Search Results for author:
Found 20 papers, 3 papers with code
RINAS: Training with Dataset Shuffling Can Be General and Fast
However, loading shuffled data for large datasets incurs significant overhead in the deep learning pipeline and severely impacts the end-to-end training throughput.
RTP: Rethinking Tensor Parallelism with Memory Deduplication
In the evolving landscape of neural network models, one prominent challenge stand out: the significant memory overheads associated with training expansive models.
In-depth Analysis On Parallel Processing Patterns for High-Performance Dataframes
In this paper, we are expanding on the initial concept by introducing a cost model for evaluating the said patterns.
Analyzing the Performance of Deep Encoder-Decoder Networks as Surrogates for a Diffusion Equation
Our results indicate that increasing the size of the training set has a substantial effect on reducing performance fluctuations and overall error.
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.
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events
In contrast, applications in social networks, road traffic, physics, and chemical property prediction where data features can be organized with nodes and edges of graphs.
Earthquake Nowcasting with Deep Learning
We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers.
Scientific Machine Learning Benchmarks
In this paper, we describe our approach to the development of scientific machine learning benchmarks and review other approaches to benchmarking scientific machine learning.
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.
HPTMT Parallel Operators for High Performance Data Science & Data Engineering
They are comprised of a rich set of sub-domains such as data engineering, deep learning, and machine learning.
HPTMT: Operator-Based Architecture for Scalable High-Performance Data-Intensive Frameworks
They employ a set of operators on specific data abstractions that include vectors, matrices, tensors, graphs, and tables.
Multidimensional Scaling for Gene Sequence Data with Autoencoders
Multidimensional scaling of gene sequence data has long played a vital role in analysing gene sequence data to identify clusters and patterns.
Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations
To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function.
A Fast, Scalable, Universal Approach For Distributed Data Aggregations
But, we believe that there is an essential requirement for a data analytics tool that can universally integrate with existing frameworks, and thereby increase the productivity and efficiency of the entire data analytics pipeline.
Deep Tiered Image Segmentation For Detecting Internal Ice Layers in Radar Imagery
Understanding the structure of Earth's polar ice sheets is important for modeling how global warming will impact polar ice and, in turn, the Earth's climate.
High Performance Data Engineering Everywhere
In this paper we present Cylon, an open-source high performance distributed data processing library that can be seamlessly integrated with existing Big Data and AI/ML frameworks.
Distributed, Parallel, and Cluster Computing Databases
Scientific Image Restoration Anywhere
We explore this question by evaluating the performance and accuracy of a scientific image restoration model, for which both model input and output are images, on edge computing devices.
Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations
We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities.
Understanding ML driven HPC: Applications and Infrastructure
We recently outlined the vision of "Learning Everywhere" which captures the possibility and impact of how learning methods and traditional HPC methods can be coupled together.
Performance Optimization on Model Synchronization in Parallel Stochastic Gradient Descent Based SVM
In this research, we identify the bottlenecks in model synchronization in parallel stochastic gradient descent (PSGD)-based SVM algorithm with respect to the training model synchronization frequency (MSF).
