What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Parallel computing
The scientist’s investigation covers issues in Parallel computing, Linear algebra, Algorithm, CUDA and Multi-core processor.
His Parallel computing research is multidisciplinary, incorporating perspectives in Scheduling, Dynamic priority scheduling, Computation and Graphics.
Enrique S. Quintana-Ortí interconnects Programming language, Numerical linear algebra and LU decomposition in the investigation of issues within Linear algebra.
His Algorithm study integrates concerns from other disciplines, such as Truncation, Matrix and Implementation.
His CUDA research incorporates elements of Virtualization and Coprocessor.
His Multi-core processor research is multidisciplinary, incorporating elements of Software portability, Xeon and Programming paradigm.
His most cited work include:
- rCUDA: Reducing the number of GPU-based accelerators in high performance clusters (197 citations)
- An Extension of the StarSs Programming Model for Platforms with Multiple GPUs (135 citations)
- iMODS: internal coordinates normal mode analysis server (133 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Parallel computing, Multi-core processor, Linear system, Linear algebra and Matrix.
Enrique S. Quintana-Ortí mostly deals with Supercomputer in his studies of Parallel computing.
His biological study deals with issues like CUDA, which deal with fields such as Coprocessor.
His Multi-core processor research includes themes of Scalability, Task parallelism, Energy consumption, Scheduling and Efficient energy use.
His research in Linear algebra intersects with topics in Sparse matrix, Cholesky decomposition and Implementation.
His study looks at the intersection of Matrix and topics like Algorithm with Factorization.
He most often published in these fields:
- Parallel computing (54.50%)
- Multi-core processor (25.75%)
- Linear system (22.00%)
What were the highlights of his more recent work (between 2016-2021)?
- Parallel computing (54.50%)
- Multi-core processor (25.75%)
- Linear system (22.00%)
In recent papers he was focusing on the following fields of study:
Enrique S. Quintana-Ortí mostly deals with Parallel computing, Multi-core processor, Linear system, Matrix and Linear algebra.
His research integrates issues of Factorization, Sparse matrix, LU decomposition, Graphics and Solver in his study of Parallel computing.
His work deals with themes such as Multithreading, Leverage, Scheduling, Efficient energy use and Kernel, which intersect with Multi-core processor.
Enrique S. Quintana-Ortí has included themes like Iterative method, Scaling and Conjugate gradient method in his Linear system study.
The concepts of his Matrix study are interwoven with issues in Algorithm, Reduction and Graphics processing unit.
His work carried out in the field of Linear algebra brings together such families of science as Artificial neural network, Voltage frequency scaling, Supercomputer and Xeon.
Between 2016 and 2021, his most popular works were:
- Adaptive Precision in Block-Jacobi Preconditioning for Iterative Sparse Linear System Solvers (27 citations)
- Batched Gauss-Jordan Elimination for Block-Jacobi Preconditioner Generation on GPUs (13 citations)
- A Case for Malleable Thread-Level Linear Algebra Libraries: The LU Factorization With Partial Pivoting (8 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Parallel computing
- Programming language
His primary areas of investigation include Parallel computing, Linear system, Linear algebra, LU decomposition and Matrix.
A large part of his Parallel computing studies is devoted to Multi-core processor.
His Linear system study combines topics in areas such as Positive-definite matrix and Floating point.
The study incorporates disciplines such as Factorization, Supercomputer and Significand in addition to Linear algebra.
His LU decomposition study incorporates themes from Iterative method, Algorithm, Pivot element and Theoretical computer science.
His study in the field of Matrix decomposition also crosses realms of Focus.
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