output
20172026
most citedInverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning

37 citations

Showing cs.DCShow all

7 papers · 1 filter

cs.DC2026

Multi-Partner Project: Multi-GPU Performance Portability Analysis for CFD Simulations at Scale

Panagiotis-Eleftherios Eleftherakis, George Anagnostopoulos, Anastassis Kapetanakis +10

As heterogeneous supercomputing architectures leveraging GPUs become increasingly central to high-performance computing (HPC), it is crucial for computational fluid dynamics (CFD)…

cs.DC2025

Modeling the Potential of Message-Free Communication via CXL.mem

Stepan Vanecek, Matthew Turner, Manisha Gajbe +2

Heterogeneous memory technologies are increasingly important instruments in addressing the memory wall in HPC systems. While most are deployed in single node setups, CXL.mem is a t…

cs.DC20254 cited

Testing and benchmarking emerging supercomputers via the MFC flow solver

Benjamin Wilfong, Anand Radhakrishnan, Henry A. Le Berre +3

Deploying new supercomputers requires testing and evaluation via application codes. Portable, user-friendly tools enable evaluation, and the Multicomponent Flow Code (MFC), a compu…

cs.DC20254 cited

Characterizing and Optimizing LLM Inference Workloads on CPU-GPU Coupled Architectures

Prabhu Vellaisamy, Thomas Labonte, Sourav Chakraborty +3

Large language model (LLM)-based inference workloads increasingly dominate data center costs and resource utilization. Therefore, understanding the inference workload characteristi…

cs.DC20241 cited

Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows

Rafael Ferreira da Silva, Deborah Bard, Kyle Chard +108

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensit…

cs.DC202425 cited

Sustainability of Data Center Digital Twins with Reinforcement Learning

Soumyendu Sarkar, Avisek Naug, Antonio Guillen +4

The rapid growth of machine learning (ML) has led to an increased demand for computational power, resulting in larger data centers (DCs) and higher energy consumption. To address t…