Asynchronous Replica Exchange Software for Grid and Heterogeneous Computing.

Parallel replica exchange sampling is an extended ensemble technique often used to accelerate the exploration of the conformational ensemble of atomistic molecular simulations of chemical systems. Inter-process communication and coordination requirements have historically discouraged the deployment of replica exchange on distributed and heterogeneous resources. Here we describe the architecture of a software (named ASyncRE) for performing asynchronous replica exchange molecular simulations on volunteered computing grids and heterogeneous high performance clusters. The asynchronous replica exchange algorithm on which the software is based avoids centralized synchronization steps and the need for direct communication between remote processes. It allows molecular dynamics threads to progress at different rates and enables parameter exchanges among arbitrary sets of replicas independently from other replicas. ASyncRE is written in Python following a modular design conducive to extensions to various replica exchange schemes and molecular dynamics engines. Applications of the software for the modeling of association equilibria of supramolecular and macromolecular complexes on BOINC campus computational grids and on the CPU/MIC heterogeneous hardware of the XSEDE Stampede supercomputer are illustrated. They show the ability of ASyncRE to utilize large grids of desktop computers running the Windows, MacOS, and/or Linux operating systems as well as collections of high performance heterogeneous hardware devices.

Crystal search - feasibility study of a real-time deep learning process for crystallization well images.

To avoid the time-consuming and often monotonous task of manual inspection of crystallization plates, a Python-based program to automatically detect crystals in crystallization wells employing deep learning techniques was developed. The program uses manually scored crystallization trials deposited in a database of an in-house crystallization robot as a training set. Since the success rate of such a system is able to catch up with manual inspection by trained persons, it will become an important tool for crystallographers working on biological samples. Four network architectures were compared and the SqueezeNet architecture performed best. In detecting crystals AlexNet accomplished a better result, but with a lower threshold the mean value for crystal detection was improved for SqueezeNet. Two assumptions were made about the imaging rate. With these two extremes it was found that an image processing rate of at least two times, but up to 58 times in the worst case, would be needed to reach the maximum imaging rate according to the deep learning network architecture employed for real-time classification. To avoid high workloads for the control computer of the CrystalMation system, the computing is distributed over several workstations, participating voluntarily, by the grid programming system from the Berkeley Open Infrastructure for Network Computing (BOINC). The outcome of the program is redistributed into the database as automatic real-time scores (ARTscore). These are immediately visible as colored frames around each crystallization well image of the inspection program. In addition, regions of droplets with the highest scoring probability found by the system are also available as images.

A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model.

Development of high-throughput technologies, such as Next-generation sequencing, allows thousands of experiments to be performed simultaneously while reducing resource requirement. Consequently, a massive amount of experiment data is now rapidly generated. Nevertheless, the data are not readily usable or meaningful until they are further analysed and interpreted. Due to the size of the data, a high performance computer (HPC) is required for the analysis and interpretation. However, the HPC is expensive and difficult to access. Other means were developed to allow researchers to acquire the power of HPC without a need to purchase and maintain one such as cloud computing services and grid computing system. In this study, we implemented grid computing in a computer training center environment using Berkeley Open Infrastructure for Network Computing (BOINC) as a job distributor and data manager combining all desktop computers to virtualize the HPC. Fifty desktop computers were used for setting up a grid system during the off-hours. In order to test the performance of the grid system, we adapted the Basic Local Alignment Search Tools (BLAST) to the BOINC system. Sequencing results from Illumina platform were aligned to the human genome database by BLAST on the grid system. The result and processing time were compared to those from a single desktop computer and HPC. The estimated durations of BLAST analysis for 4 million sequence reads on a desktop PC, HPC and the grid system were 568, 24 and 5 days, respectively. Thus, the grid implementation of BLAST by BOINC is an efficient alternative to the HPC for sequence alignment. The grid implementation by BOINC also helped tap unused computing resources during the off-hours and could be easily modified for other available bioinformatics software.