Richard Vuduc

What is he best known for?

The fields of study he is best known for:

• Operating system
• Programming language
• Algorithm

His main research concerns Parallel computing, Sparse matrix, Sparse matrix-vector multiplication, Cache and Theoretical computer science. Specifically, his work in Parallel computing is concerned with the study of CUDA. His Sparse matrix study combines topics from a wide range of disciplines, such as Performance tuning, Statistical model, Artificial intelligence, Kernel and Code generation.

His research combines Multi-core processor and Sparse matrix-vector multiplication. His Cache research is multidisciplinary, relying on both Sparse approximation, Kernel and Solver. His studies deal with areas such as Perspective, Algorithm design, Word and Mathematical optimization as well as Theoretical computer science.

His most cited work include:

• OSKI: A Library of Automatically Tuned Sparse Matrix Kernels (431 citations)
• Optimization of sparse matrix-vector multiplication on emerging multicore platforms (350 citations)
• Model-driven autotuning of sparse matrix-vector multiply on GPUs (345 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Parallel computing, Sparse matrix, Algorithm, Scalability and Theoretical computer science. His works in CUDA, Multi-core processor, Distributed memory, Cache and Speedup are all subjects of inquiry into Parallel computing. His study of Sparse matrix-vector multiplication is a part of Sparse matrix.

His Algorithm study incorporates themes from Block and Matrix multiplication. His research in Scalability intersects with topics in Structure, Supercomputer and Set. His study in Theoretical computer science is interdisciplinary in nature, drawing from both Graph and Compiler.

He most often published in these fields:

• Parallel computing (44.90%)
• Sparse matrix (17.01%)
• Algorithm (16.33%)

What were the highlights of his more recent work (between 2016-2021)?

• Parallel computing (44.90%)
• Scalability (16.33%)
• Speedup (8.16%)

In recent papers he was focusing on the following fields of study:

Richard Vuduc spends much of his time researching Parallel computing, Scalability, Speedup, Computation and Algorithm. His research investigates the link between Parallel computing and topics such as Kernel that cross with problems in Symmetric matrix and Kernel. Richard Vuduc interconnects Sparse matrix, Theoretical computer science, Set and Artificial intelligence in the investigation of issues within Scalability.

His Sparse matrix research includes elements of Structure, Machine learning and Spartan. His Computation study also includes

• Implementation together with Computer architecture, Petascale computing and Software portability,
• Computer engineering which connect with Memory hierarchy. His Algorithm research integrates issues from Object, Data structure, LU decomposition and Planar graph.

Between 2016 and 2021, his most popular works were:

• HiCOO: hierarchical storage of sparse tensors (41 citations)
• Model-Driven Sparse CP Decomposition for Higher-Order Tensors (38 citations)
• Autotuning in High-Performance Computing Applications (37 citations)

In his most recent research, the most cited papers focused on:

• Operating system
• Programming language
• Algorithm

Richard Vuduc mostly deals with Speedup, Parallel computing, Sparse matrix, Scalability and Matrix decomposition. His Speedup research incorporates elements of Kernel ridge regression, Algorithm, Training time and Shared memory. His work in Algorithm addresses subjects such as Locality of reference, which are connected to disciplines such as Computation.

His study in the fields of Degree of parallelism under the domain of Parallel computing overlaps with other disciplines such as Tensor representation. His study in the field of Scalable algorithms is also linked to topics like Scaling. He has researched Matrix decomposition in several fields, including Representation, Theoretical computer science and Computational science.