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This paper presents the implementation of a parameter-free third-order recon- struction method for cell-centered finite volume solvers on unstructured grids. The reconstruction is based on nodal gradients obtained using the least squares approach from solutions at adjacent cell centers. The cell and face gradients are computed by simple arithmetic averaging of vertex gradients, while the face values are obtained through quadratic interpolation. Importantly, the current reconstruction method does not require explicit second derivatives, and its stencil remains as compact as that used in traditional linear reconstruction methods. The third-order accuracy of the left and right states at the face values, along with the second-order accuracy of the face gradients, is numerically verified on various unstructured grids. This verified third-order accuracy is a crucial condition for ensuring the overall accuracy of the finite volume solver. 

Point cloud is an important type of geometric data structure for many embedded applications such as autonomous driving and augmented reality. Current Point Cloud Networks (PCNs) have proven to achieve great success in using inference to perform point cloud analysis, including object part segmentation, shape classification, and so on. However, point cloud applications on the computing edge require more than just the inference step. They require an end-to-end (E2E) processing of the point cloud workloads: pre-processing of raw data, input preparation, and inference to perform point cloud analysis. Current PCN approaches to support end-to-end processing of point cloud workload cannot meet the real-time latency requirement on the edge, i.e., the ability of the AI service to keep up with the speed of raw data generation by 3D sensors. Latency for end-to-end processing of the point cloud workloads stems from two reasons: memory-intensive down-sampling in the pre-processing phase and the data structuring step for input preparation in the inference phase. In this paper, we present HgPCN, an end-to-end heterogeneous architecture for real-time embedded point cloud applications. In HgPCN, we introduce two novel methodologies based on spatial indexing to address the two identified bottlenecks. In the Pre-processing Engine of HgPCN, an Octree-Indexed-Sampling method is used to optimize the memory-intensive down-sampling bottleneck of the pre-processing phase. In the Inference Engine, HgPCN extends a commercial DLA with a customized Data Structuring Unit which is based on a Voxel-Expanded Gathering method to fundamentally reduce the workload of the data structuring step in the inference phase. The initial prototype of HgPCN has been implemented on an Intel PAC (Xeon+FPGA) platform. Four commonly available point cloud datasets were used for comparison, running on three baseline devices: Intel Xeon W-2255, Nvidia Xavier NX Jetson GPU, and Nvidia 4060ti GPU. These point cloud datasets were also run on two existing PCN accelerators for comparison: PointACC and Mesorasi. Our results show that for the inference phase, depending on the dataset size, HgPCN achieves speedup from 1.3× to 10.2× vs. PointACC, 2.2× to 16.5× vs. Mesorasi, and 6.4× to 21× vs. Jetson NX GPU. Along with optimization of the memory-intensive down-sampling bottleneck in pre-processing phase, the overall latency shows that HgPCN can reach the real-time requirement by providing end-to-end service with keeping up with the raw data generation rate. 

Between May 25, 2023 and June 21, 2023, we hosted the inaugural four-week High-Performance Computing Summer Institute at Jackson State University. This endeavor was made possible through the support of a three-year NSF CISE-MSI grant. The primary objective of this Summer Institute revolved around the engagement, education, and empowerment of minority and underrepresented students in the realm of High-Performance Computing (HPC) within the field of engineering. Nine undergraduate students with diverse background were recruited to participate in this program.  Throughout the program, we immersed these students in a comprehensive curriculum that covered various critical facets of HPC. This curriculum encompassed hands-on instruction in Linux operating system command-line operations, C programming within the Linux environment, fundamental HPC concepts, parallel computing utilizing the Message Passing Interface (MPI) library, and GPU computing through OpenCL. Additionally, we delved into foundational aspects of fluid mechanics, geometric modeling, mesh generation, flow simulation via our in-house flow solvers, and the visualization of solutions. At the end of the program, every participant was tasked with delivering an oral presentation and submitting a written report encapsulating their acquired knowledge and experiences during the program. We are excited to share a detailed overview of our program's implementation with our audience. This includes insights into our utilization of ChatGPT to enhance C programming learning and our suggestion of the NSF ACCESS resources to gain access to HPC systems. We are proud to announce that the program has achieved remarkable success, as evidenced by the positive feedback we received from the participants. 

This paper presents a robust mesh moving solver developed to address moving boundary problems. Crucially, the resulting deformed mesh retains the same topology as the original mesh without being overly distorted. The mesh is treated as an elastic material, and the deformation of the computational domain resulting from moving boundaries is determined by solving the equilibrium linear elasticity equations. The linear elasticity equations are discretized by the classic Galerkin finite element method and solved by the block conjugate gradient iterative method. To maintain the quality of the mesh after motion, the Young's modulus of each element is weighted by the reciprocal of the distance between the element center and the moving boundaries. The effectiveness of this approach is demonstrated through a set of 2D and 3D test cases featuring prescribed translational and/or rotational motion of the embedded object. The method is now ready for integration into our existing in-house CFD solvers. 

Multi-robot cooperative control has been extensively studied using model-based distributed control methods. However, such control methods rely on sensing and perception modules in a sequential pipeline design, and the separation of perception and controls may cause processing latencies and compounding errors that affect control performance. End-to-end learning overcomes this limitation by implementing direct learning from onboard sensing data, with control commands output to the robots. Challenges exist in end-to-end learning for multi-robot cooperative control, and previous results are not scalable. We propose in this article a novel decentralized cooperative control method for multi-robot formations using deep neural networks, in which inter-robot communication is modeled by a graph neural network (GNN). Our method takes LiDAR sensor data as input, and the control policy is learned from demonstrations that are provided by an expert controller for decentralized formation control. Although it is trained with a fixed number of robots, the learned control policy is scalable. Evaluation in a robot simulator demonstrates the triangular formation behavior of multi-robot teams of different sizes under the learned control policy. 

Multi-principal element alloys (MPEAs) exhibit outstanding strength attributed to the complex dislocation dynamics as compared to conventional alloys. Here, we develop an atomic-lattice-distortion-dependent discrete dislocation dynamics framework consisted of random field theory and phenomenological dislocation model to investigate the fundamental deformation mechanism underlying massive dislocation motions in body-centered cubic MPEA. Amazingly, the turbulence of dislocation speed is identified in light of strong heterogeneous lattice strain field caused by short-range ordering. Importantly, the vortex from dislocation flow turbulence not only acts as an effective source to initiate dislocation multiplication but also induces the strong local pinning trap to block dislocation movement, thus breaking the strength-ductility trade-off.