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詳細
開催日 2024年10月11日(金)
開催時間 15:00 - 17:00(15:00 - 15:40 講演者2名による講演、15:50 - 16:10 討論、16:10 - 自由討論とコーヒーブレイク(参加自由))
開催都市 兵庫県神戸市/オンライン
場所

計算科学研究センター(R-CCS)6階講堂/Zoomによる遠隔セミナー

使用言語 発表・スライド共に英語
登壇者

Lingqi Zhang

高性能人工知能システム研究チーム
特別研究員

Md Anowar Hossain

離散事象シミュレーション研究チーム
特別研究員

講演題目・要旨

1st Speaker: Lingqi Zhang


Title:
Overcoming the Gap Between Compute and Memory Bandwidth in Modern GPUs
Abstract:
The imbalance between compute and memory bandwidth has been a long-standing issue. Despite efforts to address it, the gap between them is still widening. This has led to the categorization of many applications as memory-bound kernels. This dissertation centers on memory-bound kernels, with a particular emphasis on Graphics Processing Units (GPUs), given their rising prevalence in High-Performance Computing (HPC) systems. In this dissertation, we initially focus on the evolution trend of GPU development in the last decades. Examples include cooperative groups (i.e., device-wide barriers), asynchronous copy of shared memory (i.e., hardware prefetching), low(er) latency of operations, and larger volume of on-chip resources (register files and L1 cache). This dissertation seeks to utilize the latest GPU features to optimize memory-bound kernels. Specifically, we propose extending the kernel's lifetime across the time steps and taking advantage of the large volume of on-chip resources (i.e., register files and scratchpad memory) to reduce or eliminate traffic to the device memory. Furthermore, we champion a minimum level of parallelism to maximize the available on-chip resources. Based on the strategies, we propose a general execution model for running memory-bound iterative GPU kernels: PERsistent KernelS (PERKS) and a novel temporal blocking method, EBISU. Evaluations have shown outstanding performance in the latest GPU architectures compared with counterpart state-of-the-art implementations.

2nd Speaker: Md Anowar Hossain


Title:
Cognitive Driver’s Sensitivity for Next-generation Automated Cruising Traffic
Abstract:
A traffic flow model, the so-called Taillight Adapting traffic model, is developed considering the driver's activities impact following the taillight effect of the preceding vehicle, which is switched on and off based on the vehicle's accelerations and decelerations. To quantify a driver's sensitivity to the velocity gap, we introduced the concept of the new driver's sensitivity function, which has twofold formulations. The first formulation follows the HDDS model1, which presumes the sensitivity is dependent on a headway distance. The second formulation presumes that the sensitivity depends on a velocity gap, which might be informed by the taillight of a preceding vehicle. We have calibrated the neutralizing proficiency of a traffic flow field for the HDDS and proposed the Taillight Adapting model by utilizing the linear stability theory. Finally, a series of numerical simulations have been carried out of the improved Taillight Adapting model and conventional FVD model to investigate how the traffic flow field for the Taillight Adapting model behaves differently from the conventional FVD model.

注意事項

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(2024年10月7日)