Potential High-Performance Magnet: Fe - and Zr -Alloyed Ce 2 Co 17
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Over the past decade, given the higher number of data sources (e.g., Cloud applications, Internet of things) and critical business demands, Big Data transitioned from batch-oriented to real-time analytics. Stream storage systems, such as Apache Kafka, are well known for their increasing role in real-time Big Data analytics. For scalable stream data ingestion and processing, they logically split a data stream topic into multiple partitions. Stream storage systems keep multiple data stream copies to protect against data loss while implementing a stream partition as a replicated log. This architectural choice enables simplified development while trading cluster size with performance and the number of streams optimally managed. This paper introduces a shared virtual log-structured storage approach for improving the cluster throughput when multiple producers and consumers write and consume in parallel data streams. Stream partitions are associated with shared replicated virtual logs transparently to the user, effectively separating the implementation of stream partitioning (and data ordering) from data replication (and durability). We implement the virtual log technique in the KerA stream storage system. When comparing with Apache Kafka, KerA improves the cluster ingestion throughput by up to 4x when multiple producers write over hundreds of data streams.
The ability to track simulated cancer cells through the circulatory system, important for developing a mechanistic understanding of metastatic spread, pushes the limits of today's supercomputers by requiring the simulation of large fluid volumes at cellular-scale resolution. To overcome this challenge, we introduce a new adaptive physics refinement (APR) method that captures cellular-scale interaction across large domains and leverages a hybrid CPU-GPU approach to maximize performance. Through algorithmic advances that integrate multi-physics and multi-resolution models, we establish a finely resolved window with explicitly modeled cells coupled to a coarsely resolved bulk fluid domain. In this work we present multiple validations of the APR framework by comparing against fully resolved fluid-structure interaction methods and employ techniques, such as latency hiding and maximizing memory bandwidth, to effectively utilize heterogeneous node architectures. Collectively, these computational developments and performance optimizations provide a robust and scalable framework to enable system-level simulations of cancer cell transport.
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Increasingly, scientific research discoverydepends on the analysis of extremely large amounts of data. Thisdata is gathered through very large scientific instruments, verylarge numbers of small devices, and mid-scale instruments.These devices generate exponentially increasing volumes of data.Such data is often obtained, analyzed, stored, visualized, andtransferred at and among sites world-wide by largecollaborations of science communities. Managing and movingextremely large volumes of data across today’s networks is aspecial multidimensional challenge. Also, existing datamanagement and movement services and tools often areinadequate to address all specific requirements. This paperdescribes the various components of this challenge and howthose issues can be addressed by ROBIN, a uniquecomprehensive set of integrated services designed specificallyfor managing and moving extremely large amounts of data overlong distances, e.g., thousands of miles around the globe. Thispaper also describes the results of initial experiments using thatset of integrated services.
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Here we provide an overview of the past, present, and a diverse collection of future computer architecture alternatives for HPC. The end of Moore’s Law influenced the current HPC architecture focus on accelerated compute nodes composed of CPU and GPU computing components integrated into massively parallel processor architecture systems. There are many alternatives for future HPC directions, with different technologies, computing ecosystems, opportunities for lead user application-driven customization, and the role of open innovation business models. This paper provides an overview of these different new horizons for HPC, an organizing principle to focus future computing research, different public-private partnership models, and the critical role of workforce development.
U.S. computing leaders, including Department of Energy National Laboratories, have partnered with universities, government agencies, and the private sector to research responses to COVID-19, providing an unprecedented collection of resources that include some of the fastest computers in the world. For HPC users, these leadership machines will drive the AI to accelerate the discovery of promising treatments, enable at-scale simulations to understand the virus’s protein structure and attack mechanisms, and help inform policymakers to deploy resources effectively.
The integrity of science and engineering research is grounded in assumptions of rigor and transparency on the part of those engaging in such research. HPC community effort to strengthen rigor and transparency take the form of reproducibility efforts. In a recent survey of the SC conference community, we collected information about the SC Reproducibility Initiative activities. Here, we present the survey results in this paper. Results show that the reproducibility initiative activities have contributed to higher levels of awareness on the part of SC conference technical program participants, and hint at contributing to greater scientific impact for the published papers of the SC conference series. Stringent point-of-manuscript-submission verification is problematic for reasons we point out, as are inherent difficulties of computational reproducibility in HPC. Future efforts should better decouple the community educational goals from goals that specifically strengthen a research work's potential for long-term impact through reuse 5-10 years down the road.
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Results from work on feedback control of a queuing system for HPC jobs with shared storage resources.
Advanced metal organic chemical vapor deposition technique (A-MOCVD) was used to grow 4-5 µm thick REBa 2 Cu 3 O 7–δ (REBCO, RE = rare earth) films, doped with 5% and 15% Hf. Thick REBCO films doped with 5% Hf exhibit self-field critical current density (J c ) over 1.8 MA/cm 2 at 77 K and 3 MA/cm 2 at 65 K, 1.5 T. Increasing the Hf content to 15% resulted in J c above 9 MA/cm 2 at 20 K, 9 T. The effect of (Ba+Hf)/Cu content in Hf-added (5 and 15 mol.%) REBCO films on the critical current density at 77 K, 0 T, c-axis lattice parameter and out-of- plane texture has been studied. In addition to growth of thick film REBCO tapes with superior performance, the A-MOCVD reactor has been used to quadruple the precursor-to-film conversion efficiency above 45%. Furthermore, this achievement has a first order impact in lowering the cost of REBCO tapes made by A-MOCVD.