Optimizing tensor contraction expressions for hybrid CPU-GPU execution

Tensor contractions are generalized multidimensional matrix multiplication operations that widely occur in quantum chemistry. Efficient execution of tensor contractions on Graphics Processing Units (GPUs) requires several challenges to be addressed, including index permutation and small dimension-sizes reducing thread block utilization. Moreover, to apply the same optimizations to various expressions, we need a code generation tool. In this paper, we present our approach to automatically generate CUDA code to execute tensor contractions on GPUs, including management of data movement between CPU and GPU. To evaluate our tool, GPU-enabled code is generated for the most expensive contractions in CCSD(T), a key coupled cluster method, and incorporated into NWChem, a popular computational chemistry suite. For this method, we demonstrate speedup over a factor of 8.4 using one GPU as compared to one CPU core and over 2.6 when utilizing the entire system using hybrid CPU+GPU solution with 2 GPUs and 5 cores (instead of 7 cores per node). We further investigate tensor contraction code on a new series of GPUs, the Fermi GPUs, and provide several effective optimization algorithms. For the same computation of CCSD(T), on a cluster with Fermi GPUs, we achieve a speedup of 3.4 over a cluster with T10 GPUs. With a single Fermi GPU on each node, we achieve a speedup of 43 over the sequential CPU version.     

Parallel high-dimensional multi-objective feature selection for EEG classification with dynamic workload balancing on CPU–GPU architectures

Many bioinformatics applications that analyse large volumes of high-dimensional data comprise complex problems requiring metaheuristics approaches with different types of implicit parallelism. For example, although functional parallelism would be used to accelerate evolutionary algorithms, the fitness evaluation of the population could imply the computation of cost functions with data parallelism. This way, heterogeneous parallel architectures, including central processing unit (CPU) microprocessors with multiple superscalar cores and accelerators such as graphics processing units (GPUs) could be very useful. This paper aims to take advantage of such CPU–GPU heterogeneous architectures to accelerate electroencephalogram classification and feature selection problems by evolutionary multi-objective optimization, in the context of brain computing interface tasks. In this paper, we have used the OpenCL framework to develop parallel master-worker codes implementing an evolutionary multi-objective feature selection procedure in which the individuals of the population are dynamically distributed among the available CPU and GPU cores.     

A new MapReduce associative classifier based on a new storage format for large-scale imbalanced data

The process of knowledge discovery from big and high dimensional datasets has become a popular research topic. The classification problem is a key task in bioinformatics, business intelligence, decision science, astronomy, physics, etc. Building associative classifiers has been a notable research interest in recent years because of their superior accuracy. In associative classifiers, using under-sampling or over-sampling methods for imbalanced big datasets reduces accuracy or increases running time, respectively. Hence, there is a significant need to create efficient associative classifiers for imbalanced big data problems. These classifiers should be able to handle challenges such as memory usage, running time and efficiently exploring the search space. To this end, efficient calculation of measures is a primary objective for associative classifiers. In this paper, we propose a new efficient associative classifier for big imbalanced datasets. The proposed method is based on Rare-PEARs (a multi-objective evolutionary algorithm that efficiently discovers rare and reliable association rules) and is able to evaluate rules in a distributed manner by using a new storing data format. This format simplifies measures calculation and is fully compatible with the MapReduce programming model. We have applied the proposed method (RPII) on a well-known big dataset (ECBDL’14) and have compared our results with seven other learning methods. The experimental results show that RPII outperform other methods in sensitivity and final score measures (the values of sensitivity and final score measures were approximately 0.74 and 0.54 respectively). The results demonstrate that the proposed method is a good candidate for large-scale classification problems; furthermore, it achieves reasonable execution time when the target platform is a typical computer clusters.     

小杜鹃对小鳞胸鹪鹛的巢寄生

鸟类的巢寄生现象一直被作为生物协同演化的典型模式系统之一。对杜鹃选择宿主及其巢寄生情况进行调查和观测,能够为协同演化研究提供重要基础资料。2015年7月,我们在四川省雷波县发现一个被杜鹃寄生的鸟巢。通过野外观测和分子生物学检测,确定宿主为小鳞胸鹪鹛(Pnoepyga pusilla),而寄生者为小杜鹃(Cuculus poliocephalus)。     

A Missing Data Approach to Correct for Direct and Indirect Range Restrictions with a Dichotomous Criterion: A Simulation Study

A recurring methodological problem in the evaluation of the predictive validity of selection methods is that the values of the criterion variable are available for selected applicants only. This so-called range restriction problem causes biased population estimates. Correction methods for direct and indirect range restriction scenarios have widely studied for continuous criterion variables but not for dichotomous ones. The few existing approaches are inapplicable because they do not consider the unknown base rate of success. Hence, there is a lack of scientific research on suitable correction methods and the systematic analysis of their accuracies in the cases of a naturally or artificially dichotomous criterion. We aim to overcome this deficiency by viewing the range restriction problem as a missing data mechanism. We used multiple imputation by chained equations to generate complete criterion data before estimating the predictive validity and the base rate of success. Monte Carlo simulations were conducted to investigate the accuracy of the proposed correction in dependence of selection ratio, predictive validity, and base rate of success in an experimental design. In addition, we compared our proposed missing data approach with Thorndike’s well-known correction formulas that have only been used in the case of continuous criterion variables so far. The results show that the missing data approach is more accurate in estimating the predictive validity than Thorndike’s correction formulas. The accuracy of our proposed correction increases as the selection ratio and the correlation between predictor and criterion increase. Furthermore, the missing data approach provides a valid estimate of the unknown base rate of success. On the basis of our findings, we argue for the use of multiple imputation by chained equations in the evaluation of the predictive validity of selection methods when the criterion is dichotomous.     

Efficient computation of spaced seed hashing with block indexing

Background

Spaced-seeds, i.e. patterns in which some fixed positions are allowed to be wild-cards, play a crucial role in several bioinformatics applications involving substrings counting and indexing, by often providing better sensitivity with respect to k-mers based approaches. K-mers based approaches are usually fast, being based on efficient hashing and indexing that exploits the large overlap between consecutive k-mers. Spaced-seeds hashing is not as straightforward, and it is usually computed from scratch for each position in the input sequence. Recently, the FSH (Fast Spaced seed Hashing) approach was proposed to improve the time required for computation of the spaced seed hashing of DNA sequences with a speed-up of about 1.5 with respect to standard hashing computation.

Results

In this work we propose a novel algorithm, Fast Indexing for Spaced seed Hashing (FISH), based on the indexing of small blocks that can be combined to obtain the hashing of spaced-seeds of any length. The method exploits the fast computation of the hashing of runs of consecutive 1 in the spaced seeds, that basically correspond to k-mer of the length of the run.

Conclusions

We run several experiments, on NGS data from simulated and synthetic metagenomic experiments, to assess the time required for the computation of the hashing for each position in each read with respect to several spaced seeds. In our experiments, FISH can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.9x to 6.03x, depending on the structure of the spaced seeds.

     

Errors in egg-laying by female Common Cuckoo Cuculus canorus in nests of its common host

Dozens of studies have documented that brood parasites are well adapted to a brood parasitic lifestyle but not all parasitism events are successful. Co-evolution between brood parasites and their hosts is a dynamic process so it is reasonable to expect that a female brood parasite may commit errors during egg deposition by laying her eggs outside the laying period of the host, with consequent impacts on her fitness. Using an extensive dataset from a long-term study, we evaluated egg-laying patterns and errors related to the timing of egg-laying in the Common Cuckoo Cuculus canorus (hereafter ‘Cuckoo’). Specifically, we tested whether the Cuckoo avoids laying before or on the day of host clutch initiation to reduce the risk of rejection of parasitic eggs, whether laying errors will be more frequent in periods with a lack of active host nests, and whether the laying errors will be more frequent in periods with intense Cuckoo parasitism and a consequent lack of suitable host nests. We found that about one-third of Cuckoo eggs were laid on the host clutch initiation day or 1 day before, and the percentage of Cuckoo eggs laid decreased thereafter. Surprisingly, the probability of Cuckoo egg acceptance by the hosts was not affected by the egg-laying stage of the host clutch. Errors in the timing of egg-laying with fatal consequences (i.e. those precluding Cuckoo hatching because of laying in incubated or deserted clutches) were recorded in about 5% of cases. Only laying date of a Cuckoo egg had a significant effect on the probability of errors, which increased during the breeding season. This may be related to the higher number of deserted and incubated host nests at the site at the end of the breeding season. Errors in egg-laying may be attributed to young and inexperienced females but also impaired body condition or intraspecific competition may cause this behaviour. Future studies, which will test these possible explanations, will help to understand better the mechanism of co-evolutionary arms races and differences between host specialist and generalist brood parasites in various host–parasite systems.     

Large Hawk‐Cuckoo Hierococcyx sparverioides parasitism on the Chinese Babax Babax lanceolatus may be an evolutionarily recent host–parasite system

We documented brood parasitism by the poorly studied Large Hawk‐Cuckoo on a previously unknown host species, the Chinese Babax. Furthermore, we describe a new egg colour for the Large Hawk‐Cuckoo. The parasitism rate of Chinese Babax nests over 4 years was 6.9% (11 of 159 nests), with significant temporal variation. The Large Hawk‐Cuckoo laid immaculate white eggs that appeared non‐mimetic to the blue Babax eggs, an impression that was confirmed by avian visual modelling. Nevertheless, most Cuckoo eggs were accepted by the host, suggesting that this host–parasite system may be evolutionarily recent.     

大杜鹃和东方大苇莺卵的人工孵卵期和孵化率比较

大杜鹃(Cuculus canorus)是一种专性巢寄生鸟类,进化出了一系列适应对策,如雏鸟普遍出壳较早等,以更好适应寄生生活。本研究使用恒温自动孵化箱对25枚大杜鹃卵和20枚其宿主东方大苇莺(Acrocephalus orientalis)卵进行人工孵化,并对孵卵期的卵重进行连续测量。结果表明,在人工孵化条件下,大杜鹃卵的孵化率(76%)极显著高于东方大苇莺(40%)(χ~2=25.144,df=1,P0.01)。尽管大杜鹃的卵鲜重(t=7.447,df=43,P0.01)和卵体积(t=8.817,df=43,P0.01)均极显著大于东方大苇莺,但两种鸟卵的孵卵期不存在显著性差异(t=1.006,df=16,P0.05)。     

Coupling SIMD and SIMT Architectures to Boost Performance of a Phylogeny-aware Alignment Kernel

ABSTRACT: BACKGROUND: Aligning short DNA reads to a reference sequence alignment is a prerequisite fordetecting their biological origin and analyzing them in a phylogenetic context. With thePaPaRa tool we introduced a dedicated dynamic programming algorithm forsimultaneously aligning short reads to reference alignments and correspondingevolutionary reference trees. The algorithm aligns short reads to phylogenetic profiles thatcorrespond to the branches of such a reference tree. The algorithm needs to perform animmense number of pairwise alignments. Therefore, we explore vector intrinsics andGPUs to accelerate the PaPaRa alignment kernel. RESULTS: We optimized and parallelized PaPaRa on CPUs and GPUs. Via SSE 4.1 SIMD (SingleInstruction, Multiple Data) intrinsics for x86 SIMD architectures and multi-threading, weobtained a 9-fold acceleration on a single core as well as linear speedups with respect tothe number of cores. The peak CPU performance amounts to 18.1 GCUPS (Giga CellUpdates per Second) using all four physical cores on an Intel i7 2600 CPU running at 3.4GHz. The average CPU performance (averaged over all test runs) is 12.33 GCUPS. Wealso used OpenCL to execute PaPaRa on a GPU SIMT (Single Instruction, MultipleThreads) architecture. A NVIDIA GeForce 560 GPU delivered peak and averageperformance of 22.1 and 18.4 GCUPS respectively. Finally, we combined the SIMD andSIMT implementations into a hybrid CPU-GPU system that achieved an accumulatedpeak performance of 33.8 GCUPS. CONCLUSIONS: This accelerated version of PaPaRa (available at www.exelixis-lab.org/software.html)provides a significant performance improvement that allows for analyzing larger datasetsin less time. We observe that state-of-the-art SIMD and SIMT architectures delivercomparable performance for this dynamic programming kernel when the "competingprogrammer approach" is deployed. Finally, we show that overall performance can besubstantially increased by designing a hybrid CPU-GPU system with appropriate loaddistribution mechanisms.     

中杜鹃寄生繁殖及雏鸟生长一例

2002年6月,在甘肃省莲花山自然保护区观察到中杜鹃在淡眉柳莺巢中寄生繁殖,本文从体重、羽毛特征、跗蟅量度和嘴裂斑特征等方面说明了中杜鹃雏鸟的鉴别特征,并描述了雏鸟的行为和生长情况。     

An example of character release in host selection and egg colour of cuckoos Cuculus spp. in Japan

We have studied in northern Japan the hitherto unidentified eggs of a species of cuckoo in the nests of the Bush Warbler Cettia diphone . The cuckoo in question appeared to be the Himalayan Cuckoo Cuculus saturatus which parasitizes mainly the Willow Warbler Phylloscopus occipitalis in southern Japan. The egg colour in this northern Cuckoo was chocolate-brown or orange-brown, similar to that of the Bush Warbler but unlike that of the southern Himalayan Cuckoo. Egg size was significantly larger than that of the southern Himalayan Cuckoo and instead similar to that of the Little Cuckoo C. poliocephalus which uses the same host species in southern Japan, to which the Little Cuckoo is confined. The shift in host species and egg colour in the northern Himalayan Cuckoo seems to be a case of character release in the absence of the Little Cuckoo.     

Mining-based associative image filtering using harmonic mean

With the development of IT convergence technologies, users can now more easily access useful information. These days, diverse and far-reaching information is being rapidly produced and distributed instantly in digitized format. Studies are continuously seeking to develop more efficient methods of delivering information to a greater number of users. Image filtering, which extracts features of interest from images, was developed to address the weakness of collaborative filtering, which is limited to superficial data analysis. However, image filtering has its own weakness of requiring complicated calculations to obtain the similarity between images. In this study, to resolve these problems, we propose associative image filtering based on the mining method utilizing the harmonic mean. Using data mining’s Apriori algorithm, this study investigated the association among preferred images from an associative image group and obtained a prediction based on user preference mean. In so doing, we observed a positive relationship between the various image preferences and the various distances between images’ color histograms. Preference mean was calculated based on the arithmetic mean, geometric mean, and harmonic mean. We found through performance analysis that the harmonic mean had the highest accuracy. In associative image filtering, we used the harmonic mean in order to anticipate preferences. In testing accuracy with MAE utilizing the proposed method, this study demonstrated an improvement of approximately 12 % on average compared to previous collaborative image filtering.     

Semantic retrieval in DNA-based memories with Gibbs energy models

At least three types of associative memories based on DNA-affinity have been proposed. Previously, we have quantified the quality of retrieval of genomic and abiotic information in simulation by comparison to state-of-the-art symbolic methods available, such as LSA (Latent Semantic Analysis). Their performance is poor when the evaluation criterion for DNA-affinity is a simple approximation of the Gibbs energy that governs duplex formation for retrievals. Here, we use a more realistic approximation of the Gibbs energy to improve semantic retrievals in DNA memories. Their performance is much closer to that of LSA, according to human expert ratings. With more realistic approximations of DNA affinity, performance is expected to improve for other, more adaptive associative memories with compaction in silico, and even more so with actual DNA molecules in vitro.     

High efficient sedimentary basin simulations on hybrid CPU-GPU clusters

The key to achieving high performance on a GPU-enhanced cluster is efficient exploitation of each GPU’s powerful computing capability. Moreover, rationally balancing the workload between CPUs and GPUs can release additional computing power, which arises from the CPUs. In this paper, we extend our earlier work on using a hybrid CPU-GPU cluster for real-world sedimentary basin simulation, by further improving the involved CUDA implementations. A thorough analysis of the achieved new performance is also carried out. By using 1024 GPUs and 12288 CPU cores together, our best CPU-GPU hybrid implementation is able to achieve a double-precision performance of 72.8 TFlops, in connection with simulations on a huge 131072×131072 mesh.     

Fast and reliable analysis of molecular motion using proximity relations and dimensionality reduction

The analysis of molecular motion starting from extensive sampling of molecular configurations remains an important and challenging task in computational biology. Existing methods require a significant amount of time to extract the most relevant motion information from such data sets. In this work, we provide a practical tool for molecular motion analysis. The proposed method builds upon the recent ScIMAP (Scalable Isomap) method, which, by using proximity relations and dimensionality reduction, has been shown to reliably extract from simulation data a few parameters that capture the main, linear and/or nonlinear, modes of motion of a molecular system. The results we present in the context of protein folding reveal that the proposed method characterizes the folding process essentially as well as ScIMAP. At the same time, by projecting the simulation data and computing proximity relations in a low-dimensional Euclidean space, it renders such analysis computationally practical. In many instances, the proposed method reduces the computational cost from several CPU months to just a few CPU hours, making it possible to analyze extensive simulation data in a matter of a few hours using only a single processor. These results establish the proposed method as a reliable and practical tool for analyzing motions of considerably large molecular systems and proteins with complex folding mechanisms.     

Transitory memory retrieval in a biologically plausible neural network model

A number of memory models have been proposed. These all have the basic structure that excitatory neurons are reciprocally connected by recurrent connections together with the connections with inhibitory neurons, which yields associative memory (i.e., pattern completion) and successive retrieval of memory. In most of the models, a simple mathematical model for a neuron in the form of a discrete map is adopted. It has not, however, been clarified whether behaviors like associative memory and successive retrieval of memory appear when a biologically plausible neuron model is used. In this paper, we propose a network model for associative memory and successive retrieval of memory based on Pinsky-Rinzel neurons. The state of pattern completion in associative memory can be observed with an appropriate balance of excitatory and inhibitory connection strengths. Increasing of the connection strength of inhibitory interneurons changes the state of memory retrieval from associative memory to successive retrieval of memory. We investigate this transition.     

Protein sequence classification using feature hashing

Recent advances in next-generation sequencing technologies have resulted in an exponential increase in the rate at which protein sequence data are being acquired. The k-gram feature representation, commonly used for protein sequence classification, usually results in prohibitively high dimensional input spaces, for large values of k. Applying data mining algorithms to these input spaces may be intractable due to the large number of dimensions. Hence, using dimensionality reduction techniques can be crucial for the performance and the complexity of the learning algorithms. In this paper, we study the applicability of feature hashing to protein sequence classification, where the original high-dimensional space is "reduced" by hashing the features into a low-dimensional space, using a hash function, i.e., by mapping features into hash keys, where multiple features can be mapped (at random) to the same hash key, and "aggregating" their counts. We compare feature hashing with the "bag of k-grams" approach. Our results show that feature hashing is an effective approach to reducing dimensionality on protein sequence classification tasks.     

Nest defence,enemy recognition and nest inspection behaviour of experimentally parasitized Reed Warblers Acrocephalus scirpaceus

Capsule Reed Warblers in a regularly parasitized population do not recognize Cuckoo Cuculus canorus as a special enemy and do not change their behaviour at nest immediately after being parasitized.

Aims To assess if an intruder near the nest influences the behaviour of the Cuckoo host.

Methods Host responses to Cuckoo, control Pigeon dummies and human intruder were observed. Host behaviour at 71 nests was video-recorded for 30 minutes at four experimental groups of nests: Cuckoo dummy, Cuckoo dummy + Cuckoo egg, Pigeon dummy, human intruder.

Results Reed Warblers did not respond differently to the Cuckoo and the control species. The experimental procedure had no significant effect on the behaviour of hosts during the study period. We were unable to find any differences in the time spent at the nest, clutch inspection behaviour and nest defence behaviour between morning and afternoon experimental groups. Our results do not support the hypothesis that afternoon laying by the Cuckoo is maintained by a selection pressure from the host. We observed no ejection or egg-pecking during the 30-min period after the experimental parasitism.

Conclusions Low aggression and non-specificity of host responses in our study area are in line with the fact that the Reed Warbler is an intermediate rejecter of Cuckoo eggs as expected from the spatial habitat structure hypothesis.     

H?herkuckuck (Clamator glandarius) — Brutvogel in Bulgarien

Summary Of the Great Spotted Cuckoo 15 records exist in Blugaria. Breeding has been supposed so far. In 1988, a fledgling Great Spotted Cuckoo was seen together with a family of Magpies in the southwestern part of the country. So breeding is confirmed.