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Simultaneous Evaluation of Mindful Fault Checking across the CPU and GPU

DescriptionThis work comprehensively analyzes the overhead when implementing fault-checking algorithms for sparse preconditioned conjugate gradient (PCG) solvers on many-core and GPU-accelerated systems. Our objective is to selectively utilize GPUs for duplicate calculations based on the numerical properties of the sparse matrices to enhance the reliability and performance of linear system solutions. Enabling the ability to rely on the relatively underutilized CPU for fault detection improves scientific applications' ability to efficiently manage their resources on large-scale systems. By leveraging existing fault-checking techniques, we validate calculations and address potential numerical instabilities or precision-related issues during iterative solving. Through extensive experimentation on real hardware, we demonstrate the effectiveness of the conjugate gradient algorithm in providing accurate and reliable solutions for large linear systems.

Author

Hayden Estes

University of Alabama, Huntsville

Event Type

ACM Student Research Competition: Graduate Poster

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