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Efficient data structures for sparse network representation
2008
International Journal of Computer Mathematics
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In this Article, we present a cache 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. ...
doi:10.1080/00207160701753629
fatcat:gxva4pkgijhibhlh2oyo76p5ca
Prover Efficient Public Verification of Dense or Sparse/Structured Matrix-Vector Multiplication
[chapter]
2017
Lecture Notes in Computer Science
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With the emergence 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 ). ...
doi:10.1007/978-3-319-59870-3_7
fatcat:gckbmnfyafbljgwsxuda4qtlia
Tree-based Space Efficient Formats for Storing the Structure of Sparse Matrices
2014
Scalable Computing : Practice and Experience
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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 ...
doi:10.12694/scpe.v15i1.962
fatcat:rf4md6zoynegvawpqepfwodgjm
HCMB: A stable and efficient algorithm for processing the normalization of highly sparse Hi-C contact data
2021
Computational and Structural Biotechnology Journal
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Especially, the problem 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. ...
doi:10.1016/j.csbj.2021.04.064
pmid:34025950
pmcid:PMC8120939
fatcat:gnooygebobfrlnvtyqxono2fdq
Dynamic Sparse Tensor Algebra Compilation
[article]
2021
arXiv
pre-print
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the results 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 ...
arXiv:2112.01394v1
fatcat:blwlfyandbdajmrql4r6cabkmy
Computation on Sparse Neural Networks: an Inspiration for Future Hardware
[article]
2020
arXiv
pre-print
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We observe that the search for the 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 ...
arXiv:2004.11946v1
fatcat:2lnbtmi4grb65nxcxab4kz6pvy
Distributed Sparse Matrices for Very High Level Languages
[chapter]
2008
Advances in Computers
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Parallel 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. ...
doi:10.1016/s0065-2458(08)00005-3
fatcat:hienrbxdu5hdjbnadvnulvl7ku
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
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We develop 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 ...
doi:10.1145/2159430.2159436
dblp:conf/asplos/GodwinHS12
fatcat:snpuv57vajekxd7twvi3jt7nua
Comparison of Vector Operations of Open-Source Linear Optimization Kernels
2018
Acta Polytechnica Hungarica
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Finally a 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 ...
doi:10.12700/aph.15.1.2018.1.4
fatcat:gqalnlhleja4bpgg2svuresj2m
Efficient quantum circuits for arbitrary sparse unitaries
2009
Physical Review A. Atomic, Molecular, and Optical Physics
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One can formulate a model 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. ...
doi:10.1103/physreva.80.062301
fatcat:3lg2yjwg2rdilh4n4gnbeq56ee
Technical Note: Improving the computational efficiency of sparse matrix multiplication in linear atmospheric inverse problems
2016
Geoscientific Model Development Discussions
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Matrix multiplication 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. ...
doi:10.5194/gmd-2016-204
fatcat:u5qutmnqabbyrfbtk5dfol27se
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
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We evaluate the performance 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 ...
arXiv:1411.2377v1
fatcat:mld2ljfc4ndjdf5rupyycd4l2i
Exploiting sparse Markovandcovariance structure in multiresolution models
2009
Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09
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We propose a new class 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. ...
doi:10.1145/1553374.1553397
dblp:conf/icml/ChoiCW09
fatcat:2hsm2ss3gbhuvpyexvlzyh5q6q
Reduce the rank calculation of a high-dimensional sparse matrix based on network controllability theory
[article]
2022
arXiv
pre-print
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Our method offers an 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. ...
arXiv:2110.13146v2
fatcat:f2taidotgzaifbsuhuuq3rnaq4
Performance Optimization for Sparse AtAx in Parallel on Multicore CPU
2014
IEICE transactions on information and systems
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The 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. ...
doi:10.1587/transinf.e97.d.315
fatcat:bex7ierbfvfyrjedfi2dfwwbye
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