Search Results - Theory of computation → Massively parallel algorithms

Hierarchical Rasterization of Curved Primitives for Vector Graphics Rendering on the GPU by Dokter, Mark, Hladky, Jozef, Parger, Mathias, Schmalstieg, Dieter, Seidel, Hans‐Peter, Steinberger, Markus

“… Our rasterizer is fast and scalable, works on all patches in parallel, and does not require any approximations…”
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Skywalker: Efficient Alias-Method-Based Graph Sampling and Random Walk on GPUs by 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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Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds by Jakob, J., Buchenau, C., Guthe, M.

“… on unrealistic assumptions, or have extremely long computation times, making them unusable on real‐world data. We present a novel massively parallelized method to compute globally consistent oriented point normals for raw and unsorted point clouds…”
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BlasPart: A Deterministic Parallel Partitioner for Balanced Large-Scale Hypergraph Partitioning by Tong, Shengbo, Pei, Chunyan, Yu, Wenjian

“… In this paper, we propose BlasPart, a deterministic parallel algorithm for balanced large-scale hypergraph partitioning…”
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Scaling betweenness centrality using communication-efficient sparse matrix multiplication by Solomonik, Edgar, Besta, Maciej, Vella, Flavio, Hoefler, Torsten

“… through it. We propose Maximal Frontier Betweenness Centrality (MFBC): a succinct BC algorithm based on novel sparse matrix multiplication routines that performs a factor…”
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Parallelizing Maximal Clique Enumeration on GPUs by Almasri, Mohammad, Chang, Yen-Hsiang, Hajj, Izzat El, Nagi, Rakesh, Xiong, Jinjun, Hwu, Wen-mei

“…We present a GPU solution for exact maximal clique enumeration (MCE) that performs a search tree traversal following the Bron-Kerbosch algorithm…”
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HybriMoE: Hybrid CPU-GPU Scheduling and Cache Management for Efficient MoE Inference by 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 in computation…”
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ParGNN: A Scalable Graph Neural Network Training Framework on multi-GPUs by Gu, Junyu, Li, Shunde, Cao, Rongqiang, Wang, Jue, Wang, Zijian, Liang, Zhiqiang, Liu, Fang, Li, Shigang, Zhou, Chunbao, Wang, Yangang, Chi, Xuebin

“… over-partition to alleviate load imbalance. Based on the over-partition results, we present a subgraph pipeline algorithm to overlap communication and computation while maintaining the accuracy of GNN training…”
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pSyncPIM: Partially Synchronous Execution of Sparse Matrix Operations for All-Bank PIM Architectures by Baek, Daehyeon, Hwang, Soojin, Huh, Jaehyuk

“… Sparse matrix processing is another critical computation that can significantly benefit from the PIM architecture, but the current all-bank PIM control cannot support diverging executions due to the random sparsity…”
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Gluon-Async: A Bulk-Asynchronous System for Distributed and Heterogeneous Graph Analytics by Dathathri, Roshan, Gill, Gurbinder, Hoang, Loc, Jatala, Vishwesh, Pingali, Keshav, Nandivada, V. Krishna, Dang, Hoang-Vu, Snir, Marc

“…Distributed graph analytics systems for CPUs, like D-Galois and Gemini, and for GPUs, like D-IrGL and Lux, use a bulk-synchronous parallel (BSP…”
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MAD-Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems by Hsia, Samuel, Golden, Alicia, Acun, Bilge, Ardalani, Newsha, DeVito, Zachary, Wei, Gu-Yeon, Brooks, David, Wu, Carole-Jean

“…% of all GPU hours are spent on communication with no overlapping computation. To minimize this outstanding communication latency and other inherent at-scale inefficiencies, we introduce an agile performance modeling framework, MAD-Max…”
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InnerSP: A Memory Efficient Sparse Matrix Multiplication Accelerator with Locality-Aware Inner Product Processing by Baek, Daehyeon, Hwang, Soojin, Heo, Taekyung, Kim, Daehoon, Huh, Jaehyuk

“… Such an unpredictable increase in memory requirement during computation can limit the applicability of accelerators…”
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NDFT: Accelerating Density Functional Theory Calculations via Hardware/Software Co-Design on Near-Data Computing System by 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 systems…”
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Seer: Predictive Runtime Kernel Selection for Irregular Problems by Swann, Ryan, Osama, Muhammad, Sangaiah, Karthik, Mahmud, Jalal

“…Modern GPUs are designed for regular problems and suffer from load imbalance when processing irregular data. Prior to our work, a domain expert selects the…”
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SFLU: Synchronization-Free Sparse LU Factorization for Fast Circuit Simulation on GPUs by Zhao, Jianqi, Wen, Yao, Luo, Yuchen, Jin, Zhou, Liu, Weifeng, Zhou, Zhenya

“… GPUs.We in this paper propose a synchronization-free sparse LU factorization algorithm called SFLU…”
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DS-GL: Advancing Graph Learning via Harnessing Nature's Power within Scalable Dynamical Systems by Song, Ruibing, Wu, Chunshu, Liu, Chuan, Li, Ang, Huang, Michael, Geng, Tony Tong

“… problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL…”
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Leveraging Difference Recurrence Relations for High-Performance GPU Genome Alignment by Zeni, Alberto, Onken, Seth, Santambrogio, Marco Domenico, Samadi, Mehrzad

“… while decreasing the associated cost, emphasizing the need for fast and accurate software to perform sequence analysis, given the quadratic complexity of exact pairwise algorithms…”
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StocHD: Stochastic Hyperdimensional System for Efficient and Robust Learning from Raw Data by Poduval, Prathyush, Zou, Zhuowen, Najafi, Hassan, Homayoun, Houman, Imani, Mohsen

“…Hyperdimensional Computing (HDC) is a neurally-inspired computation model working based on the observation that the human brain operates on high-dimensional representations of data, called hypervector…”
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Max-PIM: Fast and Efficient Max/Min Searching in DRAM by Zhang, Fan, Angizi, Shaahin, Fan, Deliang

“… In this work, for the first time, we propose a novel 'Min/Max-in-memory' algorithm based on iterative XNOR bit-wise comparison, which supports parallel inmemory searching for minimum and maximum…”
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A Scalable and Robust Compilation Framework for Emitter-Photonic Graph State by Ren, Xiangyu, Huang, Yuexun, Liang, Zhiding, Barbalace, Antonio

“…Quantum graph states are critical resources for various quantum algorithms, and also determine essential interconnections in distributed quantum computing…”
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