Search Results - computation on a cluster with distributed memory

Parallel Large Scale High Accuracy Navier-Stokes Computations on Distributed Memory Clusters by Peigin, S., Epstein, B., Rubin, T., Seror, S.

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Generating Coupled Cluster Code for Modern Distributed-Memory Tensor Software by Brandejs, Jan, Pototschnig, Johann, Saue, Trond

“…Using GPU-based HPC platforms efficiently for coupled cluster computations is a challenge due to heterogeneous hardware structures…”
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A massively parallel tensor contraction framework for coupled-cluster computations by Solomonik, Edgar, Matthews, Devin, Hammond, Jeff R., Stanton, John F., Demmel, James

“…Precise calculation of molecular electronic wavefunctions by methods such as coupled-cluster requires the computation of tensor contractions, the cost of which has polynomial computational scaling…”
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Using distributed memory parallel computers and GPU clusters for multidimensional Monte Carlo integration by Szakowski, Dominik, Stpiczyski, Przemysaw

“…SummaryThe aim of this paper is to show that the multidimensional Monte Carlo integration can be efficiently implemented on various distributed memory parallel computers and clusters of multicore…”
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Fast distributed large-pixel-count hologram computation using a GPU cluster by Pan, Yuechao, Xu, Xuewu, Liang, Xinan

“… with 32.5 Tflop/s computing power and implemented distributed hologram computation on it with speed improvement techniques, such as shared memory on GPU, GPU level adaptive load balancing, and node level load distribution…”
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Parallel Computation of Component Trees on Distributed Memory Machines by Gotz, Markus, Cavallaro, Gabriele, Geraud, Thierry, Book, Matthias, Riedel, Morris

“… This work proposes a new efficient hybrid algorithm for the parallel computation of two particular component trees-the max- and min-tree-in shared and distributed memory environments…”
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In-Memory Distributed Matrix Computation Processing and Optimization by Yongyang Yu, Mingjie Tang, Aref, Walid G., Malluhi, Qutaibah M., Abbas, Mostafa M., Ouzzani, Mourad

“… This paper presents new efficient and scalable matrix processing and optimization techniques for in-memory distributed clusters…”
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Automatic Generation of Distributed-Memory Mappings for Tensor Computations by Kong, Martin, Abu-Yosef, Raneem, Rountev, Atanas, Sadayappan, P.

“… We introduce an innovative approach to automatically produce distributed-memory parallel code for an important sub-class of affine tensor computations common to Coupled Cluster (CC…”
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3D DFT by block tensor-matrix multiplication via a modified Cannon's algorithm: Implementation and scaling on distributed-memory clusters with fat tree networks by Malapally, Nitin, Bolnykh, Viacheslav, Suarez, Estela, Carloni, Paolo, Lippert, Thomas, Mandelli, Davide

“… We demonstrate S3DFT's efficient use of hardware resources, and its scaling using up to 16,464 cores of the JUWELS Cluster…”
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Nime: a native in-memory compute framework for cluster computing by Chen, Chao, Wang, Zhenghua, Jiang, Chen, Wang, Zheng

“… In this paper, we present NIME–a native in-memory compute framework for cluster computing–that aims to perform parallel task processing using native executors…”
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MespaConfig: Memory-Sparing Configuration Auto-Tuning for Co-Located In-Memory Cluster Computing Jobs by Zong, Zan, Wen, Lijie, Hu, Xuming, Han, Rui, Qian, Chen, Lin, Li

“…Distributed in-memory computing frameworks usually have lots of parameters (e.g., the buffer size of shuffle…”
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Efficient GPU Computation of Large Protein Solvent-Excluded Surface by PlateauHolleville, Cyprien, Maria, Maxime, Merillou, Stephane, Montes, Matthieu

“… While several methods targeted its computation, the ability to process large molecular structures to address the introduction of big complex analysis while leveraging the massively parallel…”
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A distributed in-memory key-value store system on heterogeneous CPU–GPU cluster by Zhang, Kai, Wang, Kaibo, Yuan, Yuan, Guo, Lei, Li, Rubao, Zhang, Xiaodong, He, Bingsheng, Hu, Jiayu, Hua, Bei

“…In-memory key-value stores play a critical role in many data-intensive applications to provide high-throughput and low latency data accesses…”
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Memristive Computational Memory Using Memristor Overwrite Logic (MOL) by Alhaj Ali, Khaled, Rizk, Mostafa, Baghdadi, Amer, Diguet, Jean-Philippe, Jomaah, Jalal, Onizawa, Naoya, Hanyu, Takahiro

“…), associated with an original MOL-based computational memory. MOL relies on a fully digital representation of memristor and can operate with different memristive device technologies…”
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Fast Computation of Electromagnetic Scattering From a Metal-Dielectric Composite and Randomly Distributed BoRs Cluster by Gu, Jihong, He, Zi, Yin, Hongcheng, Chen, Rushan

“…An efficient equivalence principle algorithm with spherical equivalent source (SEPA) is proposed to analyze the electromagnetic scattering from a metal-dielectric composite and randomly distributed bodies of revolution…”
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Parallel Spectral Clustering in Distributed Systems by Chen, Wen-Yen, Song, Yangqiu, Bai, Hongjie, Lin, Chih-Jen, Chang, Edward Y.

“… We parallelize both memory use and computation on distributed computers. Through an empirical study on a document data set of 193,844 instances and a photo data set of 2,121,863, we show that our parallel algorithm can effectively handle large problems…”
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Distributed Discrete Morse Sandwich: Efficient Computation of Persistence Diagrams for Massive Scalar Data by Le Guillou, Eve, Fortin, P., Tierny, J.

“… In this work, we extend DMS to distributed-memory parallelism for the efficient and scalable computation of persistence diagrams for massive datasets across multiple compute nodes…”
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Scalable distributed data cube computation for large-scale multidimensional data analysis on a Spark cluster by Lee, Suan, Kang, Seok, Kim, Jinho, Yu, Eun Jung

“… However, MapReduce incurs the overhead of disk I/Os and network traffic. To overcome these MapReduce limitations, Spark was recently proposed as a memory-based parallel/distributed processing framework…”
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DSparse: A Distributed Training Method for Edge Clusters Based on Sparse Update by Peng, Xiao-Hui, Sun, Yi-Xuan, Zhang, Zheng-Hui, Wang, Yi-Fan

“… Moreover, existing distributed training architectures often fail to consider the constraints of resources and communication efficiency in edge environments…”
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GraNNDis: Fast Distributed Graph Neural Network Training Framework for Multi-Server Clusters by Song, Jaeyong, Jang, Hongsun, Lim, Hunseong, Jung, Jaewon, Kim, Youngsok, Lee, Jinho

“…) Redundant memory usage and computation hinder the scalability of the distributed frameworks…”
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