scholarly journals High performance computing architectures analysis for gene networks inference

2019 ◽  
Author(s):  
Anderson Marco ◽  
Mario Gazziro ◽  
David Martins Jr
Keyword(s):  
Energy Consumption ◽  
Gene Networks ◽  
High Performance ◽  
Regulatory Networks ◽  
Optimal Solution ◽  
Cost Benefit ◽  
Transition Functions ◽  
Speed Up ◽  
Important Field ◽  
Acquisition Price

Modeling and inference of biological systems are an important field in computer science, presenting strong interdisciplinary aspects. In this context, the inference of gene regulatory networks and the analysis of their dynamics generated by their transition functions are important issues that demand substantial computational power. Because the algorithms that return the optimal solution have an exponential time cost, such algorithms only work for gene networks with only dozens of genes. However realistic gene networks present hundreds to thousands of genes, with some genes being hubs, i.e., their number of predictor genes are usually much higher than average. Therefore there is a need to develop ways to speed up the gene networks inference. This paper presents a benchmark involving GPUs and FPGAs to infer gene networks, analysing processing time, hardware cost acquisition, energy consumption and programming complexity. Overall Titan XP GPU achieved the best performance, but with a large cost regarding acquisition price when compared to R9 Nano GPU and DE1-SOC FPGA. In its turn, R9 Nano GPU presented the best cost-benefit regarding performance, acquisition price, energy consumption, and programming complexity, although DE1-SOC FPGA presented much smaller energy consumption.

scholarly journals An Open-Source Cloud-FPGA Gene Regulatory Accelerator

2021 ◽  
Author(s):  
Lucas Bragança ◽  
Jeronimo Penha ◽  
Michael Canesche ◽  
Dener Ribeiro ◽  
José Augusto M. Nacif ◽  
...  
Keyword(s):  
Open Source ◽  
Gene Regulatory Networks ◽  
High Performance ◽  
Regulatory Networks ◽  
Cloud Services ◽  
On Demand ◽  
Speed Up ◽  
Gene Regulatory ◽  
High Level ◽  
Better Than

FPGAs are suitable to speed up gene regulatory network (GRN) algorithms with high throughput and energy efficiency. In addition, virtualizing FPGA using hardware generators and cloud resources increases the computing ability to achieve on-demand accelerations across multiple users. Recently, Amazon AWS provides high-performance Cloud's FPGAs. This work proposes an open source accelerator generator for Boolean gene regulatory networks. The generator automatically creates all hardware and software pieces from a high-level GRN description. We evaluate the accelerator performance and cost for CPU, GPU, and Cloud FPGA implementations by considering six GRN models proposed in the literature. As a result, the FPGA accelerator is at least 12x faster than the best GPU accelerator. Furthermore, the FPGA reaches the best performance per dollar in cloud services, at least 5x better than the best GPU accelerator.

scholarly journals Purinergic ATP triggers moxibustion-induced local anti-nociceptive effect on inflammatory pain model

2021 ◽  
Author(s):  
Hai-Yan Yin ◽  
Ya-Peng Fan ◽  
Juan Liu ◽  
Dao-Tong Li ◽  
Jing Guo ◽  
...  
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Inflammatory Pain ◽  
Analgesic Effect ◽  
Intramuscular Injection ◽  
High Performance ◽  
Atp Hydrolysis ◽  
Purinergic Signalling ◽  
Pain Model ◽  
Speed Up

AbstractPurinergic signalling adenosine and its A1 receptors have been demonstrated to get involved in the mechanism of acupuncture (needling therapy) analgesia. However, whether purinergic signalling would be responsible for the local analgesic effect of moxibustion therapy, the predominant member in acupuncture family procedures also could trigger analgesic effect on pain diseases, it still remains unclear. In this study, we applied moxibustion to generate analgesic effect on complete Freund’s adjuvant (CFA)-induced inflammatory pain rats and detected the purine released from moxibustioned-acupoint by high-performance liquid chromatography (HPLC) approach. Intramuscular injection of ARL67156 into the acupoint Zusanli (ST36) to inhibit the breakdown of ATP showed the analgesic effect of moxibustion was increased while intramuscular injection of ATPase to speed up ATP hydrolysis caused a reduced moxibustion-induced analgesia. These data implied that purinergic ATP at the location of ST36 acupoint is a potentially beneficial factor for moxibustion-induced analgesia.

scholarly journals Quit When You Can: Efficient Evaluation of Ensembles by Optimized Ordering

2021 ◽  
Vol 17 (4) ◽  
pp. 1-20
Author(s):  
Serena Wang ◽  
Maya Gupta ◽  
Seungil You
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Combinatorial Optimization Problem ◽  
Joint Optimization ◽  
Early Stopping ◽  
Time Approximation ◽  
Approximation Bound ◽  
Evaluation Time ◽  
Speed Up ◽  
Real World Problems

Given a classifier ensemble and a dataset, many examples may be confidently and accurately classified after only a subset of the base models in the ensemble is evaluated. Dynamically deciding to classify early can reduce both mean latency and CPU without harming the accuracy of the original ensemble. To achieve such gains, we propose jointly optimizing the evaluation order of the base models and early-stopping thresholds. Our proposed objective is a combinatorial optimization problem, but we provide a greedy algorithm that achieves a 4-approximation of the optimal solution under certain assumptions, which is also the best achievable polynomial-time approximation bound. Experiments on benchmark and real-world problems show that the proposed Quit When You Can (QWYC) algorithm can speed up average evaluation time by 1.8–2.7 times on even jointly trained ensembles, which are more difficult to speed up than independently or sequentially trained ensembles. QWYC’s joint optimization of ordering and thresholds also performed better in experiments than previous fixed orderings, including gradient boosted trees’ ordering.

scholarly journals Single cell RNA-seq reveals genes vital to in vitro fertilized embryos and parthenotes in pigs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhi-Qiang Du ◽  
Hao Liang ◽  
Xiao-Man Liu ◽  
Yun-Hua Liu ◽  
Chonglong Wang ◽  
...  
Keyword(s):  
Embryo Development ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Rna Binding ◽  
Expression Patterns ◽  
Early Embryo ◽  
Specific Factor ◽  
Early Embryos ◽  
Genome Activation

AbstractSuccessful early embryo development requires the correct reprogramming and configuration of gene networks by the timely and faithful execution of zygotic genome activation (ZGA). However, the regulatory principle of molecular elements and circuits fundamental to embryo development remains largely obscure. Here, we profiled the transcriptomes of single zygotes and blastomeres, obtained from in vitro fertilized (IVF) or parthenogenetically activated (PA) porcine early embryos (1- to 8-cell), focusing on the gene expression dynamics and regulatory networks associated with maternal-to-zygote transition (MZT) (mainly maternal RNA clearance and ZGA). We found that minor and major ZGAs occur at 1-cell and 4-cell stages for both IVF and PA embryos, respectively. Maternal RNAs gradually decay from 1- to 8-cell embryos. Top abundantly expressed genes (CDV3, PCNA, CDR1, YWHAE, DNMT1, IGF2BP3, ARMC1, BTG4, UHRF2 and gametocyte-specific factor 1-like) in both IVF and PA early embryos identified are of vital roles for embryo development. Differentially expressed genes within IVF groups are different from that within PA groups, indicating bi-parental and maternal-only embryos have specific sets of mRNAs distinctly decayed and activated. Pathways enriched from DEGs showed that RNA associated pathways (RNA binding, processing, transport and degradation) could be important. Moreover, mitochondrial RNAs are found to be actively transcribed, showing dynamic expression patterns, and for DNA/H3K4 methylation and transcription factors as well. Taken together, our findings provide an important resource to investigate further the epigenetic and genome regulation of MZT events in early embryos of pigs.

scholarly journals Temporal concatenation for Markov decision processes

2021 ◽  
pp. 1-28
Author(s):  
Ruiyang Song ◽  
Kuang Xu
Keyword(s):  
Markov Decision Processes ◽  
Large Scale ◽  
Optimal Solution ◽  
Upper Bounds ◽  
Black Box ◽  
Decision Processes ◽  
Optimal Solutions ◽  
Wide Range ◽  
Markov Decision ◽  
Speed Up

We propose and analyze a temporal concatenation heuristic for solving large-scale finite-horizon Markov decision processes (MDP), which divides the MDP into smaller sub-problems along the time horizon and generates an overall solution by simply concatenating the optimal solutions from these sub-problems. As a “black box” architecture, temporal concatenation works with a wide range of existing MDP algorithms. Our main results characterize the regret of temporal concatenation compared to the optimal solution. We provide upper bounds for general MDP instances, as well as a family of MDP instances in which the upper bounds are shown to be tight. Together, our results demonstrate temporal concatenation's potential of substantial speed-up at the expense of some performance degradation.

scholarly journals Energy Saving Quantitative Analysis of Passive, Active, and Renewable Technologies in Different Climate Zones

2021 ◽  
Vol 11 (15) ◽  
pp. 7115
Author(s):  
Chul-Ho Kim ◽  
Min-Kyeong Park ◽  
Won-Hee Kang
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Energy Saving ◽  
High Performance ◽  
Energy Systems ◽  
Outdoor Air ◽  
Climate Zones ◽  
Renewable Energy Systems ◽  
Active Systems ◽  
High Performance Buildings

The purpose of this study was to provide a guideline for the selection of technologies suitable for ASHRAE international climate zones when designing high-performance buildings. In this study, high-performance technologies were grouped as passive, active, and renewable energy systems. Energy saving technologies comprising 15 cases were categorized into passive, active, and renewable energy systems. EnergyPlus v9.5.0 was used to analyze the contribution of each technology in reducing the primary energy consumption. The energy consumption of each system was analyzed in different climates (Incheon, New Delhi, Minneapolis, Berlin), and the detailed contributions to saving energy were evaluated. Even when the same technology is applied, the energy saving rate differs according to the climatic characteristics. Shading systems are passive systems that are more effective in hot regions. In addition, the variable air volume (VAV) system, combined VAV–energy recovery ventilation (ERV), and combined VAV–underfloor air distribution (UFAD) are active systems that can convert hot and humid outdoor temperatures to create comfortable indoor environments. In cold and cool regions, passive systems that prevent heat loss, such as high-R insulation walls and windows, are effective. Active systems that utilize outdoor air or ventilation include the combined VAV-economizer, the active chilled beam with dedicated outdoor air system (DOAS), and the combined VAV-ERV. For renewable energy systems, the ground source heat pump (GSHP) is more effective. Selecting energy saving technologies that are suitable for the surrounding environment, and selecting design strategies that are appropriate for a given climate, are very important for the design of high-performance buildings globally.

scholarly journals Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 554
Author(s):  
Suresh Kallam ◽  
Rizwan Patan ◽  
Tathapudi V. Ramana ◽  
Amir H. Gandomi
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Total Energy ◽  
Optimal Solution ◽  
Data Locality ◽  
Weighted Regression ◽  
Heterogeneous Environments ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Greedy Scheduling

Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods.

scholarly journals Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Lhassane Idoumghar ◽  
Mahmoud Melkemi ◽  
René Schott ◽  
Maha Idrissi Aouad
Keyword(s):  
Energy Consumption ◽  
Embedded Systems ◽  
Evolutionary Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Time Saving ◽  
Local Optima ◽  
Hybrid Evolutionary Algorithm ◽  
Multimodal Function Optimization ◽  
Multimodal Function

The paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called HPSO-SA, based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local-search ability. The proposed HPSO-SA algorithm is validated on ten standard benchmark multimodal functions for which we obtained significant improvements. The results are compared with these obtained by existing hybrid PSO-SA algorithms. In this paper, we provide also two versions of HPSO-SA (sequential and distributed) for minimizing the energy consumption in embedded systems memories. The two versions, of HPSO-SA, reduce the energy consumption in memories from 76% up to 98% as compared to Tabu Search (TS). Moreover, the distributed version of HPSO-SA provides execution time saving of about 73% up to 84% on a cluster of 4 PCs.

Application of High-Performance Computing to Numerical Simulation of Human Movement

1995 ◽  
Vol 117 (1) ◽  
pp. 155-157 ◽  
Author(s):  
F. C. Anderson ◽  
J. M. Ziegler ◽  
M. G. Pandy ◽  
R. T. Whalen
Keyword(s):  
Computer Architecture ◽  
High Performance ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Human Movement ◽  
Parallel Machine ◽  
Optimal Controls ◽  
Processing Machine ◽  
Vector Processing

We have examined the feasibility of using massively-parallel and vector-processing supercomputers to solve large-scale optimization problems for human movement. Specifically, we compared the computational expense of determining the optimal controls for the single support phase of gait using a conventional serial machine (SGI Iris 4D25), a MIMD parallel machine (Intel iPSC/860), and a parallel-vector-processing machine (Cray Y-MP 8/864). With the human body modeled as a 14 degree-of-freedom linkage actuated by 46 musculotendinous units, computation of the optimal controls for gait could take up to 3 months of CPU time on the Iris. Both the Cray and the Intel are able to reduce this time to practical levels. The optimal solution for gait can be found with about 77 hours of CPU on the Cray and with about 88 hours of CPU on the Intel. Although the overall speeds of the Cray and the Intel were found to be similar, the unique capabilities of each machine are better suited to different portions of the computational algorithm used. The Intel was best suited to computing the derivatives of the performance criterion and the constraints whereas the Cray was best suited to parameter optimization of the controls. These results suggest that the ideal computer architecture for solving very large-scale optimal control problems is a hybrid system in which a vector-processing machine is integrated into the communication network of a MIMD parallel machine.

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