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…
arXiv:2511.22442v2 Announce Type: replace-cross
Abstract: Ranking methods or models based on their performance is of prime importance but is tricky because performance is fundamentally multidimensional. In the case of classification, precision and rec…
arXiv:2603.27462v1 Announce Type: cross
Abstract: Matrix-vector multiplication is a fundamental building block in neural networks, vector databases, and large language models, particularly during inference. As a result, efficient matrix-vector multipl…
arXiv:2603.28569v1 Announce Type: new
Abstract: The increasing agentic capabilities of Large Language Models (LLMs) have enabled their deployment in real-world applications, such as cloud services, where customer-assistant interactions exhibit high te…
arXiv:2505.20353v3 Announce Type: replace-cross
Abstract: Diffusion Transformers (DiT) are powerful generative models but remain computationally intensive due to their iterative structure and deep transformer stacks.
To alleviate this inefficiency, …
arXiv:2510.06882v2 Announce Type: replace-cross
Abstract: Edge devices have limited resources, which inevitably leads to situations where stream processing services cannot satisfy their needs. While existing autoscaling mechanisms focus entirely on re…