Sparse General Matrix-Matrix Multiplication (SpGEMM) is a core operation in high-performance computing applications such as algebraic multigrid solvers, machine learning, and graph processing. Traditional hash-based SpGEMM methods face several challenges, including frequent hash table accesses during the symbolic phase, high-overhead sorting operations in the numeric phase, and imbalanced workload distribution across threads. To address these issues, we propose a highly optimized SpGEMM method-MH-SpGEMM. Unlike conventional approaches, MH-SpGEMM introduces a mask-based storage format for matrix$B$during the symbolic phase and uses bitwise OR operations to efficiently calculate the number of nonzeros in the result matrix. In the numeric phase, a bitonic sort algorithm is employed for rows with many nonzeros to improve sorting efficiency. Furthermore, MH-SpGEMM incorporates a finer-grained load balancing strategy. We conduct a comprehensive evaluation of MHSpGEMM against four state-of-the-art SpGEMM methods (HSMU-SpGEMM, OpSparse, nsparse, and cuSPARSE) on two NVIDIA GPUs(Ada Lovelace, Ampere). The results demonstrate that MH-SpGEMM consistently outperforms existing methods in terms of performance.

This paper presents a technique for determining an object's shape based on the similarity of radiance changes observed at points on its surface under varying illumination. To examine the similarity, we use an observation vector that represents a sequence of pixel intensities of a point on the surface under different lighting conditions. Assuming convex objects under distant illumination and orthographic projection, we show that the similarity between two observation vectors is closely related to the similarity between the surface normals of the corresponding points. This enables us to estimate the object's surface normals solely from the similarity of radiance changes under unknown distant lighting by using dimensionality reduction. Unlike most previous shape reconstruction methods, our technique neither assumes particular reflection models nor requires reference materials. This makes our method applicable to a wide variety of objects made of different materials.

Abstract- In this paper, we present a new routing approach for MCMs in which the routing space is partitioned into several towers. The routing is carried out in three steps. In the rst step, the routing density is uniformly distributed over the three dimensional routing space. In the next step, the exact locations of nets on the faces of each tower are determined. Finally, the exact paths for the nets in each tower are determined and routed. Unlike the traditional MCM routing which converts the three dimensional routing problem into a set of two dimensional routing problems, our approach decomposes the problem into a set of smaller, yet three dimensional tower routing problems. Experimental results show the validity of our methodology. 1