P. Wittich

Asymmetry of investment in crop research leads to knowledge gaps and lost opportunities to accelerate genetic gain through identifying new sources and combinations of traits and alleles. On the basis of consultation with scientists from most major seed companies, we identified several research areas with three common features: (i) relatively underrepresented in the literature; (ii) high probability of boosting productivity in a wide range of crops and environments; and (iii) could be researched in 'precompetitive' space, leveraging previous knowledge, and thereby improving models that guide crop breeding and management decisions. Areas identified included research into hormones, recombination, respiration, roots, and source–sink, which, along with new opportunities in phenomics, genomics, and bioinformatics, make it more feasible to explore crop genetic resources and improve breeding strategies.

The Central Outer Tracker is a large cylindrical drift chamber constructed to replace Collider Detector at Fermilab's original central drift chamber for the higher luminosity expected for Run 2 at the Fermilab Tevatron. The chamber's drift properties are described in the context of meeting the operating requirements for Run 2. The design and construction of the chamber, the front-end readout electronics, and the high-voltage system are described in detail. Wire aging considerations are also discussed.

Abstract New developments in liquid scintillators, high-efficiency, fast photon detectors, and chromatic photon sorting have opened up the possibility for building a large-scale detector that can discriminate between Cherenkov and scintillation signals. Such a detector could reconstruct particle direction and species using Cherenkov light while also having the excellent energy resolution and low threshold of a scintillator detector. Situated deep underground, and utilizing new techniques in computing and reconstruction, this detector could achieve unprecedented levels of background rejection, enabling a rich physics program spanning topics in nuclear, high-energy, and astrophysics, and across a dynamic range from hundreds of keV to many GeV. The scientific program would include observations of low- and high-energy solar neutrinos, determination of neutrino mass ordering and measurement of the neutrino CP-violating phase $$\delta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>δ</mml:mi></mml:math> , observations of diffuse supernova neutrinos and neutrinos from a supernova burst, sensitive searches for nucleon decay and, ultimately, a search for neutrinoless double beta decay, with sensitivity reaching the normal ordering regime of neutrino mass phase space. This paper describes Theia , a detector design that incorporates these new technologies in a practical and affordable way to accomplish the science goals described above.

MADS-box genes have been shown to play a major role in defining plant architecture. Recently, several MADS-box genes have been reported that are highly expressed in the ovule. However, only for the Petunia genes FBP7 and FBP11 has a function in defining ovule identity been shown. We have isolated a rice MADS-box gene named OsMADS13. Expression analysis has shown that this gene is highly expressed in developing ovules. In order to facilitate a detailed characterization of rice ovule-expressed genes, a comprehensive morphological description of ovule development in rice has been performed. The predicted amino acid sequence of OsMADS13 shows significant homology with ZAG2, a maize MADS-box gene, which is also expressed mainly in the ovule. Mapping of the gene in the rice genome showed that it is located on chromosome 12, which is syntenic to two maize regions where ZAG2 and its paralogous gene ZMM1 have been mapped. Our results suggest that OsMADS13 is the ortholog of ZAG2 and ZMM1 and might play a role in rice ovule and seed development. Dev. Genet. 25:237–244, 1999. © 1999 Wiley-Liss, Inc.

A maternally determined seed defect has been obtained by downregulation of the petunia MADS box genes Floral Binding Protein 7 (FBP7) and FBP11. These genes have been previously shown to play central roles in the determination of ovule identity. Aberrant development of the seed coat and consequent degeneration of the endosperm have been observed in transgenic plants in which these two genes are downregulated by cosuppression. Analysis of the expression pattern of FBP7 and FBP11 and genetic analysis confirmed the maternal inheritance of the phenotype. The FBP7 promoter was cloned and fused to reporter genes. One of these reporter genes was the BARNASE gene for targeted cell ablation. Our results indicate that FBP7 promoter activity is restricted to the seed coat of developing seeds and that it is completely silent in the gametophytically derived tissues. The mutants used in this study provided a unique opportunity to investigate one of the poorly understood aspects of seed development: the interaction of embryo, endosperm, and maternal tissues.

Summary The titan ( ttn ) mutants of Arabidopsis exhibit striking alterations in chromosome dynamics and cell division during seed development. Endosperm defects include aberrant mitoses and giant polyploid nuclei. Mutant embryos differ in cell size, morphology and viability, depending on the locus involved. Here we demonstrate that three TTN genes encode chromosome scaffold proteins of the condensin (SMC2) and cohesin (SMC1 and SMC3) classes. These proteins have been studied extensively in yeast and animal systems, where they modulate chromosome condensation, chromatid separation, and dosage compensation. Arabidopsis contains single copies of SMC1 and SMC3 cohesins. We used forward genetics to identify duplicate T‐DNA insertions in each gene. These mutants ( ttn7 and ttn8 ) have similar titan phenotypes: giant endosperm nuclei and arrested embryos with a few small cells. A single SMC2 knockout ( ttn3 ) was identified and confirmed by molecular complementation. The weak embryo phenotype observed in this mutant may result from expression of a related gene ( AtSMC2 ) with overlapping functions. Further analysis of titan mutants and the SMC gene family in Arabidopsis should provide clues to chromosome mechanics in plants and insights into the regulation of nuclear activity during endosperm development.

In most eukaryotes, the prospective chromosomal positions of meiotic crossovers are marked during meiotic prophase by protein complexes called late recombination nodules (LNs). In tomato (Solanum lycopersicum), a cytological recombination map has been constructed based on LN positions. We demonstrate that the mismatch repair protein MLH1 occurs in LNs. We determined the positions of MLH1 foci along the 12 tomato chromosome pairs (bivalents) during meiotic prophase and compared the map of MLH1 focus positions with that of LN positions. On all 12 bivalents, the number of MLH1 foci was approximately 70% of the number of LNs. Bivalents with zero MLH1 foci were rare, which argues against random failure of detecting MLH1 in the LNs. We inferred that there are two types of LNs, MLH1-positive and MLH1-negative LNs, and that each bivalent gets an obligate MLH1-positive LN. The two LN types are differently distributed along the bivalents. Furthermore, cytological interference among MLH1 foci was much stronger than interference among LNs, implying that MLH1 marks the positions of a subset of strongly interfering crossovers. Based on the distances between MLH1 foci or LNs, we propose that MLH1-positive and MLH1-negative LNs stem from the same population of weakly interfering precursors.

Summary Sweet sorghum accumulates large amounts of soluble sugar in its stem. However, a system‐based understanding of this carbohydrate allocation process is lacking. Here, we compared the dynamic transcriptome and metabolome between the conversion line R9188 and its two parents, sweet sorghum RIO and grain sorghum BT x406 that have contrasting sugar‐accumulating phenotypes. We identified two features of sucrose metabolism, stable concentrations of sugar phosphates in RIO and opposite trend of trehalose‐6‐phosphate (T6P) between RIO vs R9188/ BT x406. Integration of transcriptome and metabolome revealed R9188 is partially active in starch metabolism together with medium sucrose level, whereas sweet sorghum had the highest sucrose concentration and remained highly active in sucrose, starch, and cell wall metabolism post‐anthesis. Similar expression pattern of genes involved in sucrose degradation decreased the pool of sugar phosphates for precursors of starch and cell wall synthesis in R9188 and BT x406. Differential T6P signal between RIO vs R9188/ BT x406 is associated with introgression of T6P regulators from BT x406 into R9188, including C‐group bZIP and trehalose 6‐phosphate phosphatase ( TPP ). The inverted T6P signalling in R9188 appears to down‐regulate sucrose and starch metabolism partly through transcriptome reprogramming, whereas introgressed metabolic genes could be related to reduced cell wall metabolism. Our results show that coordinated primary metabolic pathways lead to high sucrose demand and accumulation in sweet sorghum, providing us with targets for genetic improvements of carbohydrate allocation in bioenergy crops.

All four members of a gene family, which are highly expressed in the cells of the female gametophyte (ZmES1--4: Zea mays embryo sac), were isolated from a cDNA library of maize egg cells. High expression of ZmES genes in the synergids around the micropylar region was detected in thin sections of maize ovaries. Single-cell RT--PCR analyses with the various cells of the female gametophyte confirmed the expression in synergids and also showed expression in the egg cell and central cell, and low expression in the antipodals. The expression of the whole gene family is suppressed after fertilization of the embryo sac, and expression in two-cell or later embryo stages or other tissues of maize could not be detected. In order to investigate ZmES mRNA gradients in the highly polarized and vacuolized cells of the maize embryo sac, a whole-mount in situ protocol with isolated single cells was developed: as for total RNA, ZmES transcripts are uniformly distributed in the cytoplasm of egg cell, synergids and central cell. ZmES genes encode small, cysteine-rich proteins with an N-terminal signal peptide, probably for translocation into the embryo sac cell wall. The four ZmES proteins display high sequence identity with each other, and the proposed tertiary structure of the mature peptides is similar to that of plant and animal defensins. The function of ZmES1-4 during the fertilization process is discussed.

To study the molecular regulation of nectary development, we cloned NEC1, a gene predominantly expressed in the nectaries of Petunia hybrida, by using the differential display RT-PCR technique. The secondary structure of the putative NEC1 protein is reminiscent of a transmembrane protein, indicating that the protein is incorporated into the cell membrane or the cytoplast membrane. Immunolocalization revealed that NEC1 protein is present in the nectaries. Northern blot analyses showed that NEC1 is highly expressed in nectary tissue and weakly in the stamen. GUS expression driven by the NEC1 promoter revealed GUS activity in the outer nectary parenchyma cells, the upper part of the filament and the anther stomium. The same expression pattern was observed in Brassica napus. GUS expression was observed as blue spots on the surface of very young nectaries that do not secrete nectar and do accumulate starch. GUS expression was highest in open flowers in which active secretion of nectar and starch hydrolysis had taken place. Ectopic expression of NEC1 resulted in transgenic plants that displayed a phenotype with leaves having 3-4 times more phloem bundles in mid-veins than the wild-type Petunia. The possible role of NEC1 gene in sugar metabolism and nectar secretion is discussed.

Summary The molecular and genetic control of inflorescence and flower development has been studied in great detail in model dicotyledonous plants such as Arabidopsis and Antirrhinum . In contrast, little is known about these important developmental steps in monocotyledonous species. Here we report the analysis of the Zea mays mutant branched silkless1–2 (bd1–2) , allelic to bd1 , which we have used as a tool to study the transition from spikelet to floret development in maize. Floret development is blocked in the female inflorescence (the ear) of bd1–2 plants, whereas florets develop almost normally in the male inflorescence (the tassel). Detailed phenotypic analyses indicate that in bd1–2 mutants ear inflorescence formation initiates normally, however, the spikelet meristems do not proceed to form floret meristems. The ear spikelets, at anthesis, contain various numbers of spikelet‐like meristems and glume‐like structures. Furthermore, growth of branches from the base of the ear is often observed. Expression analyses show that the floral‐specific MADS box genes Zea mays AGAMOUS1 ( ZAG1 ), ZAG2 and Zea mays MADS 2 ( ZMM2 ) are not expressed in ear florets in bd1–2 mutants, whereas their expression in tassel florets is similar to that of wild type. Taken together, these data indicate that the development from spikelet to floret meristem is differentially controlled in the ear and tassel in the monoecious grass species Zea mays , and that BRANCHED SILKLESS plays an important role in regulating the transition from spikelet meristem to floral meristem during the development of the female inflorescence of maize.

In this work, we present a study on ways that tracking algorithms can be improved with machine learning (ML). We base this study on the line segment tracking (LST) algorithm that we have designed to be naturally parallelized and vectorized in order to efficiently run on modern processors. LST has been developed specifically for the CMS Experiment at the LHC, towards the High Luminosity LHC (HL-LHC) upgrade. Moreover, we have already shown excellent efficiency and performance results as we iteratively improve LST, leveraging a full simulation of the CMS detector. At the same time, promising deep-learning-based tracking algorithms, such as Graph Neural Networks (GNNs), are being pioneered on the simplified TrackML dataset. These results suggest that parts of LST could be improved or replaced by ML. Thus, a thorough, step-by-step investigation of exactly how and where ML can be utilized, while still meeting realistic HL-LHC performance and efficiency constraints, is implemented as follows. First, a lightweight neural network is used to replace and improve upon explicitly defined track quality selections. This neural network is shown to be highly efficient and robust to displaced tracks while having little-to-no impact on the runtime of LST. These results clearly establish that ML can be used to improve LST without penalty. Next, exploratory studies of GNN track-building algorithms are described. In particular, low-level track objects from LST are considered as nodes in a graph, where edges represent higher-level objects or even entire track candidates. Then, an edge-classifier GNN is trained, and the efficiency of the resultant edge scores is compared with that of the existing LST track quality selections. These GNN studies provide insights into the practicality and performance of using more ambitious and complex ML algorithms for HL-LHC tracking at the CMS Experiment.

mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both threadand data-level parallelism. In the past few years the project transitioned from the R&amp;D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain. Prospects for an mkFit implementation of the track fit are also discussed.

The Large Hadron Collider (LHC) will be upgraded to Highluminosity LHC, increasing the number of simultaneous proton-proton collisions (pileup, PU) by several-folds. The harsher PU conditions lead to exponentially increasing combinatorics in charged particle tracking, placing a large demand on the computing resources. The projection on required computing resources exceeds the computing budget with the current algorithms running on single-thread CPUs. Motivated by the rise of heterogeneous computing in high-performance computing centers, we present Line Segment Tracking (LST), a highly parallelizeable algorithm that can run efficiently on GPUs and is being integrated to the CMS experiment central software. The usage of Alpaka framework for the algorithm implementation allows better portability of the code to run on different types of commercial parallel processors allowing flexibility on which processors to purchase for the experiment in the future. To verify a similar computational performance with a native solution, the Alpaka implementation is compared with a CUDA one on a NVIDIA Tesla V100 GPU. The algorithm creates short track segments in parallel, and progressively form higher level objects by linking segments that are consistent with genuine physics track hypothesis. The computing and physics performance are on par with the latest, multi-CPU versions of existing CMS tracking algorithms.

Sucrose synthase is usually localized by immunocytochemistry, but this method does not show the actual activity of the localized enzyme. A histochemical assay is presented here showing the activity of sucrose synthase by tetrazolium salt precipitation on sections of developing maize kernels. The advantages of the assay are a high sensitivity for low amounts of active sucrose synthase and the independence of specific antibodies. In this study the activity of endosperm sucrose synthase is shown to move gradually from the apical part of the endosperm to the basal endosperm during kernel development. This shift in sucrose synthase activity correlates well with the localization of starch synthesis during kernel development. The assay also shows the early loss of activity in the aleurone layer bordering the embryo, and a loss of activity in the apical aleurone during the final stage of kernel development while the enzyme was still found by immunocytochemistry. This is in contrast to a high sucrose synthase activity in the epithelium of the scutellum, where hardly any labelling was found with antibodies against maize sucrose synthase. Low sucrose synthase activities were found in the pericarp and pedicel parenchyma. Possible functions of the high and low activity patterns in the developing maize kernels and differences between the enzyme assay and immunocytochemistry are discussed.

We describe the new CDF Level 2 Trigger, which was commissioned during Spring 2005. The upgrade was necessitated by several factors that included increased bandwidth requirements, in view of the growing instantaneous luminosity of the Tevatron, and the need for a more robust system, since the older system was reaching the limits of maintainability. The challenges in designing the new system were interfacing with many different upstream detector subsystems, processing larger volumes of data at higher speed, and minimizing the impact on running the CDF experiment during the system commissioning phase. To meet these challenges, the new system was designed around a general purpose motherboard, the PULSAR, which is instrumented with powerful FPGAs and modern SRAMs, and which uses mezzanine cards to interface with upstream detector components and an industry standard data link (S-LINK) within the system.

Crop productivity needs to substantially increase to meet global food and feed demand for a rapidly growing world population. Agricultural technology developers are pursuing a variety of approaches based on both traditional technologies such as genetic improvement, pest control and mechanization as well as new technologies such as genomics, gene manipulation and environmental modelling to develop crops that are capable of meeting growing demand. Photosynthesis is a key biochemical process that, many suggest, is not yet optimized for industrial agriculture or the modern global environment. We are interested in identifying control points in maize photoassimilation that are amenable to gene manipulation to improve overall productivity. Our approach encompasses: developing and using novel gene discovery techniques, translating our discoveries into traits and evaluating each trait in a stepwise manner that reflects a modern production environment. Our aim is to provide step change advancement in overall crop productivity and deliver this new technology into the hands of growers.This article is part of the themed issue 'Enhancing photosynthesis in crop plants: targets for improvement'.

The adoption of large-scale distributed computing presents new opportunities and challenges for the physicists analyzing data from the Large Hadron Collider experiments. With petabytes of data to manage, effective use of provenance is critical to understanding the results.

We present the development and validation of a new multivariate $b$ jet identification algorithm ("$b$ tagger") used at the CDF experiment at the Fermilab Tevatron. At collider experiments, $b$ taggers allow one to distinguish particle jets containing $B$ hadrons from other jets. Employing feed-forward neural network architectures, this tagger is unique in its emphasis on using information from individual tracks. This tagger not only contains the usual advantages of a multivariate technique such as maximal use of information in a jet and tunable purity/efficiency operating points, but is also capable of evaluating jets with only a single track. To demonstrate the effectiveness of the tagger, we employ a novel method wherein we calculate the false tag rate and tag efficiency as a function of the placement of a lower threshold on a jet's neural network output value in $Z+1$ jet and $t\bar{t}$ candidate samples, rich in light flavor and $b$ jets, respectively.

Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. In order to achieve the theoretical performance gains of these processors, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-LHC), for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are those based on a Kalman filter approach. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust, and are in use today at the LHC. Given the utility of the Kalman filter in track finding, we have begun to port these algorithms to parallel architectures, namely Intel Xeon and Xeon Phi. We report here on our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a simplified experimental environment.

One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filter-based methods for highly parallel, many-core SIMD architectures that are now prevalent in high-performance hardware. In this paper, we discuss the design and performance of the improved tracking algorithm, referred to as MKFIT. A key piece of the algorithm is the MATRIPLEX library, containing dedicated code to optimally vectorize operations on small matrices. The physics performance of the MKFIT algorithm is comparable to the nominal CMS tracking algorithm when reconstructing tracks from simulated proton-proton collisions within the CMS detector. We study the scaling of the algorithm as a function of the parallel resources utilized and find large speedups both from vectorization and multi-threading. MKFIT achieves a speedup of a factor of 6 compared to the nominal algorithm when run in a single-threaded application within the CMS software framework.

During the High Luminosity LHC, the CMS detector will need charged particle tracking at the hardware trigger level to maintain a manageable trigger rate and achieve its physics goals. The tracklet approach is a track-finding algorithm based on a road-search algorithm that has been implemented on commercially available FPGA technology. The tracklet algorithm has achieved high performance in track-finding and completes tracking within 3.4 μs on a Xilinx Virtex-7 FPGA. An overview of the algorithm and its implementation on an FPGA is given, results are shown from a demonstrator test stand and system performance studies are presented.

Summary To study the molecular regulation of nectary development, we cloned NEC1 , a gene predominantly expressed in the nectaries of Petunia hybrida , by using the differential display RT–PCR technique. The secondary structure of the putative NEC1 protein is reminiscent of a transmembrane protein, indicating that the protein is incorporated into the cell membrane or the cytoplast membrane. Immunolocalization revealed that NEC1 protein is present in the nectaries. Northern blot analyses showed that NEC1 is highly expressed in nectary tissue and weakly in the stamen. GUS expression driven by the NEC1 promoter revealed GUS activity in the outer nectary parenchyma cells, the upper part of the filament and the anther stomium. The same expression pattern was observed in Brassica napus . GUS expression was observed as blue spots on the surface of very young nectaries that do not secrete nectar and do accumulate starch. GUS expression was highest in open flowers in which active secretion of nectar and starch hydrolysis had taken place. Ectopic expression of NEC1 resulted in transgenic plants that displayed a phenotype with leaves having 3–4 times more phloem bundles in mid‐veins than the wild‐type Petunia . The possible role of NEC1 gene in sugar metabolism and nectar secretion is discussed.

Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques including Cellular Automata or returning to Hough Transform. The most common track finding techniques in use today are however those based on the Kalman Filter. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust and are exactly those being used today for the design of the tracking system for HL-LHC. Our previous investigations showed that, using optimized data structures, track fitting with Kalman Filter can achieve large speedup both with Intel Xeon and Xeon Phi. We report here our further progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a realistic simulation setup.

We have developed a novel and generic open-source platform - Apollo - which simplifies the design of custom Advanced Telecommunications Computing Architecture (ATCA) blades by factoring the design into generic infrastructure and application-specific parts. The Apollo "Service Module" provides the required ATCA Intelligent Platform Management Controller, power entry and conditioning, a powerful system-on-module computer, and flexible clock and communications infrastructure. The Apollo "Command Module" is customized for each application and typically includes two large field-programmable gate arrays, several hundred optical fiber interfaces operating at speeds up to 28 Gbps, memories, and other supporting infrastructure. The command and service module boards can be operated together or independently on the bench without need for an ATCA shelf.

The high instantaneous luminosities expected following the upgrade of the Large Hadron Collider (LHC) to the High-Luminosity LHC (HL-LHC) pose major experimental challenges for the CMS experiment.A central component to allow efficient operation under these conditions is the reconstruction of charged particle trajectories and their inclusion in the hardwarebased trigger system.There are many challenges involved in achieving this: a large input data rate of about 20-40 Tb/s; processing a new batch of input data every 25 ns, each consisting of about 15,000 precise position measurements and rough transverse momentum measurements of particles ("stubs"); performing the pattern recognition on these stubs to find the trajectories; and producing the list of trajectory parameters within 4 µs.This paper describes a proposed solution to this problem, specifically, it presents a novel approach to pattern recognition and charged particle trajectory reconstruction using an all-FPGA solution.The results of an end-to-end demonstrator system, based on Xilinx Virtex-7 FPGAs, that meets timing and performance requirements are presented along with a further improved, optimized version of the algorithm together with its corresponding expected performance.

The first neutrino observations from the Sudbury Neutrino Observatory are presented from preliminary analyses. Based on energy, direction and location, the data in the region of interest appear to be dominated by 8B solar neutrinos, detected by the charged current reaction on deuterium and elastic scattering from electrons, with very little background. Measurements of radioactive backgrounds indicate that the measurement of all active neutrino types via the neutral current reaction on deuterium will be possible with small systematic uncertainties. Quantitative results for the fluxes observed with these reactions will be provided when further calibrations have been completed.

For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem in the High-Luminosity Large Hadron Collider (HL-LHC), for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offline. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port Kalman filter to NVIDIA GPUs.

Power density constraints are limiting the performance improvements of modern CPUs. To address this, we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The most common track finding techniques in use today are however those based on the Kalman Filter. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. We report the results of our investigations into the potential and limitations of these algorithms on the new parallel hardware.

Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. To stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-LHC), for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are those based on the Kalman Filter. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust, and are in use today at the LHC. We report on porting these algorithms to new parallel architectures. Our previous investigations showed that, using optimized data structures, track fitting with Kalman Filter can achieve large speedups both with Intel Xeon and Xeon Phi. We report here our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a realistic experimental environment.

Sucrose synthase is usually localized by immunocytochemistry, but this method does not show the actual activity of the localized enzyme. A histochemical assay is presented here showing the activity of sucrose synthase by tetrazolium salt precipitation on sections of developing maize kernels. The advantages of the assay are a high sensitivity for low amounts of active sucrose synthase and the independence of specific antibodies. In this study the activity of endosperm sucrose synthase is shown to move gradually from the apical part of the endosperm to the basal endosperm during kernel development. This shift in sucrose synthase activity correlates well with the localization of starch synthesis during kernel development. The assay also shows the early loss of activity in the aleurone layer bordering the embryo, and a loss of activity in the apical aleurone during the final stage of kernel development while the enzyme was still found by immunocytochemistry. This is in contrast to a high sucrose synthase activity in the epithelium of the scutellum, where hardly any labelling was found with antibodies against maize sucrose synthase. Low sucrose synthase activities were found in the pericarp and pedicel parenchyma. Possible functions of the high and low activity patterns in the developing maize kernels and differences between the enzyme assay and immunocytochemistry are discussed.

Faced with physical and energy density limitations on clock speed, contemporary microprocessor designers have increasingly turned to on-chip parallelism for performance gains. Algorithms should accordingly be designed with ample amounts of fine-grained parallelism if they are to realize the full performance of the hardware. This requirement can be challenging for algorithms that are naturally expressed as a sequence of small-matrix operations, such as the Kalman filter methods widely in use in high-energy physics experiments. In the High-Luminosity Large Hadron Collider (HL-LHC), for example, one of the dominant computational problems is expected to be finding and fitting charged-particle tracks during event reconstruction; today, the most common track-finding methods are those based on the Kalman filter. Experience at the LHC, both in the trigger and offline, has shown that these methods are robust and provide high physics performance. Previously we reported the significant parallel speedups that resulted from our efforts to adapt Kalman-filter-based tracking to many-core architectures such as Intel Xeon Phi. Here we report on how effectively those techniques can be applied to more realistic detector configurations and event complexity.

Abstract: The temporal and spatial distribution of the Petunia Floral Binding Proteins 7 and 11 (FBP7/11) were determined immunocytochemically during ovule initiation and development. In wild type plants, FBP7/11 were first detected in the placenta before ovule primordia were formed. At ovule primordium stage, FBP7/11 levels increased in the placenta and appeared in ovule primordia at the sites where integument primordia developed. At the megagametogenesis stage, FBP7/11 were present at high levels in the placenta, funicle and integument, but not in the nucellus or gametophyte. Transgenics with cosuppression of FBP7/11 formed normal ovule primordia on the placenta from which both normal ovules and carpel‐like structures developed. The amount of FBP7/11 was low in the ovules and undetectable in the carpel‐like structures. Plants with ectopic expression of FBP7/11 developed normal ovules on the placenta and, in addition, ovule‐ and carpel‐like structures on sepals. Placental and sepal ovules showed the same labeling pattern as observed in wild type ovules. FBP7/11 levels were, however, low or undetectable in the carpel‐like structures. The results indicate that FBP7/11 only have indirect roles in ovule primordium initiation. However, at least small quantities are needed for proper ovule differentiation. Thus, the amount of FBP7/11 is related to the type of development after primordium formation, i.e., towards the formation of real ovules or carpel‐like structures.

In this study we document the development of the phytomelan layer in the outer epidermis of the outer integument ofGasteria verrucosa. Phytomelan has been described as a black, melanin-like substance which is chemically very inert. Using histochemical techniques we show that phytomelan development in the cell wall can be divided into three stages. The first stage is deposition of a callosic layer against the tangential wall, with simultaneous thickening of the adjacent parts of the radial walls. The second stage is the conversion of this callosic wall, which we call a tertiary wall, into a noncallosic inner and outer layer. The inner layer stains predominantly for cellulose and a little for pectin. The outer layer is of unknown composition, since it did not react with the stains that were used. In the third stage the outer tertiary layer becomes black, the phytomelan. The callosic wall deposited in the first developmental stage seems to function as a carbohydrate source and as a mould for the tertiary cell wall. The conversion of the callose in the second stage might be the result of penetration of substances which react with callose. All the components for phytomelan seem to be present in the outer layer before the conversion. Phenolics might be involved in this second conversion.

We report on the progress of our studies towards a Kalman filter track reconstruction algorithm with optimal performance on manycore architectures. The combinatorial structure of these algorithms is not immediately compatible with an efficient SIMD (or SIMT) implementation; the challenge for us is to recast the existing software so it can readily generate hundreds of shared-memory threads that exploit the underlying instruction set of modern processors. We show how the data and associated tasks can be organized in a way that is conducive to both multithreading and vectorization. We demonstrate very good performance on Intel Xeon and Xeon Phi architectures, as well as promising first results on Nvidia GPUs.

The upgrades of the Compact Muon Solenoid particle physics experiment at CERN's Large Hadron Collider provide a major challenge for the real-time collision data selection. This paper presents a novel approach to pattern recognition and charged particle trajectory reconstruction using an all-FPGA solution. The challenges include a large input data rate of about 20 to 40 Tbps, processing a new batch of input data every 25 ns, each consisting of about 10,000 precise position measurements of particles (`stubs'), perform the pattern recognition on these stubs to find the trajectories, and produce the list of parameters describing these trajectories within 4 μs. A proposed solution to this problem is described, in particular, the implementation of the pattern recognition and particle trajectory determination using an all-FPGA system. The results of an end-to-end demonstrator system based on Xilinx Virtex-7 FPGAs that meets timing and performance requirements are presented.

The High-Luminosity Large Hadron Collider at CERN will be characterized by greater pileup of events and higher occupancy, making the track reconstruction even more computationally demanding. Existing algorithms at the LHC are based on Kalman filter techniques with proven excellent physics performance under a variety of conditions. Starting in 2014, we have been developing Kalman-filter-based methods for track finding and fitting adapted for many-core SIMD processors that are becoming dominant in high-performance systems. This paper summarizes the latest extensions to our software that allow it to run on the realistic CMS-2017 tracker geometry using CMSSW-generated events, including pileup. The reconstructed tracks can be validated against either the CMSSW simulation that generated the detector hits, or the CMSSW reconstruction of the tracks. In general, the code’s computational performance has continued to improve while the above capabilities were being added. We demonstrate that the present Kalman filter implementation is able to reconstruct events with comparable physics performance to CMSSW, while providing generally better computational performance. Further plans for advancing the software are discussed.

The CDF data acquisition and trigger system is being upgraded to significantly increase the bandwidth for the upcoming high luminosity running of the Tevatron Collider (run IIb). This paper focuses on the upgrade for the level 2 (L2) trigger decision crate. This crate is at the heart of the L2 trigger system and has to interface with many different subsystems both upstream and downstream. The challenge of this upgrade is to have a uniform design to be able to interface with many different data paths upstream, merge and process the data at high speed for fast L2 trigger decision making, and minimize the impact on the running CDF experiment during the commissioning phase. In order to meet this challenge, the design philosophy of the upgrade is to use one type of general purpose motherboard, with a few powerful modern FPGAs and SRAMs, to interface any user data with any industrial standard link through the use of mezzanine cards. This general purpose motherboard, named "Pulsar" (PULSer And Recorder), is fully self-testable at board level as well as at system level. CERN S-LINK is chosen to allow Pulsar to communicate with commodity processors via high bandwidth, low latency S-LINK-to-PCI cards. Knowledge gained by using S-LINK at CDF will be transferable to and from the LHC community.

Over the next ten years, the physics reach of the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) will be greatly extended through increases in the instantaneous luminosity of the accelerator and large increases in the amount of collected data. Due to changes in the way Moore's Law computing performance gains have been realized in the past decade, an aggressive program of R&D is needed to ensure that the computing capability of CMS will be up to the task of collecting and analyzing this data.

Interest in parallel architectures applied to real time selections is growing in High Energy Physics (HEP) experiments. In this paper we describe performance measurements of Graphic Processing Units (GPUs) and Intel Many Integrated Core architecture (MIC) when applied to a typical HEP online task: the selection of events based on the trajectories of charged particles. We use as benchmark a scaled-up version of the algorithm used at CDF experiment at Tevatron for online track reconstruction - the SVT algorithm - as a realistic test-case for low-latency trigger systems using new computing architectures for LHC experiment. We examine the complexity/performance trade-off in porting existing serial algorithms to many-core devices. Measurements of both data processing and data transfer latency are shown, considering different I/O strategies to/from the parallel devices.

One of the most challenging computational problems in the Run 3 of the Large Hadron Collider (LHC) and more so in the High-Luminosity LHC (HL-LHC) is expected to be finding and fitting charged-particle tracks during event reconstruction. The methods used so far at the LHC and in particular at the CMS experiment are based on the Kalman filter technique. Such methods have shown to be robust and to provide good physics performance, both in the trigger and offline. In order to improve computational performance, we explored Kalman-filter-based methods for track finding and fitting, adapted for many-core SIMD architectures. This adapted Kalman-filter-based software, called "mkFit", was shown to provide a significant speedup compared to the traditional algorithm, thanks to its parallelized and vectorized implementation. The mkFit software was recently integrated into the offline CMS software framework, in view of its exploitation during the Run 3 of the LHC. At the start of the LHC Run 3, mkFit will be used for track finding in a subset of the CMS offline track reconstruction iterations, allowing for significant improvements over the existing framework in terms of computational performance, while retaining comparable physics performance. The performance of the CMS track reconstruction using mkFit at the start of the LHC Run 3 is presented, together with prospects of further improvement in the upcoming years of data taking.

mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain.

The Sudbury Neutrino Observatory (SNO) is a second generation real time solar neutrino water Cherenkov detector using 1000 tonnes of D/sub 2/O viewed by almost 10000 photomultiplier tubes 20 cm in diameter. The electronics chain is required to provide deadtimeless sub-nanosecond time and charge measurement for photomultiplier pulses in the range of 1-1000 photoelectrons. While the solar neutrino event rate is very low, the electronics chain must handle background rates in excess of 1 kHz and burst rates in excess of 1 MHz. The electronics chain is implemented using three full custom integrated circuits and commercial ADCs, memory, and logic. The DAQ interface is VME compatible. We will briefly describe the SNO detector and then concentrate on the design of the electronic system including novel features of the DAQ and trigger paths.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

One of the most important issues that particle physics experiments at hadron colliders have to solve is realtime selection of the most interesting events. Typical collision frequencies do not allow all events to be written to tape for offline analysis, and in most cases, only a small fraction can be saved. The most commonly used strategy is based on two or three selection levels, with the low level ones usually exploiting dedicated hardware to decide within a few to ten microseconds if the event should be kept or not. This strict time requirement has made the usage of commercial devices inadequate, but recent improvements to Graphics Processing Units (GPUs) have substantially changed the conditions. Thanks to their highly parallel, multi-threaded, multicore architecture with remarkable computational power and high memory bandwidth, these commercial devices may be used in scientific applications, among which the event selection system (trigger) in particular may benefit, even at low levels. This paper describes the results of an R&D project to study the performance of GPU technology for low latency applications, such as HEP fast tracking trigger algorithms. On two different setups, we measure the latency to transfer data to/from the GPU, exploring the timing of different I/O technologies on different GPU models. We then describe the implementation and the performance of a track fitting algorithm which mimics the CDF Silicon Vertex Tracker. These studies provide performance benchmarks to investigate the potential and limitations of GPUs for future real-time applications in HEP experiments.

The CMS experiment will collect data from the proton-proton collisions delivered by the Large Hadron Collider (LHC) at a centre-of-mass energy up to 14 TeV. The CMS trigger system is designed to cope with unprecedented luminosities and LHC bunch-crossing rates up to 40 MHz. The unique CMS trigger architecture only employs two trigger levels. The Level-1 trigger is implemented using custom electronics, while the High Level Trigger (HLT) is based on software algorithms running on a large cluster of commercial processors, the Event Filter Farm. We present the major functionalities of the CMS High Level Trigger system as of the starting of LHC beams operations in September 2008. The validation of the HLT system in the online environment with Monte Carlo simulated data and its commissioning during cosmic rays data taking campaigns are discussed in detail. We conclude with the description of the HLT operations with the first circulating LHC beams before the incident occurred the 19th September 2008.

Building particle tracks is the most computationally intense step of event reconstruction at the LHC. With the increased instantaneous luminosity and associated increase in pileup expected from the High-Luminosity LHC, the computational challenge of track finding and fitting requires novel solutions. The current track reconstruction algorithms used at the LHC are based on Kalman filter methods that achieve good physics performance. By adapting the Kalman filter techniques for use on many-core SIMD architectures such as the Intel Xeon and Intel Xeon Phi and (to a limited degree) NVIDIA GPUs, we are able to obtain significant speedups and comparable physics performance. New optimizations, including a dedicated post-processing step to remove duplicate tracks, have improved the algorithm's performance even further. Here we report on the current structure and performance of the code and future plans for the algorithm.

One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is finding and fitting particle tracks during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filterbased methods for highly parallel, many-core SIMD and SIMT architectures that are now prevalent in high-performance hardware. Previously we observed significant parallel speedups, with physics performance comparable to CMS standard tracking, on Intel Xeon, Intel Xeon Phi, and (to a limited extent) NVIDIA GPUs. While early tests were based on artificial events occurring inside an idealized barrel detector, we showed subsequently that our mkFit software builds tracks successfully from complex simulated events (including detector pileup) occurring inside a geometrically accurate representation of the CMS-2017 tracker. Here, we report on advances in both the computational and physics performance of mkFit, as well as progress toward integration with CMS production software. Recently we have improved the overall efficiency of the algorithm by preserving short track candidates at a relatively early stage rather than attempting to extend them over many layers. Moreover, mkFit formerly produced an excess of duplicate tracks; these are now explicitly removed in an additional processing step. We demonstrate that with these enhancements, mkFit becomes a suitable choice for the first iteration of CMS tracking, and eventually for later iterations as well. We plan to test this capability in the CMS High Level Trigger during Run 3 of the LHC, with an ultimate goal of using it in both the CMS HLT and offline reconstruction for the HL-LHC CMS tracker.

Interest in many-core architectures applied to real time selections is growing in High Energy Physics (HEP) experiments. In this paper we describe performance measurements of many-core devices when applied to a typical HEP online task: the selection of events based on the trajectories of charged particles. We use as benchmark a scaled-up version of the algorithm used at CDF experiment at Tevatron for online track reconstruction - the SVT algorithm - as a realistic test-case for low-latency trigger systems using new computing architectures for LHC experiment. We examine the complexity/performance trade-off in porting existing serial algorithms to many-core devices. We measure performance of different architectures (Intel Xeon Phi and AMD GPUs, in addition to NVidia GPUs) and different software environments (OpenCL, in addition to NVidia CUDA). Measurements of both data processing and data transfer latency are shown, considering different I/O strategies to/from the many-core devices.

The challenging conditions of the High-Luminosity LHC require tailored hardware designs for the trigger and data acquisition systems. The Apollo platform features a "Service Module" with a powerful system-on-module computer that provides standard ATCA communications and application-specific "Command Module"s with large FPGAs and high-speed optical fiber links. The CMS version of Apollo will be used for the track finder and the pixel readout. It features up to two large FPGAs and more than 100 optical links with speeds up to 25\,Gb/s. We study carefully the design and performance of the board by using customized firmware to test power consumption, heat dissipation, and optical link integrity. This paper presents the results of these performance tests, design updates, and future plans.

This is an auspicious moment in experimental particle physics - there are large data samples at the Tevatron and a new energy regime being explored at the Large Hadron Collider with ever larger data samples. The coincidence of these two events suggests that we will soon be able to address the question: what lies beyond the standard model? Particle physics's current understanding of the universe is embodied in it. The model has been tested to extreme precision - better than a part in ten thousand - but we suspect that it is only an approximation, and that physics beyond this standard model will appear in the data of the Tevatron and LHC in the near future. This brief review touches on the status of searches for new physics at the time of the conference.

Power density constraints are limiting the performance improvements of modern CPUs. To address this, we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The most common track finding techniques in use today are however those based on the Kalman Filter. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. We report the results of our investigations into the potential and limitations of these algorithms on the new parallel hardware.

Power density constraints are limiting the performance improvements of modern CPUs. To address this, we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The most common track finding techniques in use today are however those based on the Kalman Filter. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. We report the results of our investigations into the potential and limitations of these algorithms on the new parallel hardware.

In the time since the

Over the next ten years, the physics reach of the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) will be greatly extended through increases in the instantaneous luminosity of the accelerator and large increases in the amount of collected data. Due to changes in the way Moore's Law computing performance gains have been realized in the past decade, an aggressive program of R&amp;D is needed to ensure that the computing capability of CMS will be up to the task of collecting and analyzing this data.

The Sudbury Neutrino Observatory (SNO) is a second generation real time solar neutrino water Cherenkov detector using 1000 tonnes of D/sub 2/O viewed by almost 10,000 photomultiplier tubes 20 cm in diameter. The electronics chain is required to provide deadtimeless sub-nanosecond time and charge measurement for photomultiplier pulses in the range of 1-1000 photoelectrons. The electronics chain must handle background event rates in excess of 1 kHz and burst rates in excess of 1 MHz. The SNO electronics is implemented with three full custom integrated circuits and standard commercial ADCs, memory, and logic. The DAQ interface is VME compatible. We will describe the SNO electronic system design including novel features of the DAQ and trigger paths.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The Sudbury Neutrino Observatory (SNO) is a second generation solar neutrino water Cherenkov detector using 1000 tonnes of D/sub 2/O viewed by almost 10000 20 cm photomultiplier tubes. The observatory, located 6800 ' below ground in INCO Ltd's Creighton mine near Sudbury Ontario, recently began full time operation. The SNO electronics provides deadtimeless sub-ns time and 0.1-1000 photoelectrons (pe) of charge measurement. While the solar neutrino event rate is low, the electronics must handle backgrounds in excess of 1 kHz and bursts in excess of 1 MHz. The integrated trigger system handles multiple independent triggers via 10000:1 analog sums. The electronics use three full custom integrated circuits plus standard commercial chips. There are 14 different printed circuit boards mounted in custom crates and racks. The DAQ interface is VME compatible.

Faced with physical and energy density limitations on clock speed, contemporary microprocessor designers have increasingly turned to on-chip parallelism for performance gains. Algorithms should accordingly be designed with ample amounts of fine-grained parallelism if they are to realize the full performance of the hardware. This requirement can be challenging for algorithms that are naturally expressed as a sequence of small-matrix operations, such as the Kalman filter methods widely in use in high-energy physics experiments. In the High-Luminosity Large Hadron Collider (HL-LHC), for example, one of the dominant computational problems is expected to be finding and fitting charged-particle tracks during event reconstruction; today, the most common track-finding methods are those based on the Kalman filter. Experience at the LHC, both in the trigger and offline, has shown that these methods are robust and provide high physics performance. Previously we reported the significant parallel speedups that resulted from our efforts to adapt Kalman-filter-based tracking to many-core architectures such as Intel Xeon Phi. Here we report on how effectively those techniques can be applied to more realistic detector configurations and event complexity.

Faced with physical and energy density limitations on clock speed, contemporary microprocessor designers have increasingly turned to on-chip parallelism for performance gains. Algorithms should accordingly be designed with ample amounts of fine-grained parallelism if they are to realize the full performance of the hardware. This requirement can be challenging for algorithms that are naturally expressed as a sequence of small-matrix operations, such as the Kalman filter methods widely in use in high-energy physics experiments. In the High-Luminosity Large Hadron Collider (HL-LHC), for example, one of the dominant computational problems is expected to be finding and fitting charged-particle tracks during event reconstruction; today, the most common track-finding methods are those based on the Kalman filter. Experience at the LHC, both in the trigger and offline, has shown that these methods are robust and provide high physics performance. Previously we reported the significant parallel speedups that resulted from our efforts to adapt Kalman-filter-based tracking to many-core architectures such as Intel Xeon Phi. Here we report on how effectively those techniques can be applied to more realistic detector configurations and event complexity.

This chapter contains sections titled: Introduction Plant Responses to Environmental Stresses Transcriptome Analysis of Abiotic Stress Responses Gene Network of Universal Abiotic Stress Response Conclusions References

The major challenge posed by the high instantaneous luminosity in the High Luminosity LHC (HL-LHC) motivates efficient and fast reconstruction of charged particle tracks in a high pile-up environment. While there have been efforts to use modern techniques like vectorization to improve the existing classic Kalman Filter based reconstruction algorithms, Line Segment Tracking takes a fundamentally different approach by doing a bottom-up reconstruction of tracks. Small track stubs from adjoining detector regions are constructed, and then these track stubs that are consistent with typical track trajectories are successively linked. Since the production of these track stubs is localized, they can be made in parallel, which lends way into using architectures like GPUs and multi-CPUs to take advantage of the parallelism. The algorithm is implemented in the context of the CMS Phase-2 Tracker and runs on NVIDIA Tesla V100 GPUs. Good physics and timing performance has been obtained, and stepping stones for the future are elaborated.

The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) will produce particle collisions with up to 200 simultaneous proton-proton interactions. These unprecedented conditions will create a combinatorial complexity for charged-particle track reconstruction that demands a computational cost that is expected to surpass the projected computing budget using conventional CPUs. Motivated by this and taking into account the prevalence of heterogeneous computing in cutting-edge High Performance Computing centers, we propose an efficient, fast and highly parallelizable bottom-up approach to track reconstruction for the HL-LHC, along with an associated implementation on GPUs, in the context of the Phase 2 CMS outer tracker. Our algorithm, called Segment Linking (or Line Segment Tracking), takes advantage of localized track stub creation, combining individual stubs to progressively form higher level objects that are subject to kinematical and geometrical requirements compatible with genuine physics tracks. The local nature of the algorithm makes it ideal for parallelization under the Single Instruction, Multiple Data paradigm, as hundreds of objects can be built simultaneously. The computing and physics performance of the algorithm has been tested on an NVIDIA Tesla V100 GPU, already yielding efficiency and timing measurements that are on par with the latest, multi-CPU versions of existing CMS tracking algorithms.

Abstract The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) will produce particle collisions with up to 200 simultaneous proton-proton interactions. These unprecedented conditions will create a combinatorial complexity for charged-particle track reconstruction that demands a computational cost that is expected to surpass the projected computing budget using conventional CPUs. Motivated by this and taking into account the prevalence of heterogeneous computing in cutting-edge High Performance Computing centers, we propose an efficient, fast and highly parallelizable bottom-up approach to track reconstruction for the HL-LHC, along with an associated implementation on GPUs, in the context of the Phase 2 CMS outer tracker. Our algorithm, called Segment Linking (or Line Segment Tracking), takes advantage of localized track stub creation, combining individual stubs to progressively form higher level objects that are subject to kinematical and geometrical requirements compatible with genuine physics tracks. The local nature of the algorithm makes it ideal for parallelization under the Single Instruction, Multiple Data paradigm, as hundreds of objects can be built simultaneously. The computing and physics performance of the algorithm has been tested on an NVIDIA Tesla V100 GPU, already yielding efficiency and timing measurements that are on par with the latest, multi-CPU versions of existing CMS tracking algorithms.

Seeds assure the plant the onset of a next generation and a way of dispersal. They consist of endosperm and an embryo (originating from gametophytic tissue), enveloped by a seed coat (sporophytic tissue). Plants generate different types of seeds. For instance, the endosperm may either be consumed by the embryo during seed development or retained for use by the embryo during germination. Differences in timing of endosperm digestion can be illustrated with broad bean ( Vicia faba ) and Gasteria verrucosa . Broad bean forms seeds in which the endosperm has been consumed by the fully developed embryo, while the embryo of Gasteria is less developed and surrounded by a large amount of endosperm for use during germination. An important factor in seed development is the distribution, storage, and utilization of carbohydrates, since carbohydrates are a major source of energy for cell growth. In this thesis the carbohydrate distribution is studied in developing ovules and seeds of maize ( Zea mays ) and Gasteria , by identifying the cells and tissues in which sucrose is degraded. Sucrose is the main carbohydrate supplied by these plants in the developing seeds. The sucrose degrading activity of the enzymes sucrose synthase and invertase indicates the destination of the sucrose transport (Chapters 9 and 10). Immunocytochemical and histochemical techniques are used for the localization of these enzymes in situ . The results obtained in this study on maize (Chapter 2 and 3) and Gasteria seed development (Chapters 4 and 5) show a general pattern of carbohydrate transport. First, the greatest amount of carbohydrates is applied for the development of the seed coat and nucellus (sporophytic tissues). An example of such a carbohydrate consuming process is the deposition of phytomelan in the seed coat of Gasteria . Phytomelan is a black cell wall component and chemically very inert. Histochemical and electron microscopy observations (Chapters 6 and 7) show that callose forms a mould for the deposition of phytomelan. The breakdown products of callose (glucose monomers and polymers) seem to be used for the synthesis of the phytomelan. Chemical analysis reveals that phytomelan is a complex polyphenolic polymer, and not a melanin (Chapter 8). Second, carbohydrate transport to the sporophytic tissues is followed by transport of most carbohydrates into the endosperm. These carbohydrates will be used for endosperm growth and for storage. Finally the main carbohydrate flow will go to the embryo. The pattern of carbohydrate usage observed in maize and Gasteria was used to generate a general model for angiosperm seed development (Chapter 10). The model explains differences between seeds by relating carbohydrate distribution during seed development to the timing of seed dispersal.

Building particle tracks is the most computationally intense step of event reconstruction at the LHC. With the increased instantaneous luminosity and associated increase in pileup expected from the High-Luminosity LHC, the computational challenge of track finding and fitting requires novel solutions. The current track reconstruction algorithms used at the LHC are based on Kalman filter methods that achieve good physics performance. By adapting the Kalman filter techniques for use on many-core SIMD architectures such as the Intel Xeon and Intel Xeon Phi and (to a limited degree) NVIDIA GPUs, we are able to obtain significant speedups and comparable physics performance. New optimizations, including a dedicated post-processing step to remove duplicate tracks, have improved the algorithm's performance even further. Here we report on the current structure and performance of the code and future plans for the algorithm.

in porting existing serial algorithms to many-core devices. Measurements of both data processing and data transfer latency are shown, considering different I/O strategies to/from the parallel devices.

Abstract In the High–Luminosity Large Hadron Collider (HL–LHC), one of the most challenging computational problems is expected to be finding and fitting charged-particle tracks during event reconstruction. The methods currently in use at the LHC are based on the Kalman filter. Such methods have shown to be robust and to provide good physics performance, both in the trigger and offline. In order to improve computational performance, we explored Kalman-filter-based methods for track finding and fitting, adapted for many-core SIMD (single instruction, multiple data) and SIMT (single instruction, multiple thread) architectures. Our adapted Kalman-filter-based software has obtained significant parallel speedups using such processors, e.g., Intel Xeon Phi, Intel Xeon SP (Scalable Processors) and (to a limited degree) NVIDIA GPUs. Recently, an effort has started towards the integration of our software into the CMS software framework, in view of its exploitation for the Run III of the LHC. Prior reports have shown that our software allows in fact for some significant improvements over the existing framework in terms of computational performance with comparable physics performance, even when applied to realistic detector configurations and event complexity. Here, we demonstrate that in such conditions physics performance can be further improved with respect to our prior reports, while retaining the improvements in computational performance, by making use of the knowledge of the detector and its geometry.

We present results from parallelizing the unpacking and clustering steps of the raw data from the silicon strip modules for reconstruction of charged particle tracks. Throughput is further improved by concurrently processing multiple events using nested OpenMP parallelism on CPU or CUDA streams on GPU. The new implementation along with earlier work in developing a parallelized and vectorized implementation of the combinatoric Kalman filter algorithm has enabled efficient global reconstruction of the entire event on modern computer architectures. We demonstrate the performance of the new implementation on Intel Xeon and NVIDIA GPU architectures.

The challenging conditions of the High-Luminosity LHC require tailored hardware designs for the trigger and data acquisition systems. The Apollo platform features a "Service Module" with a powerful system-on-module computer that provides standard ATCA communications and an application-specific "Command Module"s with large FPGAs and high-speed optical fiber links. The CMS version of Apollo will be used for the track finder and the pixel readout. It features up to two large FPGAs and more than 100 optical links with speeds up to 25\,Gb/s. We study carefully the design and performance of the board by using customized firmware to test power consumption, heat dissipation, and optical link integrity. This paper presents the results of these performance tests, design updates, and future plans.

The Sudbury Neutrino Observatory (SNO) is a 1000-tonne heavy water Cherenkov detector. Its usage of D2O as target allows the simultaneous measurements of the νe flux from B decay in the Sun and the total flux of all active neutrino species through the charged-current and the neutralcurrent interactions on the deuterons. Assuming the standard B shape, the νe component of the B solar neutrino flux is measured to be φe= 1.76 −0.05(stat.) +0.09 −0.09 (syst.)× 10 cms for a kinetic energy threshold of 5 MeV. The non-νecomponent is found to be φμτ= 3.41 +0.45 −0.45(stat.) +0.48 −0.45 (syst.)× 10 cms. This 5.3σ difference provides strong evidence for νe flavor transformation in the solar neutrino sector. The total active neutrino flux is measured with the neutral-current reaction at a neutrino energy threshold of 2.2 MeV. This flux is determined to be φNC= 5.09 +0.44 −0.43(stat.) +0.46 −0.43 (syst.) × 10 cms, and is consistent with solar model predictions. Assuming an undistorted B spectrum, the night minus day rate is 14.0±6.3(stat.) −1.4(sys.)% of the average rate in the charged-current channel. If the total active neutrino flux is constrained to have no asymmetry, the night-day asymmetry in the νe flux is found to be 7.0±4.9(stat.) −1.2(sys.)%. A global analysis of all the available solar neutrino data in terms of matter-enhanced oscillations of two active flavors strongly favors the Large Mixing Angle (LMA) solution. TTH01 XXX SLAC Summer Institute (SSI2002), Stanford, CA, 5-16 August, 2002

The Sudbury Neutrino Observatory (SNO) is a 1000-tonne heavy water Cherenkov detector. Its usage of D2O as target allows the simultaneous measurements of the νe flux from B decay in the Sun and the total flux of all active neutrino species through the charged-current and the neutralcurrent interactions on the deuterons. Assuming the standard B shape, the νe component of the B solar neutrino flux is measured to be φe= 1.76 −0.05(stat.) +0.09 −0.09 (syst.)× 10 cms for a kinetic energy threshold of 5 MeV. The non-νecomponent is found to be φμτ= 3.41 +0.45 −0.45(stat.) +0.48 −0.45 (syst.)× 10 cms. This 5.3σ difference provides strong evidence for νe flavor transformation in the solar neutrino sector. The total active neutrino flux is measured with the neutral-current reaction at a neutrino energy threshold of 2.2 MeV. This flux is determined to be φNC= 5.09 +0.44 −0.43(stat.) +0.46 −0.43 (syst.) × 10 cms, and is consistent with solar model predictions. Assuming an undistorted B spectrum, the night minus day rate is 14.0±6.3(stat.) −1.4(sys.)% of the average rate in the charged-current channel. If the total active neutrino flux is constrained to have no asymmetry, the night-day asymmetry in the νe flux is found to be 7.0±4.9(stat.) −1.2(sys.)%. A global analysis of all the available solar neutrino data in terms of matter-enhanced oscillations of two active flavors strongly favors the Large Mixing Angle (LMA) solution. TTH01 XXX SLAC Summer Institute (SSI2002), Stanford, CA, 5-16 August, 2002