High Performance Computing with Sparse Data
Konu özeti
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Skill Level: Intermediate
Language: English
Workload: 2 hours total
Topic: High Performance Computing with Sparse Data: Graphs, Matrices and Tensors
Overview: This lecture focuses on High-Performance Computing (HPC) techniques for sparse data, including graphs, matrices, and tensors.
Course Description: The lecture explores the critical data structures, such as coordinate and compressed sparse formats, that underpin efficient storage and computation on sparse datasets. Students will learn about the principles of parallelism, distributed computing and their challenges in sparse systems, including handling irregular data access patterns and balancing computational loads. The session also covers practical applications, such as graph analysis and tensor operations emphasizing their use in machine learning, scientific computing, and network analysis.
Course Contents:
Part 1: An Introduction to Sparse Data with Graphs
Part 2: Architectural Considerations for Sparsity
Part 3: Sparse Matrices
Part 4: Sparse Tensors
Who Should Enroll: Anyone who is working with sparse data structures and want more performance on their codes.
Prerequisite: Familiarity with C++ (not necessary)
Tools, libraries, frameworks used: SparseViz
Learning Objectives: By participating in this course, you will learn:
What is sparse data, what is sparsity?
How to store sparse data, how to process it efficiently?
About the instructor(s): Kamer Kaya is an Associate Professor at the Faculty of Engineering and Natural Sciences at Sabancı University. His research areas include high-performance computing, machine learning on sparse data, and graph algorithms.