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Parallel Programming with CUDA and OpenCL Fundamentals
The "Parallel Programming with CUDA and OpenCL Fundamentals" course teaches how to harness GPU computing power for high-performance applications. Learn how to write optimized code for NVIDIA and cross-platform GPUs, improve thread management, and reduce memory latency. With growing adoption of GPUs in data centers, their cybersecurity exposure is rising. This course highlights how vulnerabilities in GPU kernels can be exploited for side-channel attacks or unauthorized data access. Participants gain both coding proficiency and an awareness of GPU-specific cyber risks and protective measures.
Course Overview
None
Learning Objectives
- Parallel Programming with CUDA and OpenCL Fundamentals
- Master the fundamentals of parallel programming using CUDA and OpenCL.
- Learn how to write and optimize parallel code for heterogeneous computing platforms.
- Understand memory hierarchies and data parallelism concepts.
- Apply parallel programming techniques to solve computationally intensive problems.
- Understand how parallel processing with CUDA and OpenCL can significantly speed up cybersecurity tasks like cryptographic analysis.
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):
Competency Areas
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.