AI Benchmark Democratization and Carpentry
Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring dynamic,...
Deep RC: A Scalable Data Engineering and Deep Learning Pipeline
Significant obstacles exist in scientific domains including genetics, climate modeling, and astronomy due to the management, preprocess, and training on complicated data for deep learning. Even...
An MLCommons Scientific Benchmarks Ontology
Scientific machine learning research spans diverse domains and data modalities, yet existing benchmark efforts remain siloed and lack standardization. This makes novel and transformative...
Towards Experiment Execution in Support of Community Benchmark Workflows for HPC
A key hurdle is demonstrating compute resource capability with limited benchmarks. We propose workflow templates as a solution, offering adaptable designs for specific scientific applications. Our...
In-depth Analysis On Parallel Processing Patterns for High-Performance Dataframes
The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and...
Design and Implementation of an Analysis Pipeline for Heterogeneous Data
Managing and preparing complex data for deep learning, a prevalent approach in large-scale data science can be challenging. Data transfer for model training also presents difficulties, impacting...
An Overview of MLCommons Cloud Mask Benchmark: Related Research and Data
Cloud masking is a crucial task that is well-motivated for meteorology and its applications in environmental and atmospheric sciences. Its goal is, given satellite images, to accurately generate...
Hybrid Reusable Computational Analytics Workflow Management with Cloudmesh
In this paper, we summarize our effort to create and utilize a simple framework to coordinate computational analytics tasks with the help of a workflow system. Our design is based on a...
Time Series Analysis of Blockchain-Based Cryptocurrency Price Changes
In this paper we apply neural networks and Artificial Intelligence (AI) to historical records of high-risk cryptocurrency coins to train a prediction model that guesses their price. This paper's...
HPTMT: Operator-Based Architecture for Scalable High-Performance Data-Intensive Frameworks
Data-intensive applications impact many domains, and their steadily increasing size and complexity demands high-performance, highly usable environments. We integrate a set of ideas developed in...
AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariates
The COVID-19 pandemic has profound global consequences on health, economic, social, political, and almost every major aspect of human life. Therefore, it is of great importance to model COVID-19...
HPTMT Parallel Operators for High Performance Data Science & Data Engineering
Data-intensive applications are becoming commonplace in all science disciplines. They are comprised of a rich set of sub-domains such as data engineering, deep learning, and machine learning....