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Efficient data structures for sparse network representation

2008
*
International Journal of Computer Mathematics
*

In this Article, we present a cache

doi:10.1080/00207160701753629
fatcat:gxva4pkgijhibhlh2oyo76p5ca
*efficient*data*structure*, a variant*of*a linear probing hash table, for representing edge sets*of*such networks. ... Modern-day*computers*are characterized by a striking contrast between the processing power*of*the CPU and the latency*of*main memory accesses. ... The library will be in the form*of*an extension to a scripting language, the basic*structures*and algorithms implemented in a low-level language for performance. ...##
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Prover Efficient Public Verification of Dense or Sparse/Structured Matrix-Vector Multiplication
[chapter]

2017
*
Lecture Notes in Computer Science
*

With the emergence

doi:10.1007/978-3-319-59870-3_7
fatcat:gckbmnfyafbljgwsxuda4qtlia
*of*cloud*computing*services,*computationally*weak devices (Clients) can delegate expensive tasks to more powerful entities (Servers). ... The obtained algorithms are essentially optimal in the amortized model: the overhead for the Server is limited to a very small constant factor, even in the*sparse*or*structured*matrix case; and the*computational*... exist an*efficient*algorithm to*compute*e(g 1 , g 2 ). ...##
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Tree-based Space Efficient Formats for Storing the Structure of Sparse Matrices

2014
*
Scalable Computing : Practice and Experience
*

*Sparse*storage formats describe a way how

*sparse*matrices are stored in a

*computer*memory. ... Extensive research has been conducted about these formats in the context

*of*performance optimization

*of*the

*sparse*matrix-vector multiplication algorithms, but memory

*efficient*formats for storing

*sparse*... Tree-based Space

*Efficient*Formats for Storing the

*Structure*

*of*

*Sparse*MatricesSo, if matrix A is stored in the MBT format, 20 bits are needed for representing its

*structure*. 7 Algorithm 1 Procedure ...

##
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HCMB: A stable and efficient algorithm for processing the normalization of highly sparse Hi-C contact data

2021
*
Computational and Structural Biotechnology Journal
*

Especially, the problem

doi:10.1016/j.csbj.2021.04.064
pmid:34025950
pmcid:PMC8120939
fatcat:gnooygebobfrlnvtyqxono2fdq
*of*high sparsity puts forward a huge challenge on the correction, indicating the urgent need for a stable and*efficient*method for Hi-C data normalization. ... Normalization is a critical pre-processing step*of*downstream analyses for the elimination*of*systematic and technical biases from chromatin contact matrices due to different mappability, GC content, and ... In summary, the HCMB algorithm achieves comparable*computational**efficiency*in matrix balancing as well as the KR method. ...##
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Dynamic Sparse Tensor Algebra Compilation
[article]

2021
*
arXiv
*
pre-print

the results

arXiv:2112.01394v1
fatcat:blwlfyandbdajmrql4r6cabkmy
*of**sparse*tensor algebra*computations*in dynamic data*structures*. ... This paper shows how to generate*efficient*tensor algebra code that*compute*on dynamic*sparse*tensors, which have sparsity*structures*that evolve over time. ... Department*of*Energy, Office*of*Science, Office*of*Advanced Scientific*Computing*Research under Award Numbers DE-SC0008923 and DE-SC0018121; and DARPA under Awards HR0011-18-3-0007 and HR0011-20-9-0017 ...##
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Computation on Sparse Neural Networks: an Inspiration for Future Hardware
[article]

2020
*
arXiv
*
pre-print

We observe that the search for the

arXiv:2004.11946v1
fatcat:2lnbtmi4grb65nxcxab4kz6pvy
*sparse**structure*can be a general methodology for high-quality model explorations, in addition to a strategy for high-*efficiency*model execution. ... In this paper, we summarize the current status*of*the research on the*computation**of**sparse*neural networks, from the perspective*of*the*sparse*algorithms, the software frameworks, and the hardware accelerations ... The efforts on the*sparse**computation*frameworks and the*sparse*hardware accelerators mainly try to improve the*computation**efficiency*, i.e. reducing the amount*of**computation*from a large and dense model ...##
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Distributed Sparse Matrices for Very High Level Languages
[chapter]

2008
*
Advances in Computers
*

Parallel

doi:10.1016/s0065-2458(08)00005-3
fatcat:hienrbxdu5hdjbnadvnulvl7ku
*computing*is becoming ubiquitous, specifically due to the advent*of*multi-core architectures. ... We describe the design and implementation*of*a*sparse*matrix infrastructure for Star-P, a parallel implementation*of*the Matlab R programming language. ... We conclude that distributed*sparse*matrices provide a powerful set*of*primitives for numerical and combinatorial*computing*. ...##
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High-performance sparse matrix-vector multiplication on GPUs for structured grid computations

2012
*
Proceedings of the 5th Annual Workshop on General Purpose Processing with Graphics Processing Units - GPGPU-5
*

We develop

doi:10.1145/2159430.2159436
dblp:conf/asplos/GodwinHS12
fatcat:snpuv57vajekxd7twvi3jt7nua
*efficient**sparse*matrix-vector multiplication for*structured*grid*computations*on GPU architectures using CUDA [25] . ... In this paper, we address*efficient**sparse*matrix-vector multiplication for matrices arising from*structured*grid problems with high degrees*of*freedom at each grid node. ... This work was supported in part by the National Science Foundation through award 0926688 and by the Department*of*Energy (subcontract to The Ohio State University from RNET Technologies; DOE award DE-SC0002434 ...##
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Comparison of Vector Operations of Open-Source Linear Optimization Kernels

2018
*
Acta Polytechnica Hungarica
*

Finally a

doi:10.12700/aph.15.1.2018.1.4
fatcat:gqalnlhleja4bpgg2svuresj2m
*computational*study is performed comparing the performance*of*vector operations*of*different linear optimization kernels to validate the high*efficiency**of*our kernel. ... An important field*of*optimization is linear optimization which is very widely used. It is also often the hidden*computational*engine behind algorithms*of*other fields*of*optimization. ... Conclusions The linear algebraic kernel*of*the Pannon Optimizer was developed, based on results*of*the performance analysis*of**sparse*data*structures*and with consideration*of**computationally*heavy simplex-specific ...##
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Efficient quantum circuits for arbitrary sparse unitaries

2009
*
Physical Review A. Atomic, Molecular, and Optical Physics
*

One can formulate a model

doi:10.1103/physreva.80.062301
fatcat:3lg2yjwg2rdilh4n4gnbeq56ee
*of**computation*based on the composition*of**sparse*unitaries which includes the quantum Turing machine model, the quantum circuit model, anyonic models, permutational quantum*computation*... However, we show that quantum circuits can*efficiently*implement any unitary provided it has at most polynomially many nonzero entries in any row or column, and these entries are*efficiently**computable*... Conversely, quantum gates are row-*sparse*, column-*sparse*, row-*computable*, and column-*computable*unitaries due to their tensor product*structure*. Thus, the*sparse*unitary model is equivalent to BQP. ...##
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Technical Note: Improving the computational efficiency of sparse matrix multiplication in linear atmospheric inverse problems

2016
*
Geoscientific Model Development Discussions
*

Matrix multiplication

doi:10.5194/gmd-2016-204
fatcat:u5qutmnqabbyrfbtk5dfol27se
*of*two*sparse*matrices is a fundamental operation in linear Bayesian inverse problems for*computing*covariance matrices*of*observations and <i>a posteriori</i> uncertainties. ... Here we present a hybrid-parallel*sparse*-*sparse*matrix multiplication approach that is more*efficient*by a third in terms*of*execution time and operation count relative to standard*sparse*matrix multiplication ... Introduction*Sparse*-*Sparse*(SS) matrix multiplication forms the*computational*backbone*of*scientific*computation*in many fields. ...##
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A Highly Efficient Implementation of Multiple Precision Sparse Matrix-Vector Multiplication and Its Application to Product-type Krylov Subspace Methods
[article]

2014
*
arXiv
*
pre-print

We evaluate the performance

arXiv:1411.2377v1
fatcat:mld2ljfc4ndjdf5rupyycd4l2i
*of*the Krylov subspace method by using highly*efficient*multiple precision*sparse*matrix-vector multiplication (SpMV). ...*sparse*matrix collections in a memory-restricted*computing*environment. ... The results*of*the numerical experiments show that preconditioning in multiple precision*computation*is not*efficient*due to the effect*of*the matrix*structure*and other such factors, if it performs better ...##
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Exploiting sparse Markovandcovariance structure in multiresolution models

2009
*
Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09
*

We propose a new class

doi:10.1145/1553374.1553397
dblp:conf/icml/ChoiCW09
fatcat:2hsm2ss3gbhuvpyexvlzyh5q6q
*of*Gaussian MR models that capture the residual correlations within each scale using*sparse*covariance*structure*. ... This model leads to an*efficient*, new inference algorithm that is similar to multipole methods in*computational*physics. ... Evaluating the right-hand side only involves multiplications*of*a*sparse*matrix Σ c and a vector, sox new can be*computed**efficiently*. ...##
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Reduce the rank calculation of a high-dimensional sparse matrix based on network controllability theory
[article]

2022
*
arXiv
*
pre-print

Our method offers an

arXiv:2110.13146v2
fatcat:f2taidotgzaifbsuhuuq3rnaq4
*efficient*pathway to quickly estimate the rank*of*the high-dimensional*sparse*matrix when the time cost*of**computing*the rank by SVD is unacceptable. ... Notwithstanding recent advances in the promotion*of*traditional singular value decomposition (SVD), an*efficient*estimation algorithm for the rank*of*a high-dimensional*sparse*matrix is still lacking. ... This work is supported by the National Natural Science Foundation*of*China (Grant Nos. 61703136 and 61672206), the Natural Science Foundation*of*Hebei (Grant Nos. ...##
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Performance Optimization for Sparse AtAx in Parallel on Multicore CPU

2014
*
IEICE transactions on information and systems
*

The

doi:10.1587/transinf.e97.d.315
fatcat:bex7ierbfvfyrjedfi2dfwwbye
*sparse*matrix operation, y ← y+A t Ax, where A is a*sparse*matrix and x and y are dense vectors, is a widely used*computing*pattern in High Performance*Computing*(HPC) applications. ... Experiments show that our technique outperforms the Compressed*Sparse*Row (CSR) based solution in POSKI by up to 2.5 fold on over 70%*of*benchmarking matrices. key words:*sparse*A t Ax, compressed*sparse*... [4] introduced the CSB to store a*sparse*matrix to enable*efficient**computations**of*both Ax and A t x. ...
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