Abstract With the increasing number of computationally intensive applications, heterogeneous systems have become an important solution for improving computing performance. In order to effectively develop and optimize parallel programs running on these systems, performance prediction has become an indispensable part. This article aims to comprehensively review the methods and tools for predicting parallel program performance in heterogeneous systems, analyze the characteristics of existing technologies, explore their development trends, and provide valuable references and guidance for researchers and developers. This article adopts a systematic review method, first sorting out the research process of parallel program performance prediction in heterogeneous systems, and then classifying and summarizing the current mainstream performance prediction methods, including analysis model-based prediction, simulation-based prediction, and machine learning based prediction. This article also summarizes the tools and platforms used to predict parallel program performance in heterogeneous systems. Through review, it was found that various performance prediction methods and tools have their own advantages in feature richness, availability, and accuracy, but they have all improved the efficiency and accuracy of parallel program performance prediction to a certain extent. The review of this article indicates that despite various methods and tools available for performance prediction, there are still many challenges and unresolved issues. Future research should further explore more accurate, efficient and intelligent prediction methods to better support the development and optimization of parallel programs in heterogeneous systems.

It is agreed that portfolio selection is of great importance for the financial market. Numerous outstanding exact and heuristic algorithms have been proposed in the past decades. However, their development always demands meticulous human ideas and could be time-consuming. Moreover, most of them tend to suffer from performance degradation when exposed to new portfolio selection models and different investment environments. Learning-enabled approaches have recently yielded impressive results, but these methods still grapple with challenges in model design and training. In this paper, we explore the mutual facilitation of large language models (LLMs) and huristic approaches in portfolio selection, and propose a novel LLM-based multi-objective evolutionary algorithm (MOEA) named IlmPC-NSGA-II. In this algorithm, the LLM with carefully-designed well-structured prompts serves as a straightforward yet effective engine for generating new solutions, non-dominated sorting and crowding distance calculation are adopted to enable the LLM and the evolutionary process to mutually guide toward the optimal region of the solution space. Experimental results on various scales of constrained multi-objective portfolio selection models and four benchmark problems demonstrate that our proposed approach can achieve a more competitive performance compared to widely-used MOEAs and the LLM-only method.

Ocean general circulation models (OGCMs) are indispensable for studying the multi-scale oceanic processes and climate change. High-resolution ocean simulations require immense computational power and thus become a challenge in climate science. We present LICOMK++, a performance-portable OGCM using Kokkos, to facilitate global kilometer-scale ocean simulations. The breakthroughs include: (1) we enhance cuttingedge Kokkos with the Sunway architecture, enabling LICOMK++ to become the first performance-portable OGCM on diversified architectures, i.e., Sunway processors, CUDA/HIP-based GPUs, and ARM CPUs. (2) LICOMK++ overcomes the one simulated-years-per-day (SYPD) performance challenge for global realistic OGCM at $1-\mathrm{km}$ resolution. It records $\mathbf{1. 0 5}$ and 1.70 SYPD with a parallel efficiency of 54.8% and 55.6% scaling on almost the entire new Sunway supercomputer and two-thirds of the ORISE supercomputer. (3) LICOMK++ is the first global 1-km-resolution realistic OGCM to generate scientific results. It successfully reproduces mesoscale and submesoscale structures that have considerable climate effects.

In this paper, we propose a parallel non-convex approximation framework (NCAQ) for optimization problems where the objective is to minimize a convex function plus the sum of non-convex functions. Based on the structure of the objective function, our framework transforms the non-convex constraints to the logarithmic barrier function and approximates the non-convex problem by a parallel quadratic approximation scheme, which will allow the original problem to be solved by accelerated inexact gradient descent in the parallel environment. Moreover, we give a detailed convergence analysis for the proposed framework. The numerical experiments show that our framework outperforms the state-of-art approaches in terms of accuracy and computation time on the high dimension non-convex Rosenbrock test functions and the risk parity problems. In particular, we implement the proposed framework on CUDA, showing a more than 25 times speed-up ratio and removing the computational bottleneck for non-convex risk-parity portfolio design. Finally, we construct the high dimension risk parity portfolio in China’s stock market that consistently outperforms the equal weight portfolio.

When the images are formulated as discrete measures, computing Wasserstein-p distance between them is challenging due to the complexity of solving the corresponding Kantorovich's problem. In this paper, we propose a novel algorithm to compute the Wasserstein-p distance between discrete measures by restricting the optimal transport (OT) problem on a subset. First, we define the restricted OT problem and prove the solution of the restricted problem converges to Kantorovich's OT solution. Second, we propose the SparseSinkhorn algorithm for the restricted problem and provide a multi-scale algorithm to estimate the subset. Finally, we implement the proposed algorithm on CUDA and illustrate the linear computational cost in terms of time and memory requirements. We compute Wasserstein-p distance, estimate the transport mapping, and transfer color between color images with size ranges from$64\times 64$to$1920\times 1200$. (Our code is available at https://github.com/ucascnic/CudaOT)

It is agreed that portfolio selection models are of great importance for the financial market. In this article, a constrained multiperiod multiobjective portfolio model is established. This model introduces several constraints to reflect the trading restrictions and quantifies future security returns by fuzzy random variables to capture fuzzy and random uncertainties in the financial market. Meanwhile, it considers terminal wealth, conditional value at risk (CVaR), and skewness as tricriteria for decision making. Obviously, the proposed model is computationally challenging. This situation gets worse when investors are interested in a larger financial market since the data they need to analyze may constitute typical big data. Whereafter, a novel intelligent hybrid algorithm is devised to solve the presented model. In this algorithm, the uncertain objectives of the model are approximated by a simulated annealing resilient back propagation (SARPROP) neural network which is trained on the data provided by fuzzy random simulation. An improved imperialist competitive algorithm, named IFMOICA, is designed to search the solution space. The intelligent hybrid algorithm is compared with the one obtained by combining NSGA-II, SARPROP neural network, and fuzzy random simulation. The results demonstrate that the proposed algorithm significantly outperforms the compared one not only in the running time but also in the quality of obtained Pareto frontier. To improve the computational efficiency and handle the large scale securities data, the algorithm is parallelized using MPI. The conducted experiments illustrate that the parallel algorithm is scalable and can solve the model with the size of securities more than 400 in an acceptable time.