Data Structures for Graphics Processing Units (GPUs)

Dear Colleagues,

This Special Issue will delve into the innovative and rapidly evolving field of data structures tailored to graphics processing units (GPUs). GPUs, originally designed for rendering graphics, have emerged as powerful parallel processors, revolutionizing computational tasks across diverse domains. This Special Issue will explore the development and optimization of data structures that leverage the parallel processing capabilities of GPUs to achieve significant performance enhancements.

Contributors to this Special Issue will present cutting-edge research into a variety of GPU-optimized data structures, including, but not limited to, stacks, queues, trees, graphs, hash tables, and priority queues. The articles will highlight novel approaches to memory management, data access patterns, and algorithmic modifications that harness the massive parallelism of GPUs. Furthermore, this Special Issue will address practical challenges, such as synchronization, load balancing, and efficient data transfer between CPU and GPU memory.

By featuring both theoretical advancements and practical implementations, this Special Issue will bridge the gap between traditional CPU-centric data structures and their GPU-optimized counterparts. Readers will gain insights into the latest techniques for maximizing GPU performance, making this Special Issue an essential resource for researchers and practitioners seeking to exploit the full potential of GPU computing for data-intensive applications.

Dr. Byunghyun Jang
Prof. Dr. Juan A. Gómez-Pulido
Guest Editors

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