Suchergebnisse - Computing methodologies→Massively parallel algorithms

ANT-MOC: Scalable Neutral Particle Transport Using 3D Method of Characteristics on Multi-GPU Systems von Li, Shunde, Wang, Zongguo, Bu, Lingkun, Wang, Jue, Xin, Zhikuang, Li, Shigang, Wang, Yangang, Feng, Yangde, Shi, Peng, Hu, Yun, Chi, Xuebin

“… The Method Of Characteristic (MOC) to solve the Neutron Transport Equation (NTE) is the core of full-core simulation for reactors. High resolution is enabled …”
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Automatic Loop Invariant Generation for Data Dependence Analysis von Tabar, Asmae Heydari, Bubel, Richard, Hahnle, Reiner

“… Parallelization of programs relies on sound and precise analysis of data dependences in the code, specifically, when dealing with loops. State-of-art tools are …”
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In Situ Workload Estimation for Block Assignment and Duplication in Parallelization‐Over‐Data Particle Advection von Wang, Zhe, Moreland, Kenneth, Larsen, Matthew, Kress, James, Childs, Hank, Li, Guan, Shan, Guihua, Pugmire, David

“… Particle advection is a foundational algorithm for analyzing a flow field. The commonly used Parallelization‐Over‐Data (POD …”
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On‐The‐Fly Tracking of Flame Surfaces for the Visual Analysis of Combustion Processes von Oster, T., Abdelsamie, A., Motejat, M., Gerrits, T., Rössl, C., Thévenin, D., Theisel, H.

“… We present an on‐the‐fly method for tracking the flame surface directly during simulation and computing the local tangential surface deformation for arbitrary time intervals …”
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A Halfedge Refinement Rule for Parallel Catmull‐Clark Subdivision von Dupuy, J., Vanhoey, K.

“… We leverage these results to derive a novel parallel implementation of Catmull‐Clark subdivision suitable for the GPU …”
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Montblanc11https://github.com/ska-sa/montblanc.: GPU accelerated radio interferometer measurement equations in support of Bayesian inference for radio observations von Perkins, S.J., Marais, P.C., Zwart, J.T.L., Natarajan, I., Tasse, C., Smirnov, O.

“… As most of the elements of the RIME and χ2 calculation are independent of one another, they are highly amenable to parallel computation …”
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Scaling deep learning on GPU and knights landing clusters von You, Yang, Buluç, Aydın, Demmel, James

“… ) clusters and multi-GPU clusters as our target platforms. From the algorithm aspect, we focus on Elastic Averaging SGD (EASGD …”
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Massively parallel 3D image reconstruction von Wang, Xiao, Sabne, Amit, Sakdhnagool, Putt, Kisner, Sherman J., Bouman, Charles A., Midkiff, Samuel P.

“… This paper presents a new algorithm for MBIR, the Non-Uniform Parallel Super-Voxel (NU-PSV) algorithm, that regularizes the data access pattern, enables massive parallelism, and ensures fast convergence …”
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Scalable reduction collectives with data partitioning-based multi-leader design von Bayatpour, Mohammadreza, Chakraborty, Sourav, Subramoni, Hari, Lu, Xiaoyi, Panda, Dhabaleswar K. (DK)

“… Existing designs for MPI_Allreduce do not take advantage of the vast parallelism available in modern multi-/many-core processors like Intel Xeon/Xeon Phis or …”
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Seed-and-Vote based In-Memory Accelerator for DNA Read Mapping von Laguna, Ann Franchesca, Gamaarachchi, Hasindu, Yin, Xunzhao, Niemier, Michael, Parameswaran, Sri, Hu, X. Sharon

“… In-memory computing can help address the memory-bandwidth bottleneck by minimizing data transfers …”
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Gravel: fine-grain GPU-initiated network messages von Orr, Marc S., Che, Shuai, Beckmann, Bradford M., Oskin, Mark, Reinhardt, Steven K., Wood, David A.

“… (implemented with CPU threads), which combines messages targeting to the same destination. Gravel leverages diverged work-group-level semantics to amortize synchronization across the GPU's data-parallel lanes …”
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DARIS: An Oversubscribed Spatio-Temporal Scheduler for Real-Time DNN Inference on GPUs von Babaei, Amir Fakhim, Chantem, Thidapat

“… In particular, DARIS improves GPU utilization and uniquely analyzes GPU concurrency by oversubscribing computing resources …”
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Skywalker: Efficient Alias-Method-Based Graph Sampling and Random Walk on GPUs von Wang, Pengyu, Li, Chao, Wang, Jing, Wang, Taolei, Zhang, Lu, Leng, Jingwen, Chen, Quan, Guo, Minyi

“… Graph sampling and random walk operations, capturing the structural properties of graphs, are playing an important role today as we cannot directly adopt computing-intensive algorithms on large-scale graphs …”
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MAD-Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems von Hsia, Samuel, Golden, Alicia, Acun, Bilge, Ardalani, Newsha, DeVito, Zachary, Wei, Gu-Yeon, Brooks, David, Wu, Carole-Jean

“… Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs …”
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HybriMoE: Hybrid CPU-GPU Scheduling and Cache Management for Efficient MoE Inference von Zhong, Shuzhang, Sun, Yanfan, Liang, Ling, Wang, Runsheng, Huang, Ru, Li, Meng

“… The Mixture of Experts (MoE) architecture has demonstrated significant advantages as it enables to increase the model capacity without a proportional increase …”
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DenSparSA: A Balanced Systolic Array Approach for Dense and Sparse Matrix Multiplication von Wang, Ziheng, Sun, Ruiqi, He, Xin, Ma, Tianrui, Zou, An

“… Numerous studies have proposed hardware architectures to accelerate sparse matrix multiplication, but these approaches often incur substantial area and power …”
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NDFT: Accelerating Density Functional Theory Calculations via Hardware/Software Co-Design on Near-Data Computing System von Jiang, Qingcai, Tu, Buxin, Hao, Xiaoyu, Chen, Junshi, An, Hong

“… Linear-response time-dependent Density Functional Theory (LR-TDDFT) is a widely used method for accurately predicting the excited-state properties of physical …”
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ISAVS: Interactive Scalable Analysis and Visualization System von Petruzza, Steve, Venkat, Aniketh, Gyulassy, Attila, Scorzelli, Giorgio, Pascucci, Valerio, Federer, Frederick, Angelucci, Alessandra, Bremer, Peer-Timo

“… Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability …”
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SFLU: Synchronization-Free Sparse LU Factorization for Fast Circuit Simulation on GPUs von Zhao, Jianqi, Wen, Yao, Luo, Yuchen, Jin, Zhou, Liu, Weifeng, Zhou, Zhenya

“… Sparse LU factorization is one of the key building blocks of sparse direct solvers and often dominates the computing time of circuit simulation programs …”
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OpenDRC: An Efficient Open-Source Design Rule Checking Engine with Hierarchical GPU Acceleration von He, Zhuolun, Zuo, Yihang, Jiang, Jiaxi, Zheng, Haisheng, Ma, Yuzhe, Yu, Bei

“… OpenDRC maintains hierarchical layouts with layer-wise bounding volume hierarchies and performs adaptive row-based partition to identify independent regions for check pruning and/or parallel processing …”
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