Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA high performance scientific cloud computing environment for materials simulations
187
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We describe the development of a scientific cloud computing (SCC) platform that offers high performance computation capability. The platform consists of a scientific virtual machine prototype containing a UNIX operating system and several materials science codes, together with essential interface tools (an SCC toolset) that offers functionality comparable to local compute clusters. In particular, our SCC toolset provides automatic creation of virtual clusters for parallel computing, including tools for execution and monitoring performance, as well as efficient I/O utilities that enable seamless connections to and from the cloud. Our SCC platform is optimized for the Amazon Elastic Compute Cloud (EC2). We present benchmarks for prototypical scientific applications and demonstrate performance comparable to local compute clusters. To facilitate code execution and provide user-friendly access, we have also integrated cloud computing capability in a JAVA-based GUI. Our SCC platform may be an alternative to traditional HPC resources for materials science or quantum chemistry applications.
rate research
Read More
We introduce a Python framework designed to automate the most common tasks associated with the extraction and upscaling of the statistics of single-impact crater functions to inform coefficients of continuum equations describing surface morphology evolution. Designed with ease-of-use in mind, the framework allows users to extract meaningful statistical estimates with very short Python programs. Wrappers to interface with specific simulation packages, routines for statistical extraction of output, and fitting and differentiation libraries are all hidden behind simple, high-level user-facing functions. In addition, the framework is extensible, allowing advanced users to specify the collection of specialized statistics or the creation of customized plots. The framework is hosted on the BitBucket service under an open-source license, with the aim of helping non-specialists easily extract preliminary estimates of relevant crater function results associated with a particular experimental system.
Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise simulations. In settings involving many simulations, machine learning can reduce these costs, often by orders of magnitude, by interpolating between reference simulations. This requires representations that describe any molecule or material and support interpolation. We comprehensively review and discuss current representations and relations between them, using a unified mathematical framework based on many-body functions, group averaging, and tensor products. For selected state-of-the-art representations, we compare energy predictions for organic molecules, binary alloys, and Al-Ga-In sesquioxides in numerical experiments controlled for data distribution, regression method, and hyper-parameter optimization.
This paper describes JANUS, a modular massively parallel and reconfigurable FPGA-based computing system. Each JANUS module has a computational core and a host. The computational core is a 4x4 array of FPGA-based processing elements with nearest-neighbor data links. Processors are also directly connected to an I/O node attached to the JANUS host, a conventional PC. JANUS is tailored for, but not limited to, the requirements of a class of hard scientific applications characterized by regular code structure, unconventional data manipulation instructions and not too large data-base size. We discuss the architecture of this configurable machine, and focus on its use on Monte Carlo simulations of statistical mechanics. On this class of application JANUS achieves impressive performances: in some cases one JANUS processing element outperfoms high-end PCs by a factor ~ 1000. We also discuss the role of JANUS on other classes of scientific applications.
Optical phase change materials (O-PCMs), a unique group of materials featuring drastic optical property contrast upon solid-state phase transition, have found widespread adoption in photonic switches and routers, reconfigurable meta-optics, reflective display, and optical neuromorphic computers. Current phase change materials, such as Ge-Sb-Te (GST), exhibit large contrast of both refractive index (delta n) and optical loss (delta k), simultaneously. The coupling of both optical properties fundamentally limits the function and performance of many potential applications. In this article, we introduce a new class of O-PCMs, Ge-Sb-Se-Te (GSST) which breaks this traditional coupling, as demonstrated with an optical figure of merit improvement of more than two orders of magnitude. The first-principle computationally optimized alloy, Ge2Sb2Se4Te1, combines broadband low optical loss (1-18.5 micron), large optical contrast (delta n = 2.0), and significantly improved glass forming ability, enabling an entirely new field of infrared and thermal photonic devices. We further leverage the material to demonstrate nonvolatile integrated optical switches with record low loss and large contrast ratio, as well as an electrically addressed, microsecond switched pixel level spatial light modulator, thereby validating its promise as a platform material for scalable nonvolatile photonics.
This paper describes a scalable active learning pipeline prototype for large-scale brain mapping that leverages high performance computing power. It enables high-throughput evaluation of algorithm results, which, after human review, are used for iterative machine learning model training. Image processing and machine learning are performed in a batch layer. Benchmark testing of image processing using pMATLAB shows that a 100$times$ increase in throughput (10,000%) can be achieved while total processing time only increases by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust scalability. The images and algorithm results are provided through a serving layer to a browser-based user interface for interactive review. This pipeline has the potential to greatly reduce the manual annotation burden and improve the overall performance of machine learning-based brain mapping.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
