You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In this paper, we propose a new parallel ordering method to vectorize and parallelize the sparse triangular solver, which is called hierarchical block multi-color ordering. In this method, the parallel forward and backward substitutions can be vectorized while preserving the advantages of block multi-color ordering, that is, fast convergence and fewer thread synchronizations. To evaluate the proposed method in a parallel ICCG (Incomplete Cholesky Conjugate Gradient) solver, numerical tests were conducted using five test matrices on three types of computational nodes. The numerical results indicate that the proposed method outperforms the conventional block and nodal multi-color ordering methods in 13 out of 15 test cases, which confirms the effectiveness of the method.

Cited Abstract(s):
[1]: 

[2]: Accelerating large-scale linear solvers is always crucial for scientific research and industrial applications. In this regard, preconditioners play a key role in improving the performance of iterative linear solvers. This paper presents a summary and review of our work about the development of parallel ILU preconditioners on GPUs. The mechanisms of ILU(0), ILU(k), ILUT, enhanced ILUT, and block-wise ILU(k) are reviewed and analyzed, which give a clear guidance in the development of iterative linear solvers. ILU(0) is the most commonly used preconditioner, and the nonzero pattern of its matrix is exactly the same as the original matrix to be solved. ILU(k) uses k levels to control the pattern of its preconditioner matrix. ILUT selects entries for its preconditioner matrix by setting thresholds without considering its original matrix pattern. In addition to point-wise ILU preconditioners, a block-wise ILU(k) preconditioner is designed delicately in support of block-wise matrices. In implementation, the RAS (Restricted Additive Schwarz) method is adopted to optimize the parallel structure of a preconditioner matrix. Coupling with the configuration parameters of ILU preconditioners, a complex situation appears in the parallel solution process, so decoupled algorithms are adopted. These algorithms are implemented and tested on NVIDIA GPUs. The experiment results show that a single-GPU implementation can speed up an ILU preconditioner by a factor of 10, compared to traditional CPU implementation. The results also show that the ILU(0) has better speedup than ILU(k) but slower convergence than ILU(k). Level k of ILU(k) and threshold (p, t) of ILUT are effective adjustment factors for controlling the equilibrium point between acceleration and convergence for ILU(k) and ILUT, respectively. All these ILU preconditioners are characterized and compared in this work, which shows a clear picture and numerical insights for practitioners in the ILU family.

[3]: Incomplete factorization is one of the most effective general-purpose preconditioning methods for Krylov subspace solvers for large sparse systems of linear equations. Two techniques for enhancing the robustness and performance of incomplete LU factorization for sparse unsymmetric systems are described. A block incomplete factorization algorithm based on the Crout variation of LU factorization is presented. The algorithm is suitable for incorporating threshold-based dropping as well as unrestricted partial pivoting, and it overcomes several limitations of existing incomplete LU factorization algorithms with and without blocking. It is shown that blocking has a three-pronged impact: it speeds up the computation of incomplete factors and the solution of the associated triangular systems, it permits denser and more robust factors to be computed economically, and it permits a trade-off with the restart parameter of GMRES to further improve the overall speed and robustness. A highly effective heuristic for imp...

[4]: This paper is concerned with applying bandwidth and profile reduction reordering algorithms prior to computing an incomplete Cholesky factorization and using this as a preconditioner for the conjugate gradient method. Hundreds of reordering algorithms have been proposed to solve the problems of bandwidth and profile reductions since the mid-1960s. In previous publications, a large range of heuristics for bandwidth and or profile reductions was reviewed. Based on this experience, 13 heuristics were selected as the most promising methods. These are evaluated in this paper along with a variant of the breadth-first search procedure that is proposed. Numerical results confirm the effectiveness of this modified reordering algorithm for linear systems derived from specific application areas. Moreover, the most promising heuristics for several application areas are identified when reducing the computational cost of the incomplete Cholesky-conjugate gradient method.
Finally, we address the recently reported research results that are related to parallel linear solvers that involve sparse triangular solvers. Gonzaga de reported their intensive numerical test results to evaluate various reordering techniques in the ICCG method in [4] . Gupta introduced a blocking framework to generate a fast and robust preconditioner based on ILU factorization in [3] . developed a couple of ILU-based preconditioners on GPUs in [2] . reported the evaluation results of HPCG implementations using nodal and block multi-color orderings on the ARM-based system, which confirmed the superiority of the block coloring method in [1] .