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Background HH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition. It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous proteins. Results We developed a single-instruction multiple-data (SIMD) vectorized implementation of the Viterbi algorithm for profile HMM alignment and introduced various other speed-ups. These accelerated the search methods HHsearch by a factor 4 and HHblits by a factor 2 over the previous version 2.0.16. HHblits3 is ∼10× faster than PSI-BLAST and ∼20× faster than HMMER3. Jobs to perform HHsearch and HHblits searches with many query profile HMMs can be parallelized over cores and over cluster servers using OpenMP and message passing interface (MPI). The free, open-source, GPLv3-licensed software is available at https://github.com/soedinglab/hh-suite . Conclusion The added functionalities and increased speed of HHsearch and HHblits should facilitate their use in large-scale protein structure and function prediction, e.g. in metagenomics and genomics projects.

Background The maximal sensitivity for local pairwise alignment makes the Smith-Waterman algorithm a popular choice for protein sequence database search. However, its quadratic time complexity makes it compute-intensive. Unfortunately, current state-of-the-art software tools are not able to leverage the massively parallel processing capabilities of modern GPUs with close-to-peak performance. This motivates the need for more efficient implementations. Results CUDASW++4.0 is a fast software tool for scanning protein sequence databases with the Smith-Waterman algorithm on CUDA-enabled GPUs. Our approach achieves high efficiency for dynamic programming-based alignment computation by minimizing memory accesses and instructions. We provide both efficient matrix tiling, and sequence database partitioning schemes, and exploit next generation floating point arithmetic and novel DPX instructions. This leads to close-to-peak performance on modern GPU generations (Ampere, Ada, Hopper) with throughput rates of up to 1.94 TCUPS, 5.01 TCUPS, 5.71 TCUPS on an A100, L40S, and H100, respectively. Evaluation on the Swiss-Prot, UniRef50, and TrEMBL databases shows that CUDASW++4.0 gains over an order-of-magnitude performance improvements over previous GPU-based approaches (CUDASW++3.0, ADEPT, SW#DB). In addition, our algorithm demonstrates significant speedups over top-performing CPU-based tools (BLASTP, SWIPE, SWIMM2.0), can exploit multi-GPU nodes with linear scaling, and features an impressive energy efficiency of up to 15.7 GCUPS/Watt. Conclusion CUDASW++4.0 changes the standing of GPUs in protein sequence database search with Smith-Waterman alignment by providing close-to-peak performance on modern GPUs. It is freely available at https://github.com/asbschmidt/CUDASW4 .

In this paper, we investigate unreliability in hub location planning. A mixed integer nonlinear programming model is formulated for optimally locating p uncapacitated hubs, each of which can fail with a site-specific probability. The objective is to determine the location of hubs and the assignment of demand nodes to hubs to minimize expected demand weighted travel cost plus a penalty if all hubs fail. A linear version of the model is developed using a specialized flow network called a probability lattice to evaluate compound probability terms. A tabu search algorithm is proposed to find optimal to near optimal solutions for large problem instances. A parallel computing strategy is integrated into the tabu search process to improve performance. Experimental results carried out on several benchmark instances show the efficiency of our linearized model and heuristic algorithm. Compared with a standard hub median model that disregards the potential for hub failures, our model produces solutions that serve larger numbers of customers and at lower cost per customer.

This paper investigates a competitive two-agent parallel-batching scheduling problem with aging effect on parallel machines. The objective is to minimize the makespan of agent A with the constraint that the makespan of agent B is no more than a given threshold. Some key structural properties are first identified in two different cases, and based on these structural properties a novel decision tree of scheduling rules is constructed and a heuristic algorithm is designed. Then, an effective hybrid BF-VNS algorithm combining Bacterial Foraging (BF) with variable neighborhood search (VNS) is developed to tackle the studied problem. Computational experiments are conducted to evaluate the performance of the proposed hybrid algorithm and some other well-known algorithms. The experimental results indicate that the hybrid BF-VNS algorithm performs quite better than the compared algorithms.

In this study, we investigate an unrelated parallel batch processing machines scheduling problem (UPBPMSP). A set of jobs with non-identical sizes and arbitrary ready times are scheduled on unrelated parallel batch processing machines with different capacities to minimize the makespan (i.e., the completion time of the last batch). Existing studies have either decomposed the scheduling problem into two sub-problems and employed population-based heuristic interaction for searching the best solutions or used neighborhood search algorithms to search the current solution’s neighborhood to find a better solution. However, the performances of these methods are not quite satisfactory due to the complicated interactions or oversimplified neighborhood search strategies, especially for large-scale UPBPMSPs. In this study, we propose a novel tabu-based adaptive large neighborhood search (TALNS) algorithm to obtain high-quality solutions for the UPBPMSP. To avoid complex interactions, we propose a new solution structure, and the proposed TALNS is applied to obtain the optimal solution structure of the UPBPMSP. Extensive experiments are conducted to evaluate the performance of the proposed algorithm on a total of 360 instances from the literature and 30 new instances. Numerical results demonstrate that the proposed TALNS outperforms the neighborhood search methods, which outperforms the population-based methods. With the proposed TALNS, 55 out of 360 instances’ best-known solutions are updated from this study.

In this paper, we present a parallel chaotic local search enhanced harmony search algorithm (MHS–PCLS) for solving engineering design optimization problems. The concept of chaos has been previously successfully applied in metaheuristics. However, chaos sequences are sensitive to their initial conditions and cause unstable performance in algorithms. The proposed parallel chaotic local search method searches from several different initial points and diminishes the sensitivity of the initial condition, thereby increasing the robustness of the harmony search method. Numerical benchmark problems are tested to validate the effectiveness of MHS–PCLS. The simulation results confirm that MHS–PCLS obtains superior results for mathematical examples compared to other harmony search variants. Several well-known constrained engineering design problems are also tested using the new approach. The computational results demonstrate that the proposed MHS–PCLS algorithm requires a smaller number of function evaluations and in the majority of cases delivers improved and more robust results compare to other algorithms.

Background Remote homology detection is critical for inferring evolutionary and functional relationships among proteins, but divergence below 30% identity often hinders standard sequence-based searches. Profile-based methods and HMMs improve sensitivity, yet many distant homologues remain undetected. Cascade PSI-BLAST uses efficient, iterative, multi-generation searches of intermediate hits to bridge sequence gaps, constructing successive PSSMs that reveal remote relationships without structural information. However, its application to ultra-large databases such as NR and UniProt is computationally intensive. We address this limitation by optimizing intermediate selection and enabling distributed execution across multiple CPUs or servers, significantly enhancing throughput and resource utilization while preserving detection sensitivity. Results We applied the parallelized Cascade PSI-BLAST algorithm to sequences drawn from multiple SCOP classes to evaluate its sensitivity and predictive power on the GenDis database. Our method uncovers substantially more remote homologues, with less false positives, in both GenDis and UniProt compared to conventional searches. Large repositories such as NR can also be processed in a practical timeframe by distributing cascade searches across multiple servers. Finally, we have deployed a user-friendly web server for running cascade searches on smaller databases, including PDB and Swiss-Prot. Conclusion Our enhanced Cascade PSI-BLAST markedly improves detection of remote homologues across both large and curated databases by leveraging intermediate sequences and distributed execution. Multi-server parallelization reduces runtimes on expansive repositories such as NR to practical levels. The web server offers rapid, user-friendly searches on smaller datasets, while the standalone package supports scalable, customizable analyses on local infrastructure. Together, these tools provide a versatile, sequence-only platform for uncovering distant protein relationships, accelerating functional annotation and evolutionary insights.

The multiplication of computing cores in modern processor units permits revisiting the design of classical algorithms to improve computational performance in complex application domains. Artificial Intelligence planning is one of those applications where large search spaces require intelligent and more exhaustive search control. In this paper, parallel planning algorithms, derived from best-first search, are proposed for shared memory architectures. The parallel algorithms, based on the asynchronous work pool paradigm, maintain good thread occupancy in multi-core CPUs. All algorithms use one ordered global list of states stored in shared memory from where they select nodes for expansion. A parallel best-first search algorithm that develops new states with depth equal to one is proposed first. Then, we propose an extension of this parallel algorithm that features a diversification strategy in order to escape local minima. We study and analyse a set of computational experiments for problems that come from the International Planning Competition and real-world industry applications. The empirical evaluation shows that the parallel algorithms solve most of the domains efficiently without incurring higher solutions costs. In those problems with partial results, we highlight the potential shortcomings of the proposed approaches for promising future directions.

Hardware/software partitioning is an essential step in hardware/software co-design. For large size problems, it is difficult to consider both solution quality and time. This paper presents an efficient GPU-based parallel tabu search algorithm (GPTS) for HW/SW partitioning. A single GPU kernel of compacting neighborhood is proposed to reduce the amount of GPU global memory accesses theoretically. A kernel fusion strategy is further proposed to reduce the amount of GPU global memory accesses of GPTS. To further minimize the transfer overhead of GPTS between CPU and GPU, an optimized transfer strategy for GPU-based tabu evaluation is proposed, which considers that all the candidates do not satisfy the given constraint. Experiments show that GPTS outperforms state-of-the-art work of tabu search and is competitive with other methods for HW/SW partitioning. The proposed parallelization is significant when considering the ordinary GPU platform.