Suchergebnisse - Theory of computation → Parallel algorithms

MoMaS: Mold Manifold Simulation for real‐time procedural texturing von Maggioli, F., Marin, R., Melzi, S., Rodolà, E.

“… The slime mold algorithm has recently been under the spotlight thanks to its compelling properties studied across many disciplines like biology, computation theory, and artificial intelligence …”
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InnerSP: A Memory Efficient Sparse Matrix Multiplication Accelerator with Locality-Aware Inner Product Processing von 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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pSyncPIM: Partially Synchronous Execution of Sparse Matrix Operations for All-Bank PIM Architectures von 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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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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Seer: Predictive Runtime Kernel Selection for Irregular Problems von 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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Max-PIM: Fast and Efficient Max/Min Searching in DRAM von 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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BlasPart: A Deterministic Parallel Partitioner for Balanced Large-Scale Hypergraph Partitioning von 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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Scope States (Artifact) von van Antwerpen, Hendrik, Visser, Eelco

“… Compilers that can type check compilation units in parallel can make more efficient use of multi-core architectures, which are nowadays widespread …”
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ParGNN: A Scalable Graph Neural Network Training Framework on multi-GPUs von 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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Parallelizing Maximal Clique Enumeration on GPUs von 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 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 in computation …”
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Gluon-Async: A Bulk-Asynchronous System for Distributed and Heterogeneous Graph Analytics von 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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DS-GL: Advancing Graph Learning via Harnessing Nature's Power within Scalable Dynamical Systems von 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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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 systems …”
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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

“… % 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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Asynchronous Distributed-Memory Parallel Algorithms for Influence Maximization von Singhal, Shubhendra Pal, Hati, Souvadra, Young, Jeffrey, Sarkar, Vivek, Hayashi, Akihiro, Vuduc, Richard

“… We propose distributed-memory parallel algorithms for the two main kernels of a state-of-the-art implementation of one IM algorithm, influence maximization via martingales (IMM …”
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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

“… GPUs.We in this paper propose a synchronization-free sparse LU factorization algorithm called SFLU …”
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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

“… requirements.In this paper, we introduce DenSparSA, a balanced systolic array centralized architecture that can execute sparse matrix computations with minimal overhead to original dense matrix computations …”
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Leveraging Difference Recurrence Relations for High-Performance GPU Genome Alignment von 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 von 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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