Chen, Yong | Engineering Papers

DOE OSTI2024

Transition metals of Pt and Pd on the surface of topological insulator Bi2Se3

Transition metal catalysts supported on topological insulators are predicted to show improved catalytic properties due to the presence of topological surface states, which may float up to the catalysts and provide robust electron transfer. However, experimental studies of surface structures and corresponding catalytic properties of transition metal/topological insulator heterostructures have not been demonstrated so far. Here, we report the structures, chemical states, and adsorption behaviors of two conventional transition metal catalysts, Pt and Pd, on the surface of Bi2Se3, a common topological insulator material. We reveal that Pt forms nanoparticles on the Bi2Se3 surface. Moreover, the interaction between Pt and surface Se is observed. Furthermore, thermal dosing of O2 onto the Pt/Bi2Se3 heterostructure leads to no oxygen adsorption. Detailed scanning tunneling microscopy study indicates that Pt transforms into PtSe2 after the thermal process, thus preventing O2 from adsorption. For another transition metal Pd, it exhibits approximate layer-island growth on Bi2Se3, and Pd–Se interaction is also observed. Our work provides significant insights into the behaviors of transition metals on top of a common topological insulator material and will assist in the future design of catalysts built with topological materials.

Chemistry↗

DOE OSTI2023

An automated and portable method for selecting an optimal GPU frequency

Power consumption poses a significant challenge in current and emerging graphics processing unit (GPU) enabled high-performance computing systems. In modern GPUs, dynamic voltage frequency scaling (DVFS) appears to be a reliable control to regulate power consumption and performance. However, the DVFS design space is large - hence, brute-force approaches are infeasible to select the optimal frequency. Furthermore, no single frequency can be universally optimal for applications with varying computational intensities. Thus, the application's complexity and the availability of a wide range of frequency settings are a challenge in selecting the optimal frequency configuration for a given GPU workload. To that end, this paper proposes a systematic approach that consists of three steps. The feature characterization study identifies the fine-grain GPU utilization metrics that influence the power consumption and execution time of a given workload. To understand the performance, power, and energy consumption behaviors of a workload across GPU's DVFS design space, we derived analytical power and performance models using the identified fine-grain features. Here, it is shown that the same set of GPU utilization metrics can estimate both the power consumption and execution time while being agnostic of changes to frequency and input sizes. Applying a power control with the single objective of reducing power may cause performance degradation, leading to more energy consumption. A multi-objective approach is proposed to select the optimal GPU DVFS configuration for a workload that reduces power consumption with negligible degradation in performance. The evaluation was conducted using SPEC ACCEL benchmarks and three real applications - NAMD LAMMPS, and LSTM on NVIDIA GV100, GA100, and AMD MI210 GPUs. On average, real applications showed 29.6% energy savings with a performance loss of 5.2% on GA100 and 22.6% energy savings with a performance loss of 4.7% on GV100. Moreover, the proposed models are portable to real applications, GPU architectures, and vendors, and require metric collection at only the default frequency rather than all supported DVFS configurations. Additionally, we conducted a comparison between our models and the GPU assembly instructions (PTX)-based static models. The results revealed a significant reduction in the average error rates, with a decrease from 19.7% to 3.1% for power models and from 29.4% to 5.2% for performance models.

97 MATHEMATICS AND COMPUTING↗