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Found 12 papers, 1 papers with code
A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators
However, the comparative performance of these AI accelerators on large language models has not been previously studied.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
HPC-GPT: Integrating Large Language Model for High-Performance Computing
Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks.
Data Race Detection Using Large Language Models
Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumventing the need for resource-intensive manual tool creation.
A Survey of Techniques for Optimizing Transformer Inference
This paper presents a comprehensive survey of techniques for optimizing the inference phase of transformer networks.
LM4HPC: Towards Effective Language Model Application in High-Performance Computing
In recent years, language models (LMs), such as GPT-4, have been widely used in multiple domains, including natural language processing, visualization, and so on.
A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems
This end-to-end log analysis system, coupled with visual analytics support, allows users to glean and promptly extract supercomputer usage and error patterns at varying temporal and spatial resolutions.
Transfer Learning Across Heterogeneous Features For Efficient Tensor Program Generation
Tuning tensor program generation involves searching for various possible program transformation combinations for a given program on target hardware to optimize the tensor program execution.
Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study
The FAIR Guiding Principles aim to improve the findability, accessibility, interoperability, and reusability of digital content by making them both human and machine actionable.
FAIR for AI: An interdisciplinary and international community building perspective
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines
Programming Language Processing (PLP) using machine learning has made vast improvements in the past few years.
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights.
