18 citations · 39 across the 41 of their papers we have counts for
16 papers · 1 filter
Parsl+CWL: Towards Combining the Python and CWL Ecosystems
Nishchay Karle, Ben Clifford, Yadu Babuji +3
The Common Workflow Language (CWL) is a widely adopted language for defining and sharing computational workflows. It is designed to be independent of the execution engine on which…
LSHBloom: Memory-efficient, Extreme-scale Document Deduplication
Arham Khan, Robert Underwood, Carlo Siebenschuh +7
Contemporary large language model (LLM) training pipelines require the assembly of internet-scale databases full of text data from a variety of sources (e.g., web, academic, and pu…
Accelerating Python Applications with Dask and ProxyStore
J. Gregory Pauloski, Klaudiusz Rydzy, Valerie Hayot-Sasson +2
Applications are increasingly written as dynamic workflows underpinned by an execution framework that manages asynchronous computations across distributed hardware. However, execut…
Deep Model Merging: The Sister of Neural Network Interpretability -- A Survey
Arham Khan, Todd Nief, Nathaniel Hudson +6
We survey the model merging literature through the lens of loss landscape geometry to connect observations from empirical studies on model merging and loss landscape analysis to ph…
Mitigating Memorization In Language Models
Mansi Sakarvadia, Aswathy Ajith, Arham Khan +6
Language models (LMs) can "memorize" information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that d…
Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning
Nathaniel Hudson, Valerie Hayot-Sasson, Yadu Babuji +5
Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server. Existing FL frameworks…
Employing Artificial Intelligence to Steer Exascale Workflows with Colmena
Logan Ward, J. Gregory Pauloski, Valerie Hayot-Sasson +6
Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of the…
TaPS: A Performance Evaluation Suite for Task-based Execution Frameworks
J. Gregory Pauloski, Valerie Hayot-Sasson, Maxime Gonthier +5
Task-based execution frameworks, such as parallel programming libraries, computational workflow systems, and function-as-a-service platforms, enable the composition of distinct tas…
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.…
FastqZip: An Improved Reference-Based Genome Sequence Lossy Compression Framework
Yuanjian Liu, Huihao Luo, Zhijun Han +6
Storing and archiving data produced by next-generation sequencing (NGS) is a huge burden for research institutions. Reference-based compression algorithms are effective in dealing…
UniFaaS: Programming across Distributed Cyberinfrastructure with Federated Function Serving
Yifei Li, Ryan Chard, Yadu Babuji +3
Modern scientific applications are increasingly decomposable into individual functions that may be deployed across distributed and diverse cyberinfrastructure such as supercomputer…
Combining Language and Graph Models for Semi-structured Information Extraction on the Web
Zhi Hong, Kyle Chard, Ian Foster
Relation extraction is an efficient way of mining the extraordinary wealth of human knowledge on the Web. Existing methods rely on domain-specific training data or produce noisy ou…
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…