Parallel Variant Research Articles

Note on Constrained Long Choice with Multiple Beginning Elements

Recently, Pasarkar et al. (2023) have introduced the new TFNP subclass called PLC that contains the class PPP; they also have proven that several search problems related to extremal combinatorial principles (e.g., Ramsey’s theorem and the sunflower lemma) belong to PLC. This paper discusses the complexity of the three generalizations of Constrained Long Choice, a PLC-complete problem. We first discuss the parallel variant: Given a Long Choice instance and m beginning elements, find mConstrained Long Choice solutions. We show that this variant is FP‖PLC-complete. Next, we discuss the iterative variant: Given a Constrained Long Choice instance, a process function that specifies the next beginning element depending on the current solution, and an iteration parameter T, find TLong Choice solutions satisfying the suitable conditions. We prove that this variant is FPPLC-complete. Finally, we consider the inductive variant, which is an iterative variant with the inductive principle. We prove that the inductive variant is not only PLC-hard but also PLS-hard, where the class PLS is the set of all search problems that are solvable by a local search method. Furthermore, we show that our iterative and inductive variants are closed under the Turing reduction.

Higher-Order Asynchronous Effects

We explore asynchronous programming with algebraic effects. We complement their conventional synchronous treatment by showing how to naturally also accommodate asynchrony within them, namely, by decoupling the execution of operation calls into signalling that an operation's implementation needs to be executed, and interrupting a running computation with the operation's result, to which the computation can react by installing interrupt handlers. We formalise these ideas in a small core calculus and demonstrate its flexibility using examples ranging from a multi-party web application, to pre-emptive multi-threading, to (cancellable) remote function calls, to a parallel variant of runners of algebraic effects. In addition, the paper is accompanied by a formalisation of the calculus's type safety proofs in Agda, and a prototype implementation in OCaml.

Exploration of Solid Phase Peptoid Synthesis for the Design of Trifunctional Hapten-Lipid-TLR7/8 Agonist Antibody-Recruiting Oligomers That Combine Innate Effector with Innate Activation Function.

The strategic engagement of innate immunity is a promising avenue for cancer treatment. Antibody-recruiting molecules (ARMs) direct endogenous antibodies to target tumor sites, eliciting innate immune effector killing responses. In this study, we report the synthesis of ARMs by employing solid-phase peptoid synthesis to construct three libraries of antibody-recruiting oligomers. Using dinitrophenyl (DNP) as a model hapten and alkyl lipid chains for cell surface anchoring, we tailored oligomers with variations in valency and spatial configuration. Among these, an oligomer design featuring DNP connected to the oligomer backbone through an extended PEG linker and flanked by two lipid motifs emerged as the most effective in antibody recruitment in vitro. This oligomer was further functionalized to include an imidazoquinoline, creating a trifunctional hapten-lipid-TLR7/8 agonist oligomer, and a parallel variant was conjugated with rhodamine, resulting in a trifunctional hapten-lipid-dye oligomer. Upon intratumorally administration in a murine model, these oligomers induced localized immune activation within tumors. Subsequent ex vivo analysis of single-cell suspensions from excised tumors confirmed the enhanced binding of anti-DNP antibodies. These findings underscore the potential of custom-designed ARMs in orchestrating precise immune-mediated tumor targeting and highlight the adaptability of solid-phase synthesis in oligomer design for the design of multifunctional antibody recruiting molecules.

Evolving routing policies for electric vehicles by means of genetic programming

AbstractIn recent years, the growing interest in environmental sustainability has led to Electric Vehicle Routing Problems (EVRPs) attracting more and more attention. EVRPs involve the use of electric vehicles, which have additional constraints, such as range and recharging time, compared to conventional Vehicle Routing Problems (VRPs). The complexity and dynamic nature of solving VRPs often lead to the introduction of Routing Policies (RPs), simple heuristics that incrementally build routes. However, manually designing efficient RPs proves to be a challenging and time-consuming task. Therefore, there is a pressing need to explore the application of hyper-heuristics, in particular Genetic Programming (GP), to automatically generate new RPs. Since this method has not yet been investigated in the literature in the context of EVRPs, this study explores the applicability of GP to automatically generate new RPs for EVRP. To this end, three RP variants (serial, semiparallel, and parallel) are introduced in this study, along with a set of domain-specific terminal nodes to optimise three criteria: the number of vehicles, energy consumption, and total tardiness. The experimental analysis shows that the serial variant performs best in terms of energy consumption and number of vehicles, while the parallel variant is most effective in minimising the total tardiness. A comprehensive analysis of the proposed method is conducted to determine its convergence properties and the impact of the proposed terminal nodes on performance and to describe several generated RPs. The results show that the automatically generated RPs perform commendably compared to traditional methods such as metaheuristics and exact methods, which usually require significantly more runtime. More specifically, depending on the scenario in which they are used, the generated RPs achieve results that are about 20%-37% worse compared to the best known results for the number of vehicles in almost negligible time, in just some milliseconds.

A distributed memory parallel randomized Kaczmarz for sparse system of equations

AbstractKaczmarz algorithm is an iterative projection method for solving system of linear equations that arise in science and engineering problems in various application domains. In addition to classical Kaczmarz, there are randomized and parallel variants. The main challenge of the parallel implementation is the dependency of each Kaczmarz iteration on its predecessor. Because of this dependency, frequent communication is required which results in a substantial overhead. In this study, a new distributed parallel method that reduces the communication overhead is proposed. The proposed method partitions the problem so that the Kaczmarz iterations on different blocks are less dependent. A frequency parameter is introduced to see the effect of communication frequency on the performance. The communication overhead is also decreased by allowing communication between processes only if they have shared non‐zero columns. The experiments are performed using problems from various domains to compare the effects of different partitioning methods on the communication overhead and performance. Finally, parallel speedups of the proposed method on larger problems are presented.

Protocell Effects on RNA Folding, Function, and Evolution.

ConspectusCreating a living system from nonliving matter is a great challenge in chemistry and biophysics. The early history of life can provide inspiration from the idea of the prebiotic "RNA World" established by ribozymes, in which all genetic and catalytic activities were executed by RNA. Such a system could be much simpler than the interdependent central dogma characterizing life today. At the same time, cooperative systems require a mechanism such as cellular compartmentalization in order to survive and evolve. Minimal cells might therefore consist of simple vesicles enclosing a prebiotic RNA metabolism.The internal volume of a vesicle is a distinctive environment due to its closed boundary, which alters diffusion and available volume for macromolecules and changes effective molecular concentrations, among other considerations. These physical effects are mechanistically distinct from chemical interactions, such as electrostatic repulsion, that might also occur between the membrane boundary and encapsulated contents. Both indirect and direct interactions between the membrane and RNA can give rise to nonintuitive, "emergent" behaviors in the model protocell system. We have been examining how encapsulation inside membrane vesicles would affect the folding and activity of entrapped RNA.Using biophysical techniques such as FRET, we characterized ribozyme folding and activity inside vesicles. Encapsulation inside model protocells generally promoted RNA folding, consistent with an excluded volume effect, independently of chemical interactions. This energetic stabilization translated into increased ribozyme activity in two different systems that were studied (hairpin ribozyme and self-aminoacylating RNAs). A particularly intriguing finding was that encapsulation could rescue the activity of mutant ribozymes, suggesting that encapsulation could affect not only folding and activity but also evolution. To study this further, we developed a high-throughput sequencing assay to measure the aminoacylation kinetics of many thousands of ribozyme variants in parallel. The results revealed an unexpected tendency for encapsulation to improve the better ribozyme variants more than worse variants. During evolution, this effect would create a tilted playing field, so to speak, that would give additional fitness gains to already-high-activity variants. According to Fisher's Fundamental Theorem of Natural Selection, the increased variance in fitness should manifest as faster evolutionary adaptation. This prediction was borne out experimentally during in vitro evolution, where we observed that the initially diverse ribozyme population converged more quickly to the most active sequences when they were encapsulated inside vesicles.The studies in this Account have expanded our understanding of emergent protocell behavior, by showing how simply entrapping an RNA inside a vesicle, which could occur spontaneously during vesicle formation, might profoundly affect the evolutionary landscape of the RNA. Because of the exponential dynamics of replication and selection, even small changes to activity and function could lead to major evolutionary consequences. By closely studying the details of minimal yet surprisingly complex protocells, we might one day trace a pathway from encapsulated RNA to a living system.

An engineered lactate oxidase based electrochemical sensor for continuous detection of biomarker lactic acid in human sweat and serum

Lactate levels in humans reveal intensity and duration of exertion and provide a critical readout for the severity of life-threatening illnesses such as pediatric sepsis. Using the lactate oxidase enzyme (Lox) from Aerococcus viridians, we demonstrated its functionality for lactate electrochemical sensing in physiological fluids in a lab setting. The structure and dynamics of LOx were validated by crystallography, X-ray scattering, and hydroxyl radical protein footprinting. This provided a validated protein template for understanding and designing an enzyme-based electrochemical sensing elements. Using this template, LOx enzyme variants were generated and compared. Comparison of the variants demonstrates that one exhibits effective lactate sensing at significantly reduced operating voltages. Additionally, we demonstrate that the four hexahistidine-tags on each enzyme tetramer are sufficient for immobilization to create a durable, functional sensor, with no need for a covalent attachment, enabling self-immobilization and eliminating the need for additional immobilization steps. The functionality of the LOx enzyme variants was verified at physiological lactate concentrations in both human serum (0–4 mM) and artificial sweat (0–100 mM) using 3-electrode setups for analysis of the three variants in parallel. Accuracy of measurement in both artificial sweat and human serum were high. Employing a microfluidic flow cell, we successfully monitored varying lactate levels in physiological fluids continuously over a 2h period. Overall, this optimized LOx enzyme, which self-immobilizes onto gold sensing electrodes, facilitates efficient and reliable lactate detection and continuous monitoring at reduced operating voltages suitable for further development towards commercial use.

Large-scale and cooperative graybox parallel optimization on the supercomputer Fugaku

We design, develop and analyze parallel variants of a state-of-the-art graybox optimization algorithm, namely Drils (Deterministic Recombination and Iterated Local Search), for attacking large-scale pseudo-boolean optimization problems on top of the large-scale computing facilities offered by the supercomputer Fugaku. We first adopt a Master/Worker design coupled with a fully distributed Island-based model, ending up with a number of hybrid OpenMP/MPI implementations of high-level parallel Drils versions. We show that such a design, although effective, can be substantially improved by enabling a more focused iteration-level cooperation mechanism between the core graybox components of the original serial Drils algorithm. Extensive experiments are conducted in order to provide a systematic analysis of the impact of the designed parallel algorithms on search behavior, and their ability to compute high-quality solutions using increasing number of CPU-cores. Results using up to 1024×12-cores NUMA nodes, and NK-landscapes with up to 10,000 binary variables are reported, providing evidence on the relative strength of the designed hybrid cooperative graybox parallel search.

Parallelized engineering of mutational models using piggyBac transposon delivery of CRISPR libraries

New technologies and large-cohort studies have enabled novel variant discovery and association at unprecedented scale, yet functional characterization of these variants remains paramount to deciphering disease mechanisms. Approaches that facilitate parallelized genome editing of cells of interest or induced pluripotent stem cells (iPSCs) have become critical tools toward this goal. Here, we developed an approach that incorporates libraries of CRISPR-Cas9 guide RNAs (gRNAs) together with inducible Cas9 into a piggyBac (PB) transposon system to engineer dozens to hundreds of genomic variants in parallel against isogenic cellular backgrounds. This method empowers loss-of-function (LoF) studies through the introduction of insertions or deletions (indels) and copy-number variants (CNVs), though generating specific nucleotide changes is possible with prime editing. The ability to rapidly establish high-quality mutational models at scale will facilitate the development of isogenic cellular collections and catalyze comparative functional genomic studies investigating the roles of hundreds of genes and mutations in development and disease.

Hierarchical Cut Labelling - Scaling Up Distance Queries on Road Networks

Answering the shortest-path distance between two arbitrary locations is a fundamental problem in road networks. Labelling-based solutions are the current state-of-the-arts to render fast response time, which can generally be categorised into hub-based labellings, highway-based labellings, and tree decomposition labellings. Hub-based and highway-based labellings exploit hierarchical structures of road networks with the aim to reduce labelling size for improving query efficiency. However, these solutions still result in large search spaces on distance labels at query time, particularly when road networks are large. Tree decomposition labellings leverage a hierarchy of vertices to reduce search spaces over distance labels at query time, but such a hierarchy is generated using tree decomposition techniques, which may yield very large labelling sizes and slow querying. In this paper, we propose a novel solution hierarchical cut 2-hop labelling (HC2L) to address the drawbacks of the existing works. Our solution combines the benefits of hierarchical structures from both perspectives - reduce the size of a distance labelling at preprocessing time and further reduce the search space on a distance labelling at query time. At its core, we propose a new hierarchy, balanced tree hierarchy, which enables a fast, efficient data structure to reduce the size of distance labelling and to select a very small subset of labels to compute the shortest-path distance at query time. To speed up the construction process of HC2L, we further propose a parallel variant of our method, namely HC2L^p. We have evaluated our solution on 10 large real-world road networks through extensive experiments. The results show that our method is 1.5-4 times faster in terms of query processing while being comparable in terms of labelling construction time and achieving up to 60% smaller labelling size compared to the state-of-the-art approaches.

A massively parallel implicit 3D unstructured grid solver for computing turbulent flows on latest distributed memory computational architectures

An implicit unstructured grid density-based solver–PRAVAHA based on a parallel variant of the lower upper symmetric Gauss-Seidel (LUSGS) method is developed to compute large-scale engineering problems. A four-layered parallel algorithm is designed to efficiently compute three-dimensional turbulent flows on massively parallel modern multiple instruction multiple data-stream (MIMD) computational hardware. This data parallel approach achieves multiple layers of parallelism including continuity of flow solution, transfer of solution gradients, and calculation of drag/lift/solution residuals, right up to the innermost implicit LUSGS solver sub-routine, which is relatively less explored in the literature. Domain decomposition is performed using the METIS software based on multi-level graph partitioning algorithms. Non-blocking message-passing interface library functions are used to manage inter-processor communication through explicit message passing, efficiently. Super-linear scalability of the parallel solver is established on the current state-of-the-art supercomputing facility, the 838 teraflops PARAM seva on up to 6144 cores. Linear or even super-linear speedup on problems of significant size is observed even on ad-hoc parallel computing platforms like workstations and multi-node clusters, for turbulent flow simulations.

Parallel swarm-based algorithms for scheduling independent tasks

Task scheduling is crucial for achieving high performance in parallel computing. Since task scheduling is NP-hard, the efficient assignment of tasks to compute resources remains an issue. Across the literature, several algorithms have been proposed to solve different scheduling problems. One group of promising approaches in this field is formed by swarm-based algorithms which have a potential to benefit from a parallel execution. Common swarm-based algorithms are Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In this article, we propose two new scheduling methods based on parallel ACO, PSO and, Hill Climbing, respectively. These algorithms are used to solve the problem of scheduling independent tasks onto heterogeneous multicore platforms. The results of performance measuements demonstrate the improvements on the makespan and the scheduling time achieved by the parallel variants.

Operating and Environmental Cost Considerations Comparison for Hybrid-Electric Propulsion Architecture Variants

A promising solution for sustainable aviation is hybrid-electric propulsion, which is gaining popularity in recent years. However, hybrid-electric propulsion variants are affected by the technological maturity of electric components, which have a direct impact on the electrification benefit, compared to conventional propulsive configurations. This work aims in investigating three different Entry Into Service dates, i.e., 2027, 2030, and 2040, for two hybrid-electric propulsive configurations, namely the Series and the Parallel. Six hybrid variants are sized, one for each date and configuration, using an in-house tool, and their mission performance metrics, operating costs, and environmental impact are compared to a conventional aircraft with technological assumptions of 2014. Results indicate that the Parallel variants promise a block fuel and energy reduction of up to 33 % and 28 % respectively for 2040, whereas the Series variants show a respective reduction of 66 % and 58 % accordingly. Moreover, the 2027 Parallel aircraft has reduced annual operating costs compared to the Series 2027, whereas a turnaround point exists in which the Series becomes more economical to operate, compared to both Parallel and the Reference aircraft after 2030. Finally, the Life Cycle Assessment suggests that the Series variants have a lower environmental impact compared to the respective Parallel variants and that both configurations have better scores than the Reference aircraft. The Parallel and Series configurations of 2030 promise an environmental impact reduction of up to 22.8 % and 59.8 % respectively, compared to the reference aircraft.

Distributed structural clustering on large graph

SummaryGraph clustering is a primitive operation for graph data mining. It plays an important role to reveal community clusters, hubs, and outliers in complex networks. There are several graph clustering algorithms have been proposed based on the well‐studied SCAN algorithm in recent years. However, SCAN and its improved sequential variants are prohibitively slow due to their iterative computations. The parallel variants are focusing on improving the efficiency of graph clustering by utilizing multi‐cores computer architectures on single computing node with complex optimization techniques. Therefore, SCAN and its variants are not suitable for processing very large graphs due to the limitations of memory size and storage volume on a single node. In this article, we proposed a distributed parallel structural clustering algorithm using MapReduce. In order to improve the efficiency further, we proposed optimization techniques including partition based clustering and simplified combination with labels to accelerate the operations. We conducted extensive experiments on real world datasets. The experimental results showed our algorithm is high efficiency and scales well under different settings.

Resolving Deleterious and Near-Neutral Effects Requires Different Pooled Fitness Assay Designs.

Pooled sequencing-based fitness assays are a powerful and widely used approach to quantifying fitness of thousands of genetic variants in parallel. Despite the throughput of such assays, they are prone to biases in fitness estimates, and errors in measurements are typically larger for deleterious fitness effects, relative to neutral effects. In practice, designing pooled fitness assays involves tradeoffs between the number of timepoints, the sequencing depth, and other parameters to gain as much information as possible within a feasible experiment. Here, we combined simulations and reanalysis of an existing experimental dataset to explore how assay parameters impact measurements of near-neutral and deleterious fitness effects using a standard fitness estimator. We found that sequencing multiple timepoints at relatively modest depth improved estimates of near-neutral fitness effects, but systematically biased measurements of deleterious effects. We showed that a fixed total number of reads, deeper sequencing at fewer timepoints improved resolution of deleterious fitness effects. Our results highlight a tradeoff between measurement of deleterious and near-neutral effect sizes for a fixed amount of data and suggest that fitness assay design should be tuned for fitness effects that are relevant to the specific biological question.

Environmental and techno-economic evaluation for hybrid-electric propulsion architectures

AbstractHybrid-electric propulsion is a promising alternative to sustainable aviation and is mainly considered for the commuter and regional aircraft class. However, the development of hybrid-electric propulsion variants is affected by the technology readiness level of electric components. The components’ technology will determine the electrification benefit, compared to a conventional aircraft, and will suggest which is the most beneficial variant and which has a closer entry into service date. Within this work, three different dates are explored, namely 2027, 2030 and 2040, to size three Parallel and three Series hybrid-electric architecture variants using an in-house aircraft sizing tool. All variants are compared to a conventional configuration sized using technological assumptions of 2014, with the main comparison metrics being the aircraft block fuel, energy consumption, direct operating cost and holistic environmental impact. On one hand, the Parallel configurations have reduced maximum take-off mass and mission energy consumption compared to the Series, however, the latter show a greater potential for block fuel reduction and require less onboard energy for the same mission. The annual operating cost evaluation indicates that the Parallel hybrid variant of 2030 has greater operational costs than the respective Series variant; however, it has reduced capital costs compared to the latter, making it more economical to operate considering both costs. Additionally, in the case of an energy recession, both hybrid variants of 2030 show a further cost reduction, with the Series having a total reduction of 10.4% excluding capital costs, compared to the reference aircraft. Moreover, the life cycle assessment shows that the Series variants have a lower environmental impact, both compared to the reference aircraft and the Parallel variants. The former could be up to 59.7% less detrimental to the environment than the reference aircraft, whereas the latter up to 23.9%, with the integration of renewable sources for electricity production. Finally, by the year 2040, the Series variant shows outstanding performance in all comparison metrics, compared to the Parallel and the reference aircraft.

Multiplexed on-yeast serological assay for immune escape screening of SARS-CoV-2 variants

The emergence of the SARS-CoV-2 Omicron variant altered patient risk profiles and shifted the trajectory of the COVID-19 pandemic. Therefore, sensitive serological tests capable of analyzing patient IgG responses to multiple variants in parallel are highly desirable. Here, we present an adaptable serological test based on yeast surface display and serum biopanning that characterizes immune profiles against SARS-CoV-2 Wuhan (B lineage), Delta (B.1.617.2 lineage), and Omicron (B.1.1.529 lineage) receptor-binding domain (RBD) variants. We examined IgG titers from 30 serum samples from COVID-19-convalescent and vaccinated cohorts in Switzerland, and assessed the relative affinity of polyclonal serum IgG for RBD domains. We demonstrate that serum IgGs from patients recovered from severe COVID-19 between March-June 2021 bound tightly to both original Wuhan and Delta RBD variants, but failed to recognize Omicron RBDs, representing an affinity loss of >10- to 20-fold. Our yeast immunoassay is easily tailored, expandable and parallelized with newly emerging RBD variants.

A criterion space decomposition approach to generalized tri-objective tactical resource allocation

We present a tri-objective mixed-integer linear programming model of the tactical resource allocation problem with inventories, called the generalized tactical resource allocation problem (GTRAP). We propose a specialized criterion space decomposition strategy, in which the projected two-dimensional criterion space is partitioned and the corresponding sub-problems are solved in parallel by application of the quadrant shrinking method (QSM) (Boland in Eur J Oper Res 260(3):873–885, 2017) for identifying non-dominated points. To obtain an efficient implementation of the parallel variant of the QSM we suggest some modifications to reduce redundancies. Our approach is tailored for the GTRAP and is shown to have superior computational performance as compared to using the QSM without parallelization when applied to industrial instances.

Higher-Order QR with Tournament Pivoting for Tensor Compression

We present in this paper a parallel algorithm that generates a low-rank approximation of a distributed tensor using QR decomposition with tournament pivoting (QRTP). The algorithm, which is a parallel variant of the higher-order singular value decomposition, generates factor matrices for a Tucker decomposition by applying QRTP to the unfolding matrices of a tensor distributed blockwise (by subtensor) on a set of processors. For each unfolding mode the algorithm logically reorganizes (unfolds) the processors so that the associated unfolding matrix has a suitable logical distribution. We also establish error bounds between a tensor and the compressed version of the tensor generated by the algorithm.

COMPUTATIONAL EXPERIMENTS ON SOLVING GRID ELLIPTIC EQUATIONS BY SOME PARALLEL ITERATIVE METHODS ON THE K60 CLUSTER

The paper deals with the problem of preserving natural water systems, as well as maintaining their integrity, not only through the enterprise of organizational, engineering and technical solutions, but also through the development of highly effective mathematical modeling techniques that make it possible quickly and efficiently, based on interconnected high-precision models of hydrophysics and hydrobiology, predict the processes of pollution spreading and the occurrence of hazardous phenomena in coastal systems. The article considers algorithms for solving grid equations developed for high-performance cluster systems. A model of parallel computations is proposed, which makes it possible, when choosing the appropriate method for solving the problem of aquatic ecology, to estimate the cost of calculations, which is defined as the product of the time of parallel solution of the problem and the number of processors used. An estimate of the optimal amount of information packet for exchange between processors is obtained. The adaptive modified alternating-triangular method of minimum corrections is described, the results of numerical experiments for the parallel variant of this one are presented.