TSQR
Where professionals start their day. Digital townsquare for evolving industries. Cut through noise with curated content from news, media, research and social sources. TSQR is a modular, aggregation, curation and data insights platform so you can whitelabel your own digital townsquare whether its for a niche industry or new age intranet for staff. We presently curate 1000s of publications, research articles, youtube channels and twitter posts with behind the scenes expert and machine scoring and filtering. The goal is to curate the most relevant and high quality content so that professionals can have a better, faster and more focused way to stay on top of the latest developments and specific use cases of interest.
TSQR
Industry:
Artificial Intelligence (AI) Augmented Reality Bitcoin Blockchain Cannabis ESports News Social Media Virtual Currency Virtual Reality
Founded:
2018-07-01
Address:
Los Angeles, California, United States
Country:
United States
Total Employee:
1+
Status:
Active
Email Addresses:
[email protected]
Total Funding:
200 K USD
Technology used in webpage:
SPF AJAX Libraries API Google Apps For Business GoDaddy DNS GoDaddy Browse Happy Starfield Technologies
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Founder
More informations about "TSQR"
GitHub - enp1s0/tsqr-gpu: Implementation of TSQR, โฆ
TSQR is an efficient QR factorization algorithm for tall-skinny matrices in parallel computing environment. Publications "TSQR on TensorCores", Hiroyuki Ootomo and Rio Yokota, SC'19 Research Poster (Best Poster Candidate) [ File ]See details»
Reconstructing Householder Vectors from Tall-Skinny QR
The Tall-Skinny QR (TSQR) algorithm is more communication efficient than the standard Householder algorithm for QR decomposition of matrices with many more rows than columns. โฆSee details»
TSQR on TensorCores - SC19
TSQR on TensorCores Hiroyuki Ootomo1, Rio Yokota2 1School of Computing, TokyoTech 2Global Scienti c Information and Computing Center, TokyoTech Abstract Tall-Skinny QR โฆSee details»
HSL_MP82: Tall-skinny QR factorization - UKRI
May 3, 2023 (1) The TSQR method is unconditionally accurate produces a matrix \(Q\) that can be either formed explicitly (orthonormal) or can be stored implicitly via a set of Householder โฆSee details»
Communication-avoiding parallel and sequential QR factorizations
Our ๏ฌrst algorithm, Tall Skinny QR (TSQR), factors m × n matrices in a one-dimensional (1-D) block cyclic row layout, storing the Q factor (if desired) implicitly as a tree of blocks of โฆSee details»
CholeskyQR2: A Simple and Communication-Avoiding Algorithm โฆ
Jan 26, 2015 Designing communication-avoiding algorithms is crucial for high performance computing on a large-scale parallel system. The TSQR algorithm is a communication-avoiding โฆSee details»
(PDF) TSQR on Tensor Cores - ResearchGate
Nov 29, 2022 PDF | Tall-Skinny QR (TSQR) is an efficient algorithm for calculating the QR decomposition of m × n matrices where m โซ n, which is done by recursively... | Find, read and โฆSee details»
ENHANCING PERFORMANCE OF TALL-SKINNY QR โฆ
This 2012 TSQR Virtex-6 TS 62 36 % Paper SX475T 4. PROPOSED ARCHITECTURE 4.1. Parallelism 4.1.1. Coarse-Grain Parallelism in TSQR In TSQR, tiles in the local QR stage and โฆSee details»
Distributed Parallel Tall-Skinny QR Factorization: โฆ
Apr 8, 2023 Under this direction, the TSQR algorithm was proposed for tall-skinny QR factorization. In addition, Cholesky QR type algorithms have attracted much attention, and several progresses were reported [7, 8, 15, 17, 18]. As a โฆSee details»
[0806.2159] Communication-optimal parallel and sequential QR โฆ
Jun 12, 2008 We present parallel and sequential dense QR factorization algorithms that are both optimal (up to polylogarithmic factors) in the amount of communication they perform, and just โฆSee details»
Tall and skinny QR factorizations in MapReduce architectures
A TSQR factorization and implement that algorithm on a MapReduce system. This algorithm is a key ingredient in solving least-squares problems. Consider a full-rank, least โฆSee details»
Enhancing performance of Tall-Skinny QR factorization using FPGAs
Aug 31, 2012 Communication-avoiding linear algebra algorithms with low communication latency and high memory bandwidth requirements like Tall-Skinny QR factorization (TSQR) are highly โฆSee details»
hpacella/TSQR - GitHub
The tall, skinny QR (TSQR) decomposition of a matrix A developed by Demmel et al. [1] is a communication-optimal QR decomposition for matrices with many more rows than columns. โฆSee details»
Distributed Parallel Tall-Skinny QR Factorization: Performance ...
Under this direction, the TSQR algorithm was proposed [3] for tall-skinny QR factorization. In addition, Cholesky QR type algorithms have attracted much attention, and several progresses โฆSee details»
[1301.1071] Direct QR factorizations for tall-and-skinny matrices in ...
Jan 6, 2013 The QR factorization and the SVD are two fundamental matrix decompositions with applications throughout scientific computing and data analysis. For matrices with many more โฆSee details»
Dataflow Systolic Array Implementations of Exploring Dual โฆ
Tall and skinny QR (TSQR) decomposition is an essential matrix operation with various applications in edge computing, including data compression, subspace projection, and โฆSee details»
Reconstructing Householder vectors from Tall-Skinny QR
Nov 1, 2015 The Tall-Skinny QR (TSQR) algorithm is more communication efficient than the standard Householder algorithm for QR decomposition of matrices with many more rows than โฆSee details»
A Communication-Avoiding, Hybrid-Parallel, Rank-Revealing ...
Furthermore, in block orthogonalizations, TSQR is faster and more accurate than existing approaches for orthogonalizing the vectors within each block ("normalization"). TSQR's rank โฆSee details»
Communication-optimal parallel and sequential QR and LU โฆ
Both TSQR and CAQR have asymptotically lower latency cost in the parallel case, and asymptotically lower latency and bandwidth costs in the sequential case. In practice, we have โฆSee details»
Direct QR factorizations for tall-and-skinny matrices in MapReduce ...
The QR factorization and the SVD are two fundamental matrix decompositions with applications throughout scientific computing and data analysis. For matrices with many more rows than โฆSee details»