18 citations · 39 across the 10 of their papers we have counts for
10 papers
Addressing Reproducibility Challenges in HPC with Continuous Integration
Valérie Hayot-Sasson, Nathaniel Hudson, André Bauer +3
The high-performance computing (HPC) community has adopted incentive structures to motivate reproducible research, with major conferences awarding badges to papers that meet reprod…
Core Hours and Carbon Credits: Incentivizing Sustainability in HPC
Alok Kamatar, Maxime Gonthier, Valerie Hayot-Sasson +6
Realizing a shared responsibility between providers and consumers is critical to manage the sustainability of HPC. However, while cost may motivate efficiency improvements by infra…
Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing
Haochen Pan, Ryan Chard, Sicheng Zhou +7
Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Even…
Object Proxy Patterns for Accelerating Distributed Applications
J. Gregory Pauloski, Valerie Hayot-Sasson, Logan Ward +4
Workflow and serverless frameworks have empowered new approaches to distributed application design by abstracting compute resources. However, their typically limited or one-size-fi…
GreenFaaS: Maximizing Energy Efficiency of HPC Workloads with FaaS
Alok Kamatar, Valerie Hayot-Sasson, Yadu Babuji +6
Application energy efficiency can be improved by executing each application component on the compute element that consumes the least energy while also satisfying time constraints.…
Oil & Water? Diffusion of AI Within and Across Scientific Fields
Eamon Duede, William Dolan, André Bauer +2
This study empirically investigates claims of the increasing ubiquity of artificial intelligence (AI) within roughly 80 million research publications across 20 diverse scientific f…
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Nathaniel Hudson, J. Gregory Pauloski, Matt Baughman +13
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we…
Comprehensive Exploration of Synthetic Data Generation: A Survey
André Bauer, Simon Trapp, Michael Stenger +5
Recent years have witnessed a surge in the popularity of Machine Learning (ML), applied across diverse domains. However, progress is impeded by the scarcity of training data due to…
Attention Lens: A Tool for Mechanistically Interpreting the Attention Head Information Retrieval Mechanism
Mansi Sakarvadia, Arham Khan, Aswathy Ajith +5
Transformer-based Large Language Models (LLMs) are the state-of-the-art for natural language tasks. Recent work has attempted to decode, by reverse engineering the role of linear l…
Memory Injections: Correcting Multi-Hop Reasoning Failures during Inference in Transformer-Based Language Models
Mansi Sakarvadia, Aswathy Ajith, Arham Khan +5
Answering multi-hop reasoning questions requires retrieving and synthesizing information from diverse sources. Large Language Models (LLMs) struggle to perform such reasoning consi…