Full-batch Graph Neural Network (GNN) training is indispensable for interdisciplinary applications. Although fullbatch training has advantages in convergence accuracy and speed, it still faces challenges such as severe load imbalance and high communication traffic overhead. In order to address these challenges, we propose ParGNN, an efficient full-batch training system for GNNs, which adopts a profiler-guided adaptive load balancing method along with graph over-partition to alleviate load imbalance. Based on the over-partition results, we present a subgraph pipeline algorithm to overlap communication and computation while maintaining the accuracy of GNN training. Extensive experiments demonstrate that ParGNN can not only obtain the highest accuracy but also reach the preset accuracy in the shortest time. In the end-to-end experiments performed on the four datasets, ParGNN outperforms the two state-of-theart full-batch GNN systems, PipeGCN and DGL, achieving the highest speedup of $2.7 \times$ and $21.8 \times$ times respectively.

General-purpose Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental kernel in scientific computing and deep learning. The emergence of new matrix computation units such as Tensor Cores (TCs) brings more opportunities for SpMM acceleration. However, in order to fully unleash the power of hardware performance, systematic optimization is required. In this paper, we propose Acc-SpMM, a high-performance SpMM library on TCs, with multiple optimizations, including data-affinity-based reordering, memory efficient compressed format, high-throughput pipeline, and adaptive sparsity-aware load balancing. In contrast to the state-of-the-art SpMM kernels on various NVIDIA GPU architectures with a diverse range of benchmark matrices, Acc-SpMM achieves significant performance improvements, on average 2.52x (up to 5.11x) speedup on RTX 4090, on average 1.91x (up to 4.68x) speedup on A800, and on average 1.58x (up to 3.60x) speedup on H100 over cuSPARSE.

The Atomic Kinetic Monte Carlo (AKMC) method provides insights into the macroscopic behavior of materials through atomistic-level simulations and finds broad applications in materials science innovation. Improving simulation scale and performance remains a consistent focus in the development of parallel AKMC software. We port the AKMC software to GPU clusters. To alleviate the memory pressure in large-scale complex system simulations, we redesign the data layout and propose the Lattice Data Compression and Vacancy Data Decompression algorithms. Additionally, We propose a multi-level pipeline scheme combined with an on-demand communication forwarding and merging strategy to reduce data transfer and communication overhead. Compared to state-of-the-art KMC software, MISA-AKMC achieves a 10.41-fold improvement in computational throughput and a 52.07-fold expansion in simulation scale. We implement the first true micrometer-scale AKMC simulation involving 20 quadrillion atoms on GPU clusters. MISA-AKMC achieves 96.03% parallel efficiency in weak scaling and 85.29% in strong scaling on 16,000 GPUs.

Full-batch graph neural network (GNN) training is essential for interdisciplinary applications. Large-scale graph data is usually divided into subgraphs and distributed across multiple compute units to train GNN. The state-of-the-art load balancing method based on direct graph partition is too rough to effectively achieve true load balancing on GPU clusters. We propose ParGNN, which employs a profiler-guided load balance workflow in conjunction with graph repartition to alleviate load imbalance and minimize communication traffic. Experiments have verified that ParGNN has the capability to scale to larger clusters.

The Method Of Characteristic (MOC) to solve the Neutron Transport Equation (NTE) is the core of full-core simulation for reactors. High resolution is enabled by discretizing the NTE through massive tracks to traverse the 3D reactor geometry. However, the 3D full-core simulation is prohibitively expensive because of the high memory consumption and the severe load imbalance. To deal with these challenges, we develop ANT-MOC1. Specifically, we build a performance model for memory footprint, computation and communication, based on which a track management strategy is proposed to overcome the resolution bottlenecks caused by limited GPU memory. Furthermore, we implement a novel multi-level load mapping strategy to ensure load balancing among nodes, GPUs, and CUs. ANT-MOC enables a 3D full-core reactor simulation with 100 billion tracks on 16,000 GPUs, with 70.69% and 89.38% parallel efficiency for strong scalability and weak scalability, respectively.

Structural dynamics simulation plays an important role in research on reactor design and complex engineering. The Hybrid Total Finite Element Tearing and Interconnecting (HTFETI) method combined with Newmark method is an efficient way to solve large-scale structural dynamics problems. However, the sparse direct solver and the load imbalance caused by inconsistent density models are two critical issues limiting the performance and the scalability of structural dynamics computing. For the former, we propose an efficient variable-size batched method to accelerate SpMV on GPUs. For the latter, we establish an online performance prediction model, based on which we then design a novel inter-cluster subdomain fine-tuning algorithm to balance the workload of HTFETI parallel computing. We are the first to achieve the high-fidelity structural dynamics simulation of China Experimental Fast Reactor core assembly with up to 53.4 billion grids. The weak and strong scalability efficiencies reach 91.77% and 86.13% on 12,800 GPUs, respectively.