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The funding information displayed below comes from the
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The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
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High-probability grants
According to our matching algorithm, Ying Zhu is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2013 — 2017 |
Owen, Scott Zhu, Ying |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Courseware For Improving Undergraduate Students' Debugging Skill in Gpu Programming @ Georgia State University Research Foundation, Inc.
This project is developing educational materials to help undergraduate computer science students improve their debugging skill in GPU (Graphics Processing Unit) programming. As multi-core processor architecture becomes the norm, undergraduate computer science students need to shift their focus to multi-core parallel programming. GPU programming is an important part of multi-core parallel programming, especially in heterogeneous computing environments that use a mixture of CPUs, GPUs, and other specialty cores. To write GPU programs, students need to learn special programming languages and new debugging skills. Debugging is particularly challenging for GPU programming because of the complexity of parallel processing in multi-core environments. However, there is a shortage of educational materials for teaching how to debug GPU programs. The project goals are as follows:
1. Software defect pattern analysis: Collect and analyze common mistakes made by novice programmers in GPU programming. 2. Teaching materials: Based on the analysis of the software defects, develop teaching materials such as debugging checklists; create tutorials to teach debugging techniques in GPU programming; develop debugging exercises for students to practice their debugging skills. 3. Evaluation: Apply the educational materials to parallel and distributed computing courses and computer graphics courses, and measure the efficacy of this approach. 4. Dissemination: Share the educational materials online with students and instructors and publicize their existence through other online venues such as the ACM SIGGRAPH Education Committee site and print venues such as the IEEE Computer Graphics & Applications Education Department.
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