The XFP (17-BM) Beamline for X-ray Footprinting at NSLS-II

Hydroxyl-radical mediated synchrotron X-ray footprinting (XF) is a powerful solution-state technique in structural biology for the study of macromolecular structure and dynamics of proteins and nucleic acids, with several synchrotron resources available to serve the XF community worldwide. The XFP (Biological X-ray Footprinting) beamline at the NSLS-II was constructed on a three-pole wiggler source at 17-BM to serve as the premier beamline for performing this technique, providing an unparalleled combination of high flux density broadband beam, flexibility in beam morphology, and sample handling capabilities specifically designed for XF experiments. The details of beamline design, beam measurements, and science commissioning results for a standard protein using the two distinct XFP endstations are presented here. XFP took first light in 2016 and is now available for general user operations through peer-reviewed proposals. Currently, beam sizes from 450 μm × 120 μm to 2.7 mm × 2.7 mm (FWHM) are available, with a flux of 1.6 × 1016 photons s-1 (measured at 325 mA ring current) in a broadband (5-16 keV) beam. This flux is expected to rise to 2.5 × 1016 photons s-1 at the full NSLS-II design current of 500 mA, providing an incident power density of \u3e500 W mm-2 at full focus

From Flop to Megaflops: Java for Technical Computing

this article we show how optimizing array bounds checks and null pointer checks creates loop nests on which aggressive optimizations can be used. Applying these optimizations by hand to a simple matrix-multiply test case leads to Java-compliant programs whose performance is in excess of 500 Mflops on a four-processor 332MHz RS/6000 model F50 computer. We also report in this article the effect that various optimizations have on the performance of six floating-point-intensive benchmarks. Through these optimizations we have been able to achieve with Java at least 80% of the peak Fortran performance on the same benchmarks. Since all of these optimizations can be automated, we conclude that Java will soon be a serious contender for numerically intensive computing