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You searched for subject:(SpMV). Showing records 1 – 15 of 15 total matches.

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Colorado State University

1. Augustine, Travis. Identification of regular patterns within sparse data structures.

Degree: MS(M.S.), Computer Science, 2020, Colorado State University

 Sparse matrix-vector multiplication (SpMV) is an essential computation in linear algebra. There is a well-known trade-off between operating on a dense or a sparse structure… (more)

Subjects/Keywords: SpMV

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APA (6th Edition):

Augustine, T. (2020). Identification of regular patterns within sparse data structures. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/208429

Chicago Manual of Style (16th Edition):

Augustine, Travis. “Identification of regular patterns within sparse data structures.” 2020. Masters Thesis, Colorado State University. Accessed April 13, 2021. http://hdl.handle.net/10217/208429.

MLA Handbook (7th Edition):

Augustine, Travis. “Identification of regular patterns within sparse data structures.” 2020. Web. 13 Apr 2021.

Vancouver:

Augustine T. Identification of regular patterns within sparse data structures. [Internet] [Masters thesis]. Colorado State University; 2020. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/10217/208429.

Council of Science Editors:

Augustine T. Identification of regular patterns within sparse data structures. [Masters Thesis]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/208429


Iowa State University

2. Groth, Brandon. Using machine learning to improve dense and sparse matrix multiplication kernels.

Degree: 2019, Iowa State University

 This work is comprised of two different projects in numerical linear algebra. The first project is about using machine learning to speed up dense matrix-matrix… (more)

Subjects/Keywords: BLAS; GEMM; HPC; OpenMP; SpMV; Applied Mathematics; Computer Sciences

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APA (6th Edition):

Groth, B. (2019). Using machine learning to improve dense and sparse matrix multiplication kernels. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/17688

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Groth, Brandon. “Using machine learning to improve dense and sparse matrix multiplication kernels.” 2019. Thesis, Iowa State University. Accessed April 13, 2021. https://lib.dr.iastate.edu/etd/17688.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Groth, Brandon. “Using machine learning to improve dense and sparse matrix multiplication kernels.” 2019. Web. 13 Apr 2021.

Vancouver:

Groth B. Using machine learning to improve dense and sparse matrix multiplication kernels. [Internet] [Thesis]. Iowa State University; 2019. [cited 2021 Apr 13]. Available from: https://lib.dr.iastate.edu/etd/17688.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Groth B. Using machine learning to improve dense and sparse matrix multiplication kernels. [Thesis]. Iowa State University; 2019. Available from: https://lib.dr.iastate.edu/etd/17688

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Illinois – Urbana-Champaign

3. AlMasri, Mohammad. On implementing sparse matrix-vector multiplication on intel platform.

Degree: MS, Electrical & Computer Engr, 2018, University of Illinois – Urbana-Champaign

 Sparse matrix-vector multiplication, SpMV, can be a performance bottle-neck in iterative solvers and algebraic eigenvalue problems. In this thesis, we present our sparse matrix compressed… (more)

Subjects/Keywords: SpMV; SIMD; CCF; CSR; I-e; MKL; OpenMP; Skylake; KNL

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APA (6th Edition):

AlMasri, M. (2018). On implementing sparse matrix-vector multiplication on intel platform. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/101729

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

AlMasri, Mohammad. “On implementing sparse matrix-vector multiplication on intel platform.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed April 13, 2021. http://hdl.handle.net/2142/101729.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

AlMasri, Mohammad. “On implementing sparse matrix-vector multiplication on intel platform.” 2018. Web. 13 Apr 2021.

Vancouver:

AlMasri M. On implementing sparse matrix-vector multiplication on intel platform. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/2142/101729.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

AlMasri M. On implementing sparse matrix-vector multiplication on intel platform. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/101729

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


The Ohio State University

4. Ashari, Arash. Sparse Matrix-Vector Multiplication on GPU.

Degree: PhD, Computer Science and Engineering, 2014, The Ohio State University

 Sparse Matrix-Vector multiplication (SpMV) is one of the key operations in linear algebra. Overcoming thread divergence, load imbalance and un-coalesced and indirect memory access due… (more)

Subjects/Keywords: Computer Engineering; Computer Science; GPU; CUDA; Sparse; SpMV; BRC; ACSR

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APA (6th Edition):

Ashari, A. (2014). Sparse Matrix-Vector Multiplication on GPU. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1417770100

Chicago Manual of Style (16th Edition):

Ashari, Arash. “Sparse Matrix-Vector Multiplication on GPU.” 2014. Doctoral Dissertation, The Ohio State University. Accessed April 13, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1417770100.

MLA Handbook (7th Edition):

Ashari, Arash. “Sparse Matrix-Vector Multiplication on GPU.” 2014. Web. 13 Apr 2021.

Vancouver:

Ashari A. Sparse Matrix-Vector Multiplication on GPU. [Internet] [Doctoral dissertation]. The Ohio State University; 2014. [cited 2021 Apr 13]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1417770100.

Council of Science Editors:

Ashari A. Sparse Matrix-Vector Multiplication on GPU. [Doctoral Dissertation]. The Ohio State University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1417770100


Colorado State University

5. Dinkins, Stephanie. Model for predicting the performance of sparse matrix vector multiply (SpMV) using memory bandwidth requirements and data locality, A.

Degree: MS(M.S.), Computer Science, 2012, Colorado State University

 Sparse matrix vector multiply (SpMV) is an important computation that is used in many scientific and structural engineering applications. Sparse computations like SpMV require the… (more)

Subjects/Keywords: data locality; Manhattan distance; performance model; sparse matrices; sparse matrix vector multiply; SpMV

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APA (6th Edition):

Dinkins, S. (2012). Model for predicting the performance of sparse matrix vector multiply (SpMV) using memory bandwidth requirements and data locality, A. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/65303

Chicago Manual of Style (16th Edition):

Dinkins, Stephanie. “Model for predicting the performance of sparse matrix vector multiply (SpMV) using memory bandwidth requirements and data locality, A.” 2012. Masters Thesis, Colorado State University. Accessed April 13, 2021. http://hdl.handle.net/10217/65303.

MLA Handbook (7th Edition):

Dinkins, Stephanie. “Model for predicting the performance of sparse matrix vector multiply (SpMV) using memory bandwidth requirements and data locality, A.” 2012. Web. 13 Apr 2021.

Vancouver:

Dinkins S. Model for predicting the performance of sparse matrix vector multiply (SpMV) using memory bandwidth requirements and data locality, A. [Internet] [Masters thesis]. Colorado State University; 2012. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/10217/65303.

Council of Science Editors:

Dinkins S. Model for predicting the performance of sparse matrix vector multiply (SpMV) using memory bandwidth requirements and data locality, A. [Masters Thesis]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/65303


University of Illinois – Chicago

6. Maggioni, Marco. Sparse Convex Optimization on GPUs.

Degree: 2016, University of Illinois – Chicago

 Convex optimization is a fundamental mathematical framework used for general problem solving. The computational time taken to optimize problems formulated as Linear Programming, Integer Linear… (more)

Subjects/Keywords: SpMV; GPU; Interior Point Method; Convex Optimization; Linear Programming; Integer Linear Programming; Adaptive; Conjugate Gradient; Cholesky

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APA (6th Edition):

Maggioni, M. (2016). Sparse Convex Optimization on GPUs. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/20173

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Maggioni, Marco. “Sparse Convex Optimization on GPUs.” 2016. Thesis, University of Illinois – Chicago. Accessed April 13, 2021. http://hdl.handle.net/10027/20173.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Maggioni, Marco. “Sparse Convex Optimization on GPUs.” 2016. Web. 13 Apr 2021.

Vancouver:

Maggioni M. Sparse Convex Optimization on GPUs. [Internet] [Thesis]. University of Illinois – Chicago; 2016. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/10027/20173.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Maggioni M. Sparse Convex Optimization on GPUs. [Thesis]. University of Illinois – Chicago; 2016. Available from: http://hdl.handle.net/10027/20173

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Virginia Tech

7. Belgin, Mehmet. Structure-based Optimizations for Sparse Matrix-Vector Multiply.

Degree: PhD, Computer Science, 2010, Virginia Tech

 This dissertation introduces two novel techniques, OSF and PBR, to improve the performance of Sparse Matrix-vector Multiply (SMVM) kernels, which dominate the runtime of iterative… (more)

Subjects/Keywords: Code Generators; Vectorization; Sparse; SpMV; SMVM; Matrix Vector Multiply; PBR; OSF; thread pool; parallel SpMV

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APA (6th Edition):

Belgin, M. (2010). Structure-based Optimizations for Sparse Matrix-Vector Multiply. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/30260

Chicago Manual of Style (16th Edition):

Belgin, Mehmet. “Structure-based Optimizations for Sparse Matrix-Vector Multiply.” 2010. Doctoral Dissertation, Virginia Tech. Accessed April 13, 2021. http://hdl.handle.net/10919/30260.

MLA Handbook (7th Edition):

Belgin, Mehmet. “Structure-based Optimizations for Sparse Matrix-Vector Multiply.” 2010. Web. 13 Apr 2021.

Vancouver:

Belgin M. Structure-based Optimizations for Sparse Matrix-Vector Multiply. [Internet] [Doctoral dissertation]. Virginia Tech; 2010. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/10919/30260.

Council of Science Editors:

Belgin M. Structure-based Optimizations for Sparse Matrix-Vector Multiply. [Doctoral Dissertation]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/30260


Iowa State University

8. Townsend, Kevin Rice. Computing SpMV on FPGAs.

Degree: 2016, Iowa State University

 There are hundreds of papers on accelerating sparse matrix vector multiplication (SpMV), however, only a handful target FPGAs. Some claim that FPGAs inherently perform inferiorly… (more)

Subjects/Keywords: Computer Engineering (Computing and Networking Systems); Computer Engineering; Computing and Networking Systems; FPGA; High Performance Reconfigurable Computing; Sparse Matrix Vector Multiplication; SpMV; Computer Engineering

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APA (6th Edition):

Townsend, K. R. (2016). Computing SpMV on FPGAs. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/15227

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Townsend, Kevin Rice. “Computing SpMV on FPGAs.” 2016. Thesis, Iowa State University. Accessed April 13, 2021. https://lib.dr.iastate.edu/etd/15227.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Townsend, Kevin Rice. “Computing SpMV on FPGAs.” 2016. Web. 13 Apr 2021.

Vancouver:

Townsend KR. Computing SpMV on FPGAs. [Internet] [Thesis]. Iowa State University; 2016. [cited 2021 Apr 13]. Available from: https://lib.dr.iastate.edu/etd/15227.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Townsend KR. Computing SpMV on FPGAs. [Thesis]. Iowa State University; 2016. Available from: https://lib.dr.iastate.edu/etd/15227

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

9. Godwin, Jeswin Samuel. High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations.

Degree: MS, Computer Science and Engineering, 2013, The Ohio State University

 In this thesis, we address efficient sparse matrix-vector multiplication for matrices arising from structured grid problems with high degrees of freedom at each grid node.… (more)

Subjects/Keywords: Computer Engineering; Computer Science; "SPMV; GPU; Structured Grid; Column-Diagonal"

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APA (6th Edition):

Godwin, J. S. (2013). High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1357280824

Chicago Manual of Style (16th Edition):

Godwin, Jeswin Samuel. “High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations.” 2013. Masters Thesis, The Ohio State University. Accessed April 13, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1357280824.

MLA Handbook (7th Edition):

Godwin, Jeswin Samuel. “High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations.” 2013. Web. 13 Apr 2021.

Vancouver:

Godwin JS. High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations. [Internet] [Masters thesis]. The Ohio State University; 2013. [cited 2021 Apr 13]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1357280824.

Council of Science Editors:

Godwin JS. High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations. [Masters Thesis]. The Ohio State University; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1357280824


Indian Institute of Science

10. Ramesh, Chinthala. Hardware-Software Co-Design Accelerators for Sparse BLAS.

Degree: PhD, Engineering, 2019, Indian Institute of Science

 Sparse Basic Linear Algebra Subroutines (Sparse BLAS) is an important library. Sparse BLAS includes three levels of subroutines. Level 1, Level2 and Level 3 Sparse… (more)

Subjects/Keywords: Sparse Matrix Storage Formats; Hardware-Software Codesign Accelerators; Sparse BLAS; Hardware Accelerator; Sawtooth Compressed Row Storage; Sparse Vector Vector Multiplication; Sparse Matrix Matrix Multiplication; Sparse Matrix Vector Multiplication; Compressed Row Storage; Sparse Basic Linear Algebra Subroutines; SpMV Multiplication; SpMM Multiplication; Nano Science and Engineering

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APA (6th Edition):

Ramesh, C. (2019). Hardware-Software Co-Design Accelerators for Sparse BLAS. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/4276

Chicago Manual of Style (16th Edition):

Ramesh, Chinthala. “Hardware-Software Co-Design Accelerators for Sparse BLAS.” 2019. Doctoral Dissertation, Indian Institute of Science. Accessed April 13, 2021. http://etd.iisc.ac.in/handle/2005/4276.

MLA Handbook (7th Edition):

Ramesh, Chinthala. “Hardware-Software Co-Design Accelerators for Sparse BLAS.” 2019. Web. 13 Apr 2021.

Vancouver:

Ramesh C. Hardware-Software Co-Design Accelerators for Sparse BLAS. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2019. [cited 2021 Apr 13]. Available from: http://etd.iisc.ac.in/handle/2005/4276.

Council of Science Editors:

Ramesh C. Hardware-Software Co-Design Accelerators for Sparse BLAS. [Doctoral Dissertation]. Indian Institute of Science; 2019. Available from: http://etd.iisc.ac.in/handle/2005/4276

11. Karakasis, Vasileios. Βελτιστοποίηση του υπολογιστικού πυρήνα πολλαπλασιασμού αραιού πίνακα με διάνυσμα σε σύγχρονες πολυπύρηνες αρχιτεκτονικές υπολογιστών.

Degree: 2012, National Technical University of Athens (NTUA); Εθνικό Μετσόβιο Πολυτεχνείο (ΕΜΠ)

This thesis focuses on the optimization of the Sparse Matrix-Vector Multiplication kernel (SpMV) for modern multicore architectures. We perform an in-depth performance analysis of the… (more)

Subjects/Keywords: Υπολογιστικά συστήματα υψηλών επιδόσεων; Επιστημονικές εφαρμογές; Πολλαπλασιασμός αραιού πίνακα με διάνυσμα; Πολυπύρηνες αρχιτεκτονικές; Συμπίεση δεδομένων; Ενεργειακή απόδοση; High performance computing; Scientific applications; Sparse matrix-vector multiplication; Multicore; Data compression; Energy-efficiency; SpMV; CSX; HPC

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APA (6th Edition):

Karakasis, V. (2012). Βελτιστοποίηση του υπολογιστικού πυρήνα πολλαπλασιασμού αραιού πίνακα με διάνυσμα σε σύγχρονες πολυπύρηνες αρχιτεκτονικές υπολογιστών. (Thesis). National Technical University of Athens (NTUA); Εθνικό Μετσόβιο Πολυτεχνείο (ΕΜΠ). Retrieved from http://hdl.handle.net/10442/hedi/34819

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Karakasis, Vasileios. “Βελτιστοποίηση του υπολογιστικού πυρήνα πολλαπλασιασμού αραιού πίνακα με διάνυσμα σε σύγχρονες πολυπύρηνες αρχιτεκτονικές υπολογιστών.” 2012. Thesis, National Technical University of Athens (NTUA); Εθνικό Μετσόβιο Πολυτεχνείο (ΕΜΠ). Accessed April 13, 2021. http://hdl.handle.net/10442/hedi/34819.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Karakasis, Vasileios. “Βελτιστοποίηση του υπολογιστικού πυρήνα πολλαπλασιασμού αραιού πίνακα με διάνυσμα σε σύγχρονες πολυπύρηνες αρχιτεκτονικές υπολογιστών.” 2012. Web. 13 Apr 2021.

Vancouver:

Karakasis V. Βελτιστοποίηση του υπολογιστικού πυρήνα πολλαπλασιασμού αραιού πίνακα με διάνυσμα σε σύγχρονες πολυπύρηνες αρχιτεκτονικές υπολογιστών. [Internet] [Thesis]. National Technical University of Athens (NTUA); Εθνικό Μετσόβιο Πολυτεχνείο (ΕΜΠ); 2012. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/10442/hedi/34819.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Karakasis V. Βελτιστοποίηση του υπολογιστικού πυρήνα πολλαπλασιασμού αραιού πίνακα με διάνυσμα σε σύγχρονες πολυπύρηνες αρχιτεκτονικές υπολογιστών. [Thesis]. National Technical University of Athens (NTUA); Εθνικό Μετσόβιο Πολυτεχνείο (ΕΜΠ); 2012. Available from: http://hdl.handle.net/10442/hedi/34819

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

12. Sedaghati Mokhtari, Naseraddin. Performance Optimization of Memory-Bound Programs on Data Parallel Accelerators.

Degree: PhD, Computer Science and Engineering, 2016, The Ohio State University

 High performance applications depend on high utilization of memory bandwidth and computing resources, and data parallel accelerators have proven to be very effective in providing… (more)

Subjects/Keywords: Computer Science; Computer Engineering; Engineering; Stencil Computation, GPU, CUDA, SpMV, Graph Processing, Performance Analysis, SIMD

…Conclusion . . . . . . . . . . . . . . . . . SpMV Representations on GPUs . . . . . Sparse Matices… …and Features . . . . . . . 4.2.1 Feature Analysis . . . . . . . . . . SpMV Performance… …multiplication (SpMV). Efficient execution of SpMV on modern data parallel accelerators (… …ratio. Such properties make GPU-specific optimizations for high-performance SpMV very… …domains and sparsity features), Chapter 4 evaluates the SpMV kernel performance of each of… 

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APA (6th Edition):

Sedaghati Mokhtari, N. (2016). Performance Optimization of Memory-Bound Programs on Data Parallel Accelerators. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1452255686

Chicago Manual of Style (16th Edition):

Sedaghati Mokhtari, Naseraddin. “Performance Optimization of Memory-Bound Programs on Data Parallel Accelerators.” 2016. Doctoral Dissertation, The Ohio State University. Accessed April 13, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1452255686.

MLA Handbook (7th Edition):

Sedaghati Mokhtari, Naseraddin. “Performance Optimization of Memory-Bound Programs on Data Parallel Accelerators.” 2016. Web. 13 Apr 2021.

Vancouver:

Sedaghati Mokhtari N. Performance Optimization of Memory-Bound Programs on Data Parallel Accelerators. [Internet] [Doctoral dissertation]. The Ohio State University; 2016. [cited 2021 Apr 13]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1452255686.

Council of Science Editors:

Sedaghati Mokhtari N. Performance Optimization of Memory-Bound Programs on Data Parallel Accelerators. [Doctoral Dissertation]. The Ohio State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1452255686

13. Boyer, Brice. Multiplication matricielle efficace et conception logicielle pour la bibliothèque de calcul exact LinBox : Efficient matrix multiplication and design for the exact linear algebra library LinBox.

Degree: Docteur es, Mathématiques, 2012, Université de Grenoble

Dans ce mémoire de thèse, nous développons d'abord des multiplications matricielles efficaces. Nous créons de nouveaux ordonnancements qui permettent de réduire la taille de la… (more)

Subjects/Keywords: Algèbre linéaire exacte; Bibliothèque mathématique générique; Multiplication matricielle dense/SpMV; Matrice dense/creuse; Ordonnancements/jeu de galet; Patrons de conception; Exact linear algebra; Generic mathematic library; Dense matrix multiplication/SpMV; Sparse/dense matrix; Schedulings/pebble games; Design patterns

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APA (6th Edition):

Boyer, B. (2012). Multiplication matricielle efficace et conception logicielle pour la bibliothèque de calcul exact LinBox : Efficient matrix multiplication and design for the exact linear algebra library LinBox. (Doctoral Dissertation). Université de Grenoble. Retrieved from http://www.theses.fr/2012GRENM019

Chicago Manual of Style (16th Edition):

Boyer, Brice. “Multiplication matricielle efficace et conception logicielle pour la bibliothèque de calcul exact LinBox : Efficient matrix multiplication and design for the exact linear algebra library LinBox.” 2012. Doctoral Dissertation, Université de Grenoble. Accessed April 13, 2021. http://www.theses.fr/2012GRENM019.

MLA Handbook (7th Edition):

Boyer, Brice. “Multiplication matricielle efficace et conception logicielle pour la bibliothèque de calcul exact LinBox : Efficient matrix multiplication and design for the exact linear algebra library LinBox.” 2012. Web. 13 Apr 2021.

Vancouver:

Boyer B. Multiplication matricielle efficace et conception logicielle pour la bibliothèque de calcul exact LinBox : Efficient matrix multiplication and design for the exact linear algebra library LinBox. [Internet] [Doctoral dissertation]. Université de Grenoble; 2012. [cited 2021 Apr 13]. Available from: http://www.theses.fr/2012GRENM019.

Council of Science Editors:

Boyer B. Multiplication matricielle efficace et conception logicielle pour la bibliothèque de calcul exact LinBox : Efficient matrix multiplication and design for the exact linear algebra library LinBox. [Doctoral Dissertation]. Université de Grenoble; 2012. Available from: http://www.theses.fr/2012GRENM019

14. Hong, Changwan. Code Optimization on GPUs.

Degree: PhD, Computer Science and Engineering, 2019, The Ohio State University

 Graphic Processing Units (GPUs) have become popular in the last decade due to their high memory bandwidth and powerful computing capacity. Nevertheless, achieving high-performance on… (more)

Subjects/Keywords: Computer Science; GPU; performance; modeling; optimization; SpMV; SpMM; SDDMM; sparse matrix; graph processing; tiling; multicore; manycore; matrix multiplication; tensor; stencil; SIMD; data locality; CSR; parallel; load balance; shared memory; graph analytics

…82 4.1 cuSPARSE SpMV/SpMM performance and upper-bound: NVIDIA Pascal P100 GPU… …115 4.12 Performance profiles: RS-SpMM and Loop-over-SpMV; single and double; K=8,32,128,512… …applications. SpMV requires a vector to be multiplied by a sparse matrix. SpMM is a generalization of… …SpMV, and requires multiple vectors to be multiplied by a sparse matrix. While repeated… …applications of SpMV can be used to perform SpMM, better data reuse can be achieved by devising… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hong, C. (2019). Code Optimization on GPUs. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1557123832601533

Chicago Manual of Style (16th Edition):

Hong, Changwan. “Code Optimization on GPUs.” 2019. Doctoral Dissertation, The Ohio State University. Accessed April 13, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1557123832601533.

MLA Handbook (7th Edition):

Hong, Changwan. “Code Optimization on GPUs.” 2019. Web. 13 Apr 2021.

Vancouver:

Hong C. Code Optimization on GPUs. [Internet] [Doctoral dissertation]. The Ohio State University; 2019. [cited 2021 Apr 13]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1557123832601533.

Council of Science Editors:

Hong C. Code Optimization on GPUs. [Doctoral Dissertation]. The Ohio State University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1557123832601533

15. Ross, Christine Anne Haines. Accelerating induction machine finite-element simulation with parallel processing.

Degree: MS, Electrical & Computer Engineering, 2015, University of Illinois – Urbana-Champaign

 Finite element analysis used for detailed electromagnetic analysis and design of electric machines is computationally intensive. A means of accelerating two-dimensional transient finite element analysis,… (more)

Subjects/Keywords: finite element; simulation; finite; element; MATLAB; Graphics Processing Unit (GPU); parallel; parallel; processing; linear; nonlinear; transient; eddy current; eddy; induction; Machine; induction machine; electrical machine; speedup; electromagnetic; Compute Unified Device Architecture (CUDA); sparse matrix-vector multiplication; Sparse Matrix-vector Multiply (SpMV); Krylov; iterative solver; Finite Element Method (FEM); Finite Element Analysis (FEA); Galerkin

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ross, C. A. H. (2015). Accelerating induction machine finite-element simulation with parallel processing. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/88070

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ross, Christine Anne Haines. “Accelerating induction machine finite-element simulation with parallel processing.” 2015. Thesis, University of Illinois – Urbana-Champaign. Accessed April 13, 2021. http://hdl.handle.net/2142/88070.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ross, Christine Anne Haines. “Accelerating induction machine finite-element simulation with parallel processing.” 2015. Web. 13 Apr 2021.

Vancouver:

Ross CAH. Accelerating induction machine finite-element simulation with parallel processing. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/2142/88070.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ross CAH. Accelerating induction machine finite-element simulation with parallel processing. [Thesis]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/88070

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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