arXiv:2604.02344v1 Announce Type: new
Abstract: WebGPU’s security-focused design imposes per-operation validation that compounds across the many small dispatches in neural network inference, yet the true cost of this overhead is poorly characterized. …
arXiv:2604.02556v1 Announce Type: new
Abstract: Large language models (LLMs) have grown beyond the memory capacity of single GPU devices, necessitating quantization techniques for practical deployment. While NF4 (4-bit NormalFloat) quantization enable…
arXiv:2603.20920v2 Announce Type: replace-cross
Abstract: The exponential growth in data has intensified the demand for computational power to train large-scale deep learning models. However, the rapid growth in model size and complexity raises concer…
arXiv:2604.01489v1 Announce Type: new
Abstract: High-performance GPU kernels are critical to modern machine learning systems, yet developing efficient implementations remains a challenging, expert-driven process due to the tight coupling between algor…
arXiv:2604.02131v1 Announce Type: cross
Abstract: Cloud computing allows scalable resource provisioning, but dynamic workload changes often lead to higher costs due to over-provisioning. Machine learning (ML) approaches, such as Long Short-Term Memory…
arXiv:2604.02158v1 Announce Type: cross
Abstract: Efficient utilization of GPU resources and power has become critical with the growing demand for GPUs in high-performance computing (HPC). In this paper, we analyze GPU utilization and GPU memory utili…
arXiv:2604.00222v1 Announce Type: cross
Abstract: Performance regression testing is essential in large-scale continuous-integration (CI) systems, yet executing full performance suites for every commit is prohibitively expensive. Prior work on performa…
arXiv:2511.22302v2 Announce Type: replace
Abstract: Numerical simulations have revolutionized the industrial design process by reducing prototyping costs, design iterations, and enabling product engineers to explore the design space more efficiently. …
arXiv:2603.28823v1 Announce Type: cross
Abstract: Scaling laws relate model quality to compute budget (FLOPs), but practitioners face wall-clock time constraints, not compute budgets. We study optimal model sizing under fixed time budgets from 5 minut…
arXiv:2603.26823v1 Announce Type: new
Abstract: The development of large-scale foundation models, particularly Large Language Models (LLMs), is constrained by significant computational and memory bottlenecks. These challenges elevate throughput optimi…