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The appearance of ice I in the smallest possible clusters and the nature of its phase coexistence with liquid water could not thus far be unraveled. The experimental and theoretical infrared spectroscopic and free-energy results of this work show the emergence of the characteristic hydrogen-bonding pattern of ice I in clusters containing only around 90 water molecules. The onset of crystallization is accompanied by an increase of surface oscillator intensity with decreasing surface-to-volume ratio, a spectral indicator of nanoscale crystallinity of water. In the size range from 90 to 150 water molecules, we observe mixtures of largely crystalline and purely amorphous clusters. Our analysis suggests that the liquid–ice I transition in clusters loses its sharp 1st-order character at the end of the crystalline-size regime and occurs over a range of temperatures through heterophasic oscillations in time, a process without analog in bulk water.
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FINN- R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks
- Award ID(s):
- 1717213
- PAR ID:
- 10106928
- Date Published:
- Journal Name:
- ACM Transactions on Reconfigurable Technology and Systems
- Volume:
- 11
- Issue:
- 3
- ISSN:
- 1936-7406
- Page Range / eLocation ID:
- 1 to 23
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The imbalanced I/O load on large parallel file systems affects the parallel I/O performance of high-performance computing (HPC) applications. One of the main reasons for I/O imbalances is the lack of a global view of system-wide resource consumption. While approaches to address the problem already exist, the diversity of HPC workloads combined with different file striping patterns prevents widespread adoption of these approaches. In addition, load-balancing techniques should be transparent to client applications. To address these issues, we proposeTarazu, an end-to-end control plane where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load statistics for global data placement on distributed storage servers, while our design model employs trace-based optimization techniques to minimize latency for I/O load requests between clients and servers and to handle multiple striping patterns in files. We evaluate our proposed system on an experimental cluster for two common use cases: the synthetic I/O benchmark IOR and the scientific application I/O kernel HACC-I/O. We also use a discrete-time simulator with real HPC application traces from emerging workloads running on the Summit supercomputer to validate the effectiveness and scalability ofTarazuin large-scale storage environments. The results show improvements in load balancing and read performance of up to 33% and 43%, respectively, compared to the state-of-the-art.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3242897
