Transcriptome and metabolome analysis reveals anthocyanin biosynthesis pathway associated with ramie (Boehmeria nivea (L.) Gaud.) leaf color formation

Background The bast fiber crop ramie can be used as high-quality forage resources, especially in tropical or subtropical region where there is lack of high-quality protein feed. Hongxuan No.1 (HX_1) is a unique ramie variety with a light reddish brown leaf color, which is obviously different from elite cultivar, Zhongzhu No.1 (ZZ_1, green leaf). While, the regulatory mechanism of color difference or secondary metaboliates synthesis between these two varieties have not been studied. Results In this study, phenotypic, transcriptomic and metabolomic analysis of HX_1 and ZZ_1 were conducted to elucidate the mechanism of leaf color formation. Chromaticity value and pigment content measuring showed that anthocyanin was the main metabolites imparting the different leaf color phenotype between the two varieties. Based on LC/MS, at least 14 anthocyanins were identified in leaves of HX_1 and ZZ_1, and the HX_1 showed the higher relative content of malvidin-, pelargonidin-,and cyanidin-based anthocyanins. Transcriptome and metabolome co-analysis revealed that the up-regulated expression of flavonoids synthesis gene was positively correlated with total anthocyanins accumulation in ramie leaf, and the differentfially expression of “blue gene” (F3’5’H) and the “red gene” (F3’H) in leaves bring out HX_1 metabolic flow more input into the cyanidin branch. Furthermore, the enrichment of glycosylated modification pathway (UGT and AT) and the expression of flavonoid 3-O-glucosyl transferase (UFGT), anthocyanidin reductase (ANR), in leaves were significantly influenced the diversity of anthocyanins between HX_1 and ZZ_1. Conclusions Phenotypic, transcriptomic and metabolomic analysis of HX_1 and ZZ_1 indicated that the expression levels of genes related to anthocyanin metabolism contribute to the color formation of ramie variety. Anthocyanins are important plant secandary metabilates with many physiological functions, the results of this study will deepened our understanding of ramie leaf color formation, and provided basis for molecular breeding of functional forage ramie.

Anomaly Detection via Mining Numerical Workflow Relations from Logs

<div>Complex software intensive systems, especially distributed systems, generate logs for troubleshooting. The logs are text messages recording system events, which can help engineers determine the system's runtime status. This paper proposes a novel approach named ADR (stands for Anomaly Detection by workflow Relations) that employs matrix nullspace to mine numerical relations from log data. The mined relations can be used for both offline and online anomaly detection and facilitate fault diagnosis. We have evaluated ADR on log data collected from two distributed systems, HDFS (Hadoop Distributed File System) and BGL (IBM Blue Gene/L supercomputers system). ADR successfully mined 87 and 669 numerical relations from the logs and used them to detect anomalies with high precision and recall. For online anomaly detection, ADR employs PSO (Particle Swarm Optimization) to find the optimal sliding windows' size and achieves fast anomaly detection.</div><div>The experimental results confirm that ADR is effective for both offline and online anomaly detection. </div>

Anomaly Detection via Mining Numerical Workflow Relations from Logs

<div>Complex software intensive systems, especially distributed systems, generate logs for troubleshooting. The logs are text messages recording system events, which can help engineers determine the system's runtime status. This paper proposes a novel approach named ADR (stands for Anomaly Detection by workflow Relations) that employs matrix nullspace to mine numerical relations from log data. The mined relations can be used for both offline and online anomaly detection and facilitate fault diagnosis. We have evaluated ADR on log data collected from two distributed systems, HDFS (Hadoop Distributed File System) and BGL (IBM Blue Gene/L supercomputers system). ADR successfully mined 87 and 669 numerical relations from the logs and used them to detect anomalies with high precision and recall. For online anomaly detection, ADR employs PSO (Particle Swarm Optimization) to find the optimal sliding windows' size and achieves fast anomaly detection.</div><div>The experimental results confirm that ADR is effective for both offline and online anomaly detection. </div>

Anomaly Detection via Mining Numerical Workflow Relations from Logs

<div>Complex software intensive systems, especially distributed systems, generate logs for troubleshooting. The logs are text messages recording system events, which can help engineers determine the system's runtime status. This paper proposes a novel approach named ADR (stands for Anomaly Detection by workflow Relations) that employs matrix nullspace to mine numerical relations from log data. The mined relations can be used for both offline and online anomaly detection and facilitate fault diagnosis. We have evaluated ADR on log data collected from two distributed systems, HDFS (Hadoop Distributed File System) and BGL (IBM Blue Gene/L supercomputers system). ADR successfully mined 87 and 669 numerical relations from the logs and used them to detect anomalies with high precision and recall. For online anomaly detection, ADR employs PSO (Particle Swarm Optimization) to find the optimal sliding windows' size and achieves fast anomaly detection.</div><div>The experimental results confirm that ADR is effective for both offline and online anomaly detection. </div>

Tamanhos de Requisições de E/S de Aplicações HPC em um Supercomputador

Este estudo busca identificar os tamanhos de requisições de entrada e saída mais comuns utilizados por aplicações HPC em ambientes de grande escala. Para isso, utilizamos dados de um ano inteiro de caracterização com a ferramenta Darshan no supercomputador Intrepid Blue Gene/P. Ao identificar os diferentes padrões de acesso e os tamanhos de requisições, contribuímos para que novas técnicas de otimização possam ser testadas e avaliadas considerando os tamanhos de requisições semelhantes ao encontrado nestes ambientes.

Overhead of using spare nodes

With the increasing fault rate on high-end supercomputers, the topic of fault tolerance has been gathering attention. To cope with this situation, various fault-tolerance techniques are under investigation; these include user-level, algorithm-based fault-tolerance techniques and parallel execution environments that enable jobs to continue following node failure. Even with these techniques, some programs with static load balancing, such as stencil computation, may underperform after a failure recovery. Even when spare nodes are present, they are not always substituted for failed nodes in an effective way. This article considers the questions of how spare nodes should be allocated, how to substitute them for faulty nodes, and how much the communication performance is affected by such a substitution. The third question stems from the modification of the rank mapping by node substitutions, which can incur additional message collisions. In a stencil computation, rank mapping is done in a straightforward way on a Cartesian network without incurring any message collisions. However, once a substitution has occurred, the optimal node-rank mapping may be destroyed. Therefore, these questions must be answered in a way that minimizes the degradation of communication performance. In this article, several spare node allocation and failed node substitution methods will be proposed, analyzed, and compared in terms of communication performance following the substitution. The proposed substitution methods are named sliding methods. The sliding methods are analyzed by using our developed simulation program and evaluated by using the K computer, Blue Gene/Q (BG/Q), and TSUBAME 2.5. It will be shown that when failures occur, the stencil communication performance on the K and BG/Q can be slowed around 10 times depending on the number of node failures. The barrier performance on the K can be cut in half. On BG/Q, barrier performance can be slowed by a factor of 10. Further, it will also be shown that almost no such communication performance degradation can be seen on TSUBAME 2.5. This is because TSUBAME 2.5 has an Infiniband network connected with a FatTree topology, while the K computer and BG/Q have dedicated Cartesian networks. Thus, the communication performance degradation depends on network characteristics.

Torus Pairwise Disjoint-Path Routing

Modern supercomputers include hundreds of thousands of processors and they are thus massively parallel systems. The interconnection network of a system is in charge of mutually connecting these processors. Recently, the torus has become a very popular interconnection network topology. For example, the Fujitsu K, IBM Blue Gene/L, IBM Blue Gene/P, and Cray Titan supercomputers all rely on this topology. The pairwise disjoint-path routing problem in a torus network is addressed in this paper. This fundamental problem consists of the selection of mutually vertex disjoint paths between given vertex pairs. Proposing a solution to this problem has critical implications, such as increased system dependability and more efficient data transfers, and provides concrete implementation of green and sustainable computing as well as security, privacy, and trust, for instance, for the Internet of Things (IoT). Then, the correctness and complexities of the proposed routing algorithm are formally established. Precisely, in an n-dimensional k-ary torus ( n < k , k ≥ 5 ), the proposed algorithm connects c ( c ≤ n ) vertex pairs with mutually vertex-disjoint paths of lengths at most 2 k ( c − 1 ) + n ⌊ k / 2 ⌋ , and the worst-case time complexity of the algorithm is O ( n c 4 ) . Finally, empirical evaluation of the proposed algorithm is conducted in order to inspect its practical behavior.

Strong scaling for numerical weather prediction at petascale with the atmospheric model NUMA

Numerical weather prediction (NWP) has proven to be computationally challenging due to its inherent multiscale nature. Currently, the highest resolution global NWP models use a horizontal resolution of 9 km. At this resolution, many important processes in the atmosphere are not resolved. Needless to say, this introduces errors. In order to increase the resolution of NWP models, highly scalable atmospheric models are needed. The non-hydrostatic unified model of the atmosphere (NUMA), developed by the authors at the Naval Postgraduate School, was designed to achieve this purpose. NUMA is used by the Naval Research Laboratory, Monterey as the engine inside its next generation weather prediction system NEPTUNE. NUMA solves the fully compressible Navier–Stokes equations by means of high-order Galerkin methods (both spectral element as well as discontinuous Galerkin methods can be used). NUMA is capable of running middle and upper atmosphere simulations since it does not make use of the shallow-atmosphere approximation. This article presents the performance analysis and optimization of the spectral element version of NUMA. The performance at different optimization stages is analyzed using a theoretical performance model as well as measurements via hardware counters. Machine-independent optimization is compared to machine-specific optimization using Blue Gene (BG)/Q vector intrinsics. The best portable version of the main computations was found to be about two times slower than the best non-portable version. By using vector intrinsics, the main computations reach 1.2 PFlops on the entire IBM Blue Gene supercomputer Mira (12% of the theoretical peak performance). The article also presents scalability studies for two idealized test cases that are relevant for NWP applications. The atmospheric model NUMA delivers an excellent strong scaling efficiency of 99% on the entire supercomputer Mira using a mesh with 1.8 billion grid points. This allows running a global forecast of a baroclinic wave test case at a 3-km uniform horizontal resolution and double precision within the time frame required for operational weather prediction.