APU Performance Evaluation for Accelerating Computationally Expensive Workloads

Ernesto Rivera-Alvarado; Francisco J. Torres-Rojas

doi:10.1016/j.entcs.2020.02.015

APU Performance Evaluation for Accelerating Computationally Expensive Workloads

Ernesto Rivera-Alvarado
, Francisco J. Torres-Rojas

Costa Rica Institute of Technology

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

1 Cita (Scopus)

Resumen

APUs (Accelerated Processing Units) are widely available in personal computers as low-cost processors that have a CPU and an integrated GPU for displaying graphics, in the same die. Using a ray tracing algorithm as a computationally intensive workload and taking advantage of the APU specific characteristics, we compare the performance of this SoC against CPU and GPU solutions in the same price range.

Idioma original	Inglés
Páginas (desde-hasta)	103-118
Número de páginas	16
Publicación	Electronic Notes in Theoretical Computer Science
Volumen	349
DOI	https://doi.org/10.1016/j.entcs.2020.02.015
Estado	Publicada - 1 jun 2020

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abstract = "APUs (Accelerated Processing Units) are widely available in personal computers as low-cost processors that have a CPU and an integrated GPU for displaying graphics, in the same die. Using a ray tracing algorithm as a computationally intensive workload and taking advantage of the APU specific characteristics, we compare the performance of this SoC against CPU and GPU solutions in the same price range.",

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AU - Torres-Rojas, Francisco J.

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AB - APUs (Accelerated Processing Units) are widely available in personal computers as low-cost processors that have a CPU and an integrated GPU for displaying graphics, in the same die. Using a ray tracing algorithm as a computationally intensive workload and taking advantage of the APU specific characteristics, we compare the performance of this SoC against CPU and GPU solutions in the same price range.

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KW - computationally intensive workloads

KW - CPU

KW - GPU

KW - ray tracing

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APU Performance Evaluation for Accelerating Computationally Expensive Workloads

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