GPU/CPU parallel computation of material damage

Abstract

In this paper compute unified device architecture programming and open multiprocessing are used for the graphics processing unit and central processing unit parallel computation of material damage. The material damage is evaluated by a multilevel finite element analysis within material domains reconstructed from a high-resolution micro-focus X-ray computed tomography system. An effective computational method is investigated for solving the linear equations of finite element analysis. Numerical results show an encouraging trend in reducing the computation cost for the digital diagnosis of material damage.

Energy‐efficient sender‐initiated threshold‐based load balancing (e‐STLB) in cloud computing environment

“…Furthermore, QoS parameters such as availability, resilience, and response time can be enhanced by proficient load balancing. [14][15][16][17][18][19] In this paper, we categorize and review existing approaches aimed at improving energy efficiency in cloud computing. The categories that are most relevant to the proposed study are outlined below.…”

“…Other measures include access control, reuse of data centre heat, renewable energy, and use of durable eco‐friendly hardware. Furthermore, QoS parameters such as availability, resilience, and response time can be enhanced by proficient load balancing 14‐19 . In this paper, we categorize and review existing approaches aimed at improving energy efficiency in cloud computing.…”

A degradation-based sorting method for lithium-ion battery reuse

“…In the field of material damage, various sensing technologies have been used in the past, including ultrasonic wave propagation [13–15], vibration [16], acoustic emission [17], and X-ray computed tomography [18–21]. But, X-ray measurement has not been applied in quantifying the degradation of Lithium-ion batteries.…”