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Pengqi Lu

dblp:270/3861 · DBLP profile ↗
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4ranked-venue papers
1as first author
3since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 4 · 1 first-author · 3 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
High-performance computing · 77% Parallel and multicore computing · 23%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 9 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › parallel computing › parallel machine learning
3d parallelism
0.712023
AGCM-3DLF: Accelerating Atmospheric General Circulation Model via 3-D Parallelization and Leap-Format · IEEE Trans. Parallel Distributed Syst. 2023
High-performance computing › large-scale simulation
atmospheric general circulation model
0.712023
AGCM-3DLF: Accelerating Atmospheric General Circulation Model via 3-D Parallelization and Leap-Format · IEEE Trans. Parallel Distributed Syst. 2023
High-performance computing
performance optimization at scale
0.712023
AGCM-3DLF: Accelerating Atmospheric General Circulation Model via 3-D Parallelization and Leap-Format · IEEE Trans. Parallel Distributed Syst. 2023
High-performance computing
scientific computing systems
0.712023
AGCM-3DLF: Accelerating Atmospheric General Circulation Model via 3-D Parallelization and Leap-Format · IEEE Trans. Parallel Distributed Syst. 2023
Compilers and program optimization
loop optimization
0.512021
Temporal vectorization for stencils · SC 2021
Compilers and program optimization
vectorization
0.512021
Temporal vectorization for stencils · SC 2021
High-performance computing
stencil computation
0.512021
Temporal vectorization for stencils · SC 2021
High-performance computing
supercomputing
0.212023
AGCM-3DLF: Accelerating Atmospheric General Circulation Model via 3-D Parallelization and Leap-Format · IEEE Trans. Parallel Distributed Syst. 2023
Parallel and multicore computing › data parallelism
SIMD vectorization
0.112021
Temporal vectorization for stencils · SC 2021

Methods — techniques the papers use, named apart from their topics

temporal vectorization · 1.0iteration space transformation · 1.0shifting window communication · 0.73-d decomposition · 0.7
YearPublicationVenuePosition
2023 AGCM-3DLF: Accelerating Atmospheric General Circulation Model via 3-D Parallelization and Leap-Format
abstract
The atmospheric general circulation model (AGCM) has been an important research tool in the study of climate change for decades. As the demand for high-resolution simulation is becoming urgent, the scalability and simulation efficiency is faced with great challenges, especially for the latitude-longitude mesh-based models. In this paper, we propose a highly scalable 3-D atmospheric general circulation model based on leap-format, namely AGCM-3DLF. First, it utilizes a 3-D decomposition method allowing for parallelism release in all three physical dimensions. Then the leap-format difference computation scheme is adopted to maintain computational stability in grid updating and avoid additional filtering at the high latitudes. A novel shifting window communication algorithm is designed for parallelization of the unified model. Furthermore, a series of optimizations are conducted to improve the effectiveness of large-scale simulations. Experiment results in different platforms demonstrate good efficiency and scalability of the model. AGCM-3DLF scales up to the entire CAS-Xiandao1 supercomputer (196,608 CPU cores), attaining the speed of 11.1 simulation-year-per-day (SYPD) at a high resolution of 25KM. In addition, simulations conducted on the Sunway TaihuLight supercomputer exhibit a 1.06 million cores scalability with 36.1% parallel efficiency.
He Zhang 0005, Yunquan Zhang, Baodong Wu, Kun Li 0016, Shigang Li 0002, Pengqi Lu, Junmin Xiao
IEEE Trans. Parallel Distributed Syst.9
2021 AutoFlow: Hotspot-Aware, Dynamic Load Balancing for Distributed Stream Processing
Pengqi Lu, Yue Yue, Yunquan Zhang
ICA3PP (3)1
2021 Temporal vectorization for stencils
abstract
Stencil computations represent a very common class of nested loops in scientific and engineering applications. Exploiting vector units in modern CPUs is crucial to achieving peak performance. Previous vectorization approaches often consider the data space, in particular the innermost unit-strided loop. It leads to the well-known data alignment conflict problem that vector loads are overlapped due to the data sharing between continuous stencil computations. This paper proposes a novel temporal vectorization scheme for stencils. It vectorizes the stencil computation in the iteration space and assembles points with different time coordinates in one vector. The temporal vectorization leads to a small fixed number of vector reorganizations that is irrelevant to the vector length, stencil order, and dimension. Furthermore, it is also applicable to Gauss-Seidel stencils, whose vectorization is not well-studied. The effectiveness of the temporal vectorization is demonstrated by various Jacobi and Gauss-Seidel stencils.
Yunquan Zhang, Kun Li 0016, Pengqi Lu, Yue Yue
SC5
2020 A Highly Efficient Dynamical Core of Atmospheric General Circulation Model based on Leap-Format
abstract
The finite-difference dynamical core based on the equal-interval latitude-longitude mesh has been widely used for numerical simulations of the Atmospheric General Circulation Model (AGCM). Previous work utilizes different filtering schemes to alleviate the instability problem incurred by the unequal physical spacing at different latitudes, but they all incur high communication and computation overhead and become a scaling bottleneck. This paper proposes a new leap-format finite-difference computing scheme. It generalizes the usual finite-difference format with adaptive wider intervals and is able to maintain the computational stability in the grid updating. Therefore, the costly filtering scheme is eliminated. The new scheme is parallelized with a shifting communication method and implemented with fine communication optimizations based on a 3D decomposition. With the proposed leap-format computation scheme, the communication overhead of the AGCM is significantly reduced and good load balance is exhibited. The simulation results verify the correctness of the new leap-format scheme. The new scheme achieves the speed of 16.6 simulation-year-per-day (SYPD) and up to 3.3x speedup over the latest implementation.
He Zhang 0005, Baodong Wu, Shigang Li 0002, Pengqi Lu, Yunquan Zhang, Yongjun Xu 0001
IPDPS6