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Algorithms for Matrix Multiplication via Sampling and Opportunistic Matrix Multiplication
As proposed by Karppa and Kaski (in: Proceedings 30th ACM-SIAM Symposium on Discrete Algorithms (SODA), 2019) a novel “broken" or "opportunistic"...
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A fast scalable distributed kriging algorithm using Spark framework
Environmental and climate models used for weather prediction require evenly spaced meteorological datasets at a very high spatial and temporal...
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HPMaX: heterogeneous parallel matrix multiplication using CPUs and GPUs
We present a novel heterogeneous parallel matrix multiplication algorithm that utilizes both central processing units (CPUs) and graphics processing...
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Faster Combinatorial k-Clique Algorithms
Detecting if a graph contains a k-Clique is one of the most fundamental problems in computer science. The asymptotically fastest algorithm runs in...
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A Normal Form for Matrix Multiplication Schemes
Schemes for exact multiplication of small matrices have a large symmetry group. This group defines an equivalence relation on the set of...
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Privacy-Preserving Clustering for Multi-dimensional Data Randomization Under LDP
Randomization of multi-dimensional data under local differential privacy is a significant and practical application of big data. Because of the...
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Linear Algebra
Haksun Li, PhDa*
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Improved parallel matrix multiplication using Strassen and Urdhvatiryagbhyam method
The current milieu, encourages rapid growth of wireless communication, multimedia applications, robotics and graphics to have efficient utilization...
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Improving Support-Minors Rank Attacks: Applications to G \(\displaystyle e\) MSS and Rainbow
The Support-Minors (SM) method has opened new routes to attack multivariate schemes with rank properties that were previously impossible to exploit,...
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Towards Optimal Fast Matrix Multiplication on CPU-GPU Platforms
Increasing computing power has become available through the use of GPUs, bringing new opportunities to accelerate fast matrix multiplication using...
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Supercomputer Environment for Recursive Matrix Algorithms
AbstractA new runtime environment for the execution of recursive matrix algorithms on a supercomputer with distributed memory is proposed. It is...
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PARCSIM: a parallel computing simulator for scalable software optimization
PARCSIM is a parallel software simulator that allows a user to capture, through a graphical interface, matrix algorithm schemes that solve scientific...
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Linear Algebra
Linear algebra algorithms are the most important algorithms in numerical computing. They are the foundation of all numerical algorithms. Many...
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Algorithmic Thinking
A problem is specified by rigorously specifying the input and the desired output. An algorithm is a set of rules specifying the sequences of...
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Improved Cryptanalysis of HFERP
In this paper we introduce a new attack on the multivariate encryption scheme HFERP, a big field scheme including an extra variable set, additional...
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Error Estimation and Correction Using the Forward CENA Method
The increasing use of heterogeneous and more energy-efficient computing systems has led to a renewed demand for reduced- or mixed-precision...
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Accelerating approximate matrix multiplication for near-sparse matrices on GPUs
Although the matrix multiplication plays a vital role in computational linear algebra, there are few efficient solutions for matrix multiplication of...
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POAS: a framework for exploiting accelerator level parallelism in heterogeneous environments
In the era of heterogeneous computing, a new paradigm called accelerator level parallelism (ALP) has emerged. In ALP, accelerators are used...
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Power Awareness in Low Precision Neural Networks
Existing approaches for reducing DNN power consumption rely on quite general principles, including avoidance of multiplication operations and...