The field of artificial intelligence and scientific simulations is moving towards more efficient computing methods. Researchers are focusing on developing techniques to reduce computational demands and improve performance. One notable direction is the use of sparse training methods, which can preserve critical structural relationships between weight matrices and enable efficient training of large language models. Another area of research is the development of fault-tolerant algorithms, which can enhance resilience and efficiency in large-scale systems. Additionally, there is a growing interest in optimizing eigenvalue decomposition algorithms for multi-GPU architectures, which can lead to significant speedups in various applications. Noteworthy papers include: EcoSpa, which introduces an efficient structured sparse training method that enables substantial improvements in transformer training. MACKO, which proposes a GPU-optimized format and kernel for sparse matrix-vector multiplication, enabling efficient SpMV for unstructured sparsity. Fault Oblivious Eigenvalue Solver, which presents a novel fault-tolerant eigenvalue solver based on erasure-coded computations. Pipelined Dense Symmetric Eigenvalue Decomposition, which proposes a pipelined two-stage eigenvalue decomposition algorithm that surpasses state-of-the-art baselines.