scholarly journals Exploring versioned distributed arrays for resilience in scientific applications

2016 ◽  
Vol 31 (6) ◽  
pp. 564-590 ◽  
Author(s):  
A Chien ◽  
P Balaji ◽  
N Dun ◽  
A Fang ◽  
H Fujita ◽  
...  
Keyword(s):  
Large Scale ◽  
Error Recovery ◽  
Error Rates ◽  
Preconditioned Conjugate Gradient ◽  
Rollback Recovery ◽  
System A ◽  
Global Arrays ◽  
Programming Effort ◽  
Extreme Scale ◽  
Recovery Schemes

Exascale studies project reliability challenges for future HPC systems. We present the Global View Resilience (GVR) system, a library for portable resilience. GVR begins with a subset of the Global Arrays interface, and adds new capabilities to create versions, name versions, and compute on version data. Applications can focus versioning where and when it is most productive, and customize for each application structure independently. This control is portable, and its embedding in application source makes it natural to express and easy to maintain. The ability to name multiple versions and “partially materialize” them efficiently makes ambitious forward-recovery based on “data slices” across versions or data structures both easy to express and efficient. Using several large applications (OpenMC, preconditioned conjugate gradient (PCG) solver, ddcMD, and Chombo), we evaluate the programming effort to add resilience. The required changes are small (< 2% lines of code (LOC)), localized and machine-independent, and perhaps most important, require no software architecture changes. We also measure the overhead of adding GVR versioning and show that overheads < 2% are generally achieved. This overhead suggests that GVR can be implemented in large-scale codes and support portable error recovery with modest investment and runtime impact. Our results are drawn from both IBM BG/Q and Cray XC30 experiments, demonstrating portability. We also present two case studies of flexible error recovery, illustrating how GVR can be used for multi-version rollback recovery, and several different forward-recovery schemes. GVR’s multi-version enables applications to survive latent errors (silent data corruption) with significant detection latency, and forward recovery can make that recovery extremely efficient. Our results suggest that GVR is scalable, portable, and efficient. GVR interfaces are flexible, supporting a variety of recovery schemes, and altogether GVR embodies a gentle-slope path to tolerate growing error rates in future extreme-scale systems.

Optical Scanning for Large-Scale Studies

1975 ◽  
Vol 14 (01) ◽  
pp. 32-34
Author(s):  
Elisabeth Schach
Keyword(s):  
Large Scale ◽  
Error Rates ◽  
Data Reporting ◽  
Reading Error ◽  
Optical Scanning ◽  
Rejection Rates

Data reporting the experience with an optical mark page reader is presented (IBM 1231Ν1). Information from 52,000 persons was gathered in seven countries, decentrally coded and centrally processed. Reader performance rates (i.e. sheets read per hour, sheet rejection rates, reading error rates) and costs (coding, verification, reading, etc.) are given.

Rethinking the notion of the high-performing education system: A Daoist response

2021 ◽  
pp. 174549992098369
Author(s):  
Charlene Tan
Keyword(s):  
Education System ◽  
Large Scale ◽  
Alternative Interpretation ◽  
Educational Systems ◽  
Education Systems ◽  
High Performing ◽  
Mutual Learning ◽  
System A ◽  
High Performers ◽  
Major Implication

This article challenges the dominant notion of the ‘high-performing education system’ and offers an alternative interpretation from a Daoist perspective. The paper highlights two salient characteristics of such a system: its ability to outperform other education systems in international large-scale assessments; and its status as a positive or negative ‘reference society’. It is contended that external standards are applied and imposed on educational systems across the globe, judging a system to be high- or low- performing, and consequently worthy of emulation or deserving of criticism. Three cardinal Daoist principles that are drawn from the Zhuangzi are expounded: a rejection of an external and oppressive dao (way); the emptying of one’s heart-mind; and an ethics of difference. A major implication is a celebration of a plurality of high performers and reference societies, each unique in its own dao but converging on mutual learning and appreciation.

scholarly journals Path-encoded high-dimensional quantum communication over a 2-km multicore fiber

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Beatrice Da Lio ◽  
Daniele Cozzolino ◽  
Nicola Biagi ◽  
Yunhong Ding ◽  
Karsten Rottwitt ◽  
...  
Keyword(s):  
Quantum Key Distribution ◽  
Quantum Communication ◽  
Error Rates ◽  
Quantum States ◽  
High Dimensional ◽  
Secret Key ◽  
System A ◽  
Reliable Transmission ◽  
Multicore Fiber ◽  
Interferometric Detection

AbstractQuantum key distribution (QKD) protocols based on high-dimensional quantum states have shown the route to increase the key rate generation while benefiting of enhanced error tolerance, thus overcoming the limitations of two-dimensional QKD protocols. Nonetheless, the reliable transmission through fiber links of high-dimensional quantum states remains an open challenge that must be addressed to boost their application. Here, we demonstrate the reliable transmission over a 2-km-long multicore fiber of path-encoded high-dimensional quantum states. Leveraging on a phase-locked loop system, a stable interferometric detection is guaranteed, allowing for low error rates and the generation of 6.3 Mbit/s of a secret key rate.

scholarly journals Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method?

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Moritz Mercker ◽  
Philipp Schwemmer ◽  
Verena Peschko ◽  
Leonie Enners ◽  
Stefan Garthe
Keyword(s):  
Habitat Selection ◽  
Large Scale ◽  
Error Rates ◽  
Process Models ◽  
Statistical Hypothesis ◽  
Type I ◽  
Tracking Data ◽  
Selection Models ◽  
Animal Tracking ◽  
Step Selection

Abstract Background New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods. Methods We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing. Results We demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs. Conclusions Based on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.

scholarly journals Analysis of Error Recovery Schemes for Networks on Chips

2005 ◽  
Vol 22 (5) ◽  
pp. 434-442 ◽  
Author(s):  
S. Murali ◽  
T. Theocharides ◽  
N. Vijaykrishnan ◽  
M.J. Irwin ◽  
L. Benini ◽  
...  
Keyword(s):  
Error Recovery ◽  
Networks On Chips ◽  
Analysis Of Error ◽  
Recovery Schemes

scholarly journals Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 233
Author(s):  
Jonathan Z.L. Zhao ◽  
Eliseos J. Mucaki ◽  
Peter K. Rogan
Keyword(s):  
Machine Learning ◽  
Ionizing Radiation ◽  
Radiation Exposure ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Error Rates ◽  
Support Vector ◽  
Dose Estimation ◽  
Gene Signatures ◽  
Ionizing Radiation Exposure

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% (DDB2,  PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% (DDB2,  CD8A,  TALDO1,  PCNA,  EIF4G2,  LCN2,  CDKN1A,  PRKCH,  ENO1,  and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

scholarly journals Dual-hop Relaying Networks for Short-Packet URLLCs: Performance analysis and Optimization

2021 ◽  
Author(s):  
Nguyen Thi Yen Linh ◽  
Tu Ngo Hoang ◽  
Pham Ngoc Son ◽  
Vo Nguyen Quoc Bao
Keyword(s):  
Fading Channels ◽  
Optimization Problems ◽  
Signal To Noise Ratio ◽  
Error Rates ◽  
Decode And Forward ◽  
Location Optimization ◽  
Maximum Ratio Combining ◽  
System A ◽  
Qualitative Conclusion ◽  
Short Packet

<div>This paper investigates short-packet communications for the dual-hop decode-and-forward relaying system to facilitate ultra-reliable and low-latency communications. In this system, a selected relay having the highest signal-to-noise ratio (SNR) serves as a forwarder to support the unavailable direct link between the source and destination, whereas a maximum ratio combining technique is leveraged at the destination to achieve the highest diversity gain. Approximated expressions of end-to-end (e2e) block error rates (BLERs) are derived over quasi-static Rayleigh fading channels and the finite-blocklength regime. To gain more insights about the performance behavior in the high-SNR regime, we provide the asymptotic analysis with two approaches, from which the qualitative conclusion based on the diversity order is made. Furthermore, the power allocation and relay location optimization problems are also considered to minimize the asymptotic e2e BLER under the configuration constraints. Our analysis is verified through Monte-Carlo simulations, which yield the system parameters' impact on the system performance.</div>

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