RDMA data transfer and GPU acceleration methods for high-throughput online processing of serial crystallography images.

The continual evolution of photon sources and high-performance detectors drives cutting-edge experiments that can produce very high throughput data streams and generate large data volumes that are challenging to manage and store. In these cases, efficient data transfer and processing architectures that allow online image correction, data reduction or compression become fundamental. This work investigates different technical options and methods for data placement from the detector head to the processing computing infrastructure, taking into account the particularities of modern modular high-performance detectors. In order to compare realistic figures, the future ESRF beamline dedicated to macromolecular X-ray crystallography, EBSL8, is taken as an example, which will use a PSI JUNGFRAU 4M detector generating up to 16 GB of data per second, operating continuously during several minutes. Although such an experiment seems possible at the target speed with the 100 Gb s-1 network cards that are currently available, the simulations generated highlight some potential bottlenecks when using a traditional software stack. An evaluation of solutions is presented that implements remote direct memory access (RDMA) over converged ethernet techniques. A synchronization mechanism is proposed between a RDMA network interface card (RNIC) and a graphics processing unit (GPU) accelerator in charge of the online data processing. The placement of the detector images onto the GPU is made to overlap with the computation carried out, potentially hiding the transfer latencies. As a proof of concept, a detector simulator and a backend GPU receiver with a rejection and compression algorithm suitable for a synchrotron serial crystallography (SSX) experiment are developed. It is concluded that the available transfer throughput from the RNIC to the GPU accelerator is at present the major bottleneck in online processing for SSX experiments.

Navigating from a Depth Image Converted into Sound.

Background. Common manufactured depth sensors generate depth images that humans normally obtain from their eyes and hands. Various designs converting spatial data into sound have been recently proposed, speculating on their applicability as sensory substitution devices (SSDs). Objective. We tested such a design as a travel aid in a navigation task. Methods. Our portable device (MeloSee) converted 2D array of a depth image into melody in real-time. Distance from the sensor was translated into sound intensity, stereo-modulated laterally, and the pitch represented verticality. Twenty-one blindfolded young adults navigated along four different paths during two sessions separated by one-week interval. In some instances, a dual task required them to recognize a temporal pattern applied through a tactile vibrator while they navigated. Results. Participants learnt how to use the system on both new paths and on those they had already navigated from. Based on travel time and errors, performance improved from one week to the next. The dual task was achieved successfully, slightly affecting but not preventing effective navigation. Conclusions. The use of Kinect-type sensors to implement SSDs is promising, but it is restricted to indoor use and it is inefficient on too short range.