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Found 7 papers, 1 papers with code
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.
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.
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.
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.
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).
