations. ![]() This simulation is similar to those which won part of the 1993 Gordon Bell performance prize using a 1024-node CM-5. ![]() This is more than a factor of three better than last year's Gordon Bell price/performance winners. The resulting price/performance is $15/Mflop, or equivalently, 67 Gflops per million dollars. The beginning of this simulation sustained approximately 10 Gflops over a 44 hour period, and saved 68 Gbytes of raw data. The simulations were performed on a 70 processor DEC Alpha cluster (Avalon) constructed entirely from commodity personal computer technology and freely available software, for a cost of 152 thousand dollars.Avalon performed a 60 million particle molecular dynamics (MD) simulation of shock-induced plasticity using the SPaSM MD code. We apply remote sensing science and machine learning algorithms to detect and classify agricultural crops and then estimate crop yields.Īs an entry for the 1998 Gordon Bell price/performance prize, we present two calculations from the disciplines of condensed matter physics and astrophysics. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte (8×1015 bits) scale are now available in commercial clouds (e.g., Google Earth Engine and Amazon NASA NEX), and new commercial satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology.
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