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X-ray crystallography is a powerful method that has significantly contributed to our understanding of the biological function of proteins and other molecules. This method relies on the production of crystals that, however, are usually a bottleneck in the process. For some molecules, no crystallization has been achieved or insufficient crystals were obtained. Some other systems do not crystallize at all, such as nanoparticles which, because of their dimensions, cannot be treated by the usual crystallographic methods. To solve this, whole pair distribution function has been proposed to bridge the gap between Bragg and Debye scattering theories. To execute a fitting, the spectra of several different constructs, composed of millions of particles each, should be computed using a particle–pair or particle–particle (pp) distance algorithm. Using this computation as a test bench for current field-programmable gate array (FPGA) technology, we evaluate how the parallel computation capability of FPGAs can be exploited to reduce the computation time. We present two different solutions to the problem using two state-of-the-art FPGA technologies. In the first one, the main C program uses OmpSs (a high-level programming model developed at the Barcelona Supercomputing Center, that enables task offload to different high-performance computing devices) for task invocation, and kernels are built with OpenCL using reduced data sizes to save transmission time. The second approach uses task and data parallelism to operate on data locally and update data globally in a decoupled task. Benchmarks have been evaluated over an Intel D5005 Programmable Acceleration Card, computing a model of 2 million particles in 81.57 s – 24.5 billion atom pairs per second (bapps)– and over a ZU102 in 115.31 s. In our last test, over an up-to-date Alveo U200 board, the computation lasted for 34.68 s (57.67 bapps). In this study, we analyze the results in relation to the classic terms of speed-up and efficiency, This work was partially supported by the Spanish Government (AEI) and European Union (FEDER) (projects CTQ2017-88948-P, PID2021-124848OB-I00, RTI2018-095232-B-C21, SEV-2015-0493 and TIN2015-65316-P, grant BES-2016-078046) and by the Generalitat de Catalunya (contracts 2017-SGR-1742, 2017-SGR-1414, 2017-SGR-670 and 2017-SGR-1328)., Peer Reviewed, Postprint (published version)

X-ray crystallography is a powerful method that has significantly contributed to our understanding of the biological function of proteins and other molecules. This method relies on the production of crystals that, however, are usually a bottleneck in the process. For some molecules, no crystallization has been achieved or insufficient crystals were obtained. Some other systems do not crystallize at all, such as nanoparticles which, because of their dimensions, cannot be treated by the usual crystallographic methods. To solve this, whole pair distribution function has been proposed to bridge the gap between Bragg and Debye scattering theories. To execute a fitting, the spectra of several different constructs, composed of millions of particles each, should be computed using a particle-pair or particle-particle (pp) distance algorithm. Using this computation as a test bench for current field-programmable gate array (FPGA) technology, we evaluate how the parallel computation capability of FPGAs can be exploited to reduce the computation time. We present two different solutions to the problem using two state-of-the-art FPGA technologies. In the first one, the main C program uses OmpSs (a high-level programming model developed at the Barcelona Supercomputing Center, that enables task offload to different high-performance computing devices) for task invocation, and kernels are built with OpenCL using reduced data sizes to save transmission time. The second approach uses task and data parallelism to operate on data locally and update data globally in a decoupled task. Benchmarks have been evaluated over an Intel D5005 Programmable Acceleration Card, computing a model of 2 million particles in 81.57 s - 24.5 billion atom pairs per second (bapps)- and over a ZU102 in 115.31 s. In our last test, over an up-to-date Alveo U200 board, the computation lasted for 34.68 s (57.67 bapps). In this study, we analyze the results in relation to the classic terms of speed-up and efficiency

Colloidal gels made of lipid vesicles at highly diluted conditions have been recently described. The structure and composition of this type of material could be especially relevant for studies that combine model lipid membranes with proteins, peptides, or enzymes to replicate biological conditions. Details about the nanoscale events that occur during the formation of such gels would motivate their future application. Thus, in this work we investigate the gelation mechanism, which consists of a lipid dispersion of vesicles going through a process that involves freezing and heating. The appropriate combination of techniques (transmission electron microscopy, differential scanning calorimetry and synchrotron small angle X-ray scattering) allowed in-depth analysis of the different events that give rise to the formation of the gel. Results showed how freezing damaged the lipid dispersion, causing a polydisperse suspension of membrane fragments and vesicles upon melting. Heating above the lipids’ main phase transition temperature promoted the formation of elongated tubular structures. After cooling, these lipid tubes broke down into vesicles that formed branched aggregates across the aqueous phase, obtaining a material with gel characteristics. These mechanistic insights may also allow finding new ways to interact with lipid vesicles to form structured materials. Future works might complement the presented results with molecular dynamics or nuclear magnetic resonance experiments.