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Sparse Triangular Solve (SpTRSV) has long been an essential kernel in the field of scientific computing. Due to its low computational intensity and internal data dependencies, SpTRSV is hard to implement and optimize on graphics processing units (GPUs). ...

The coresets approach, also called subsampling or subset selection, aims to select a subsample as a surrogate for the observed sample and has found extensive application in large-scale data analysis. Existing coresets methods construct the ...${}^{\mathrm{}}$$$$$

• A novel scheme, termed mixed cell compressed sparse row (mCCSR) is proposed to address the sparsity, block structure, and partial sub-matrix symmetry in the matrix.
• Time domain boundary element method (TDBEM) in elastodynamics.
• The ...

In this paper, a novel mixed cell compressed sparse row (mCCSR) scheme is proposed to address the sparsity, block structure, and partial sub-matrix symmetry inherent in the coefficient matrix of the time domain boundary element method (TDBEM) in ...

Embedding is a crucial step for deep neural networks. Datasets, from different applications, with different structures, can all be processed through an embedding layer and transformed into a dense matrix. The transformation must minimize both the loss ...

Sparse general matrix–matrix multiplication (SpGEMM) is a crucial and complex computational task in many practical applications. Improving the performance of SpGEMM on SIMT processors like modern GPUs is challenging due to the unpredictable ...

In our paper, we consider the following general problems: check feasibility, count the number of feasible solutions, find an optimal solution, and count the number of optimal solutions in $\mathcal{P}\cap {\mathbb{Z}}^n$, assuming that $\mathcal{P}$ is a polyhedron, defined by systems $A\mathrm{...}$$\mathrm{}$${}^{}$$$${}^{}$${}^{}$$$${}^{}$

Density peaks clustering (DPC) has had an impact in many fields, as it can quickly select centers and effectively process complex data. However, it also has low operational efficiency and a “Domino” effect. To solve these defects, we propose a ...

Finite element methods require the composition of the global stiffness matrix from local finite element contributions. The composition process combines the computation of element stiffness matrices and their assembly into the global stiffness ...

• A new sparse matrix format, Compressed-Rows-Aligned-Columns (CRAC), ideal for hp-FEM.
• Parallelization of the assembly procedure using atomic synchronization.
• Local-to-global degrees of freedom maps for vectorization.
• ...

Sparse matrix multiplication is ubiquitous in many applications such as graph processing and numerical simulation. In recent years, numerous efficient sparse matrix multiplication algorithms and computational libraries have been proposed. However,...

Supercomputer architectures are trending toward higher computational throughput due to the inclusion of heterogeneous compute nodes. These multi-GPU nodes increase on-node computational efficiency, while also increasing the amount of ...

Even though it is well known that binary values are common in graph applications (e.g., adjacency matrix), how to leverage the phenomenon for efficiency has not yet been adequately explored. This paper presents a systematic study on ...

• The performance of GraphBLAS algorithms can be accelerated with sparse bit storage format and bit manipulation.

Wireless Sensor Networks (WSNs) gather active sensor data within a specified period to the sink node. The data transmission in restricted resource utilization in wireless surroundings is a primary issue. Compressive sensing enables resource ...

A novel lossy RGB (Red, Green, Blue) colour still image compression algorithm is proposed. The intended method introduces Legendre wavelet-based image transformation technique integrated with vector quantization and run length encoding. High ...

Sparse matrix-sparse vector multiplication (SpMSpV) is an important primitive for graph algorithms and machine learning applications. The sparsity of the input and output vectors makes its floating point efficiency in general lower than sparse matrix-...

An interesting phenomenon in linear programming (LP) is how to deal with solutions in which the number of nonzero variables is less than the number of rows of the matrix in standard form. An interesting approach is that of basis-deficiency-...

Positive or Non-negative Matrix Factorization (NMF) is an effective technique and has been widely used for Big Data representation. It aims to find two non-negative matrices W and H whose product provides an optimal approximation to the original ...$$$\mathrm{}$

Segmented operations, such as segmented sum, segmented scan and segmented sort, are important building blocks for parallel irregular algorithms. We in this work propose a new parallel primitive called segmented merge. Its function is in parallel ...

Sparse data applications have irregular access patterns that stymie modern memory architectures. Although hyper-sparse workloads have received considerable attention in the past, moderately-sparse workloads prevalent in machine learning applications, ...

Personalized recommendation is a key technology to effectively solve the overload of online information and eliminate information islands. It is widely known as an important way to improve the quality of information services. However, cold start, data ...

Protein is a complex organic substance with a spatial structure, which exists widely in the body of living things. Almost all living things rely on protein to form an important part of the body, perform many physiological function adjustments, and ...