What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Parallel computing
His main research concerns Parallel computing, Compiler, Distributed computing, Code generation and Nested loop join.
His work deals with themes such as Automatic parallelization and Stencil, which intersect with Parallel computing.
His Compiler study incorporates themes from Parallelism, SIMD and Source code.
The Distributed computing study combines topics in areas such as Scheduling, Queueing theory, Scalability and Supercomputer.
His Code generation research is multidisciplinary, incorporating perspectives in Tensor contraction, Computation and Computer engineering.
P. Sadayappan has researched Nested loop join in several fields, including Deadlock, Loop and Affine transformation.
His most cited work include:
- A practical automatic polyhedral parallelizer and locality optimizer (678 citations)
- Gaining insights into multicore cache partitioning: Bridging the gap between simulation and real systems (319 citations)
- Scalable work stealing (209 citations)
What are the main themes of his work throughout his whole career to date?
Parallel computing, Distributed computing, Computation, Algorithm and Compiler are his primary areas of study.
His work carried out in the field of Parallel computing brings together such families of science as Sparse matrix and Code generation.
His Code generation course of study focuses on CUDA and General-purpose computing on graphics processing units.
His research integrates issues of Scalability and Job scheduler, Scheduling, Rate-monotonic scheduling, Fair-share scheduling in his study of Distributed computing.
His work in Algorithm addresses subjects such as Loop fusion, which are connected to disciplines such as Loop tiling.
His biological study spans a wide range of topics, including Stencil and SIMD.
He most often published in these fields:
- Parallel computing (58.25%)
- Distributed computing (19.10%)
- Computation (14.86%)
What were the highlights of his more recent work (between 2013-2021)?
- Parallel computing (58.25%)
- Computation (14.86%)
- Compiler (13.68%)
In recent papers he was focusing on the following fields of study:
P. Sadayappan mainly investigates Parallel computing, Computation, Compiler, Algorithm and Sparse matrix.
His Parallel computing study integrates concerns from other disciplines, such as Kernel and Stencil.
P. Sadayappan combines subjects such as Computational complexity theory, Tensor representation, Representation and Affine transformation with his study of Computation.
His Compiler research includes elements of Class, Redundancy and Error detection and correction.
His Algorithm research incorporates elements of Memory hierarchy, Permutation, Sequence, Bottleneck and Speedup.
As a part of the same scientific family, P. Sadayappan mostly works in the field of Sparse matrix, focusing on Multiplication and, on occasion, Matrix multiplication.
Between 2013 and 2021, his most popular works were:
- Fast sparse matrix-vector multiplication on GPUs for graph applications (106 citations)
- Hybrid Hexagonal/Classical Tiling for GPUs (78 citations)
- Automatic Selection of Sparse Matrix Representation on GPUs (71 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Parallel computing
P. Sadayappan mainly investigates Parallel computing, Stencil, Computation, Code generation and Compiler.
His research in Parallel computing intersects with topics in Sparse matrix and Kernel.
His Stencil study combines topics in areas such as Statement, Data parallelism, Loop unrolling and Shared memory.
The study incorporates disciplines such as Degree of parallelism, Tensor representation, Fiber and Finite volume method in addition to Computation.
His Code generation research incorporates themes from Scalability, Dataflow, Concurrency and Adaptive mesh refinement.
His Compiler study combines topics from a wide range of disciplines, such as Retiming, Associative property, Pointer, Dependence analysis and Class.
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